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			@article{Simony2016,
			abstract = {Does the default mode network (DMN) reconfigure to encode information about the changing environment? This question has proven difficult, because patterns of functional connectivity reflect a mixture of stimulus-induced neural processes, intrinsic neural processes and non-neuronal noise. Here we introduce inter-subject functional correlation (ISFC), which isolates stimulus-dependent inter-regional correlations between brains exposed to the same stimulus. During fMRI, we had subjects listen to a real-life auditory narrative and to temporally scrambled versions of the narrative. We used ISFC to isolate correlation patterns within the DMN that were locked to the processing of each narrative segment and specific to its meaning within the narrative context. The momentary configurations of DMN ISFC were highly replicable across groups. Moreover, DMN coupling strength predicted memory of narrative segments. Thus, ISFC opens new avenues for linking brain network dynamics to stimulus features and behaviour.},
			author = {Simony, Erez and Honey, Christopher J. and Chen, Janice and Lositsky, Olga and Yeshurun, Yaara and Wiesel, Ami and Hasson, Uri},
			doi = {10.1038/ncomms12141},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/newnew/Simony2016.pdf:pdf},
			isbn = {2041-1723 (Electronic)$\backslash$r2041-1723 (Linking)},
			issn = {2041-1723},
			journal = {Nature Communications},
			number = {May 2015},
			pages = {1--13},
			pmid = {27424918},
			publisher = {Nature Publishing Group},
			title = {{Dynamical reconfiguration of the default mode network during narrative comprehension}},
			url = {http://dx.doi.org/10.1038/ncomms12141},
			volume = {7},
			year = {2016}
			}

			@article{Petrovic2019,
			title={Guided graph spectral embedding: Application to the {C}. elegans connectome},
			author={Petrovic, Miljan and Bolton, Thomas AW and Preti, Maria Giulia and Li{\'e}geois, Rapha{\"e}l and Van De Ville, Dimitri},
			journal={Network Neuroscience},
			volume={3},
			number={3},
			pages={807--826},
			year={2019},
			publisher={MIT Press}
			}

			@article{Ogg2008,
			title={Neural correlates of a clinical continuous performance test},
			author={Ogg, Robert J and Zou, Ping and Allen, Deanna N and Hutchins, Sabrina B and Dutkiewicz, Radek M and Mulhern, Raymond K},
			journal={Magnetic resonance imaging},
			volume={26},
			number={4},
			pages={504--512},
			year={2008},
			publisher={Elsevier}
			}

			@article{Hager1998,
			title={Challenging the anterior attentional system with a continuous performance task: a functional magnetic resonance imaging approach},
			author={H{\"a}ger, Frank and Volz, Hans-Peter and Gaser, Christian and Mentzel, Hans-Joachim and Kaiser, Werner A and Sauer, Heinrich},
			journal={European Archives of Psychiatry and Clinical Neuroscience},
			volume={248},
			number={4},
			pages={161--170},
			year={1998},
			publisher={Springer}
			}

			@article{Tana2010,
			title={Exploring cortical attentional system by using fMRI during a Continuous Perfomance Test},
			author={Tana, Maria Gabriella and Montin, Eros and Cerutti, Sergio and Bianchi, Anna M},
			journal={Computational intelligence and neuroscience},
			volume={2010},
			pages={3},
			year={2010},
			publisher={Hindawi Publishing Corp.}
			}

			@article{Kuhn1955,
			title={The Hungarian method for the assignment problem},
			author={Kuhn, Harold W},
			journal={Naval research logistics quarterly},
			volume={2},
			number={1-2},
			pages={83--97},
			year={1955},
			publisher={Wiley Online Library}
			}

			@article{Braun2015,
			abstract = {The brain is an inherently dynamic system, and executive cognition requires dynamically reconfiguring, highly evolving networks of brain regions that interact in complex and transient communication patterns. However, a precise characterization of these reconfiguration processes during cognitive function in humans remains elusive. Here, we use a series of techniques developed in the field of "dynamic network neuroscience" to investigate the dynamics of functional brain networks in 344 healthy subjects during a working-memory challenge (the "n-back" task). In contrast to a control condition, in which dynamic changes in cortical networks were spread evenly across systems, the effortful working-memory condition was characterized by a reconfiguration of frontoparietal and frontotemporal networks. This reconfiguration, which characterizes "network flexibility," employs transient and heterogeneous connectivity between frontal systems, which we refer to as "integration." Frontal integration predicted neuropsychological measures requiring working memory and executive cognition, suggesting that dynamic network reconfiguration between frontal systems supports those functions. Our results characterize dynamic reconfiguration of large-scale distributed neural circuits during executive cognition in humans and have implications for understanding impaired cognitive function in disorders affecting connectivity, such as schizophrenia or dementia.},
			archivePrefix = {arXiv},
			arxivId = {arXiv:1408.1149},
			author = {Braun, Urs and Sch{\"{a}}fer, Axel and Walter, Henrik and Erk, Susanne and Romanczuk-Seiferth, Nina and Haddad, Leila and Schweiger, Janina I and Grimm, Oliver and Heinz, Andreas and Tost, Heike and Meyer-Lindenberg, Andreas and Bassett, Danielle S},
			doi = {10.1073/pnas.1422487112},
			eprint = {arXiv:1408.1149},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/newnew/Braun2015.pdf:pdf},
			isbn = {0027-8424},
			issn = {1091-6490},
			journal = {Proceedings of the National Academy of Sciences of the United States of America},
			keywords = {Adolescent,Adult,Brain,Brain Mapping,Brain: physiology,Cognition,Executive Function,Female,Frontal Lobe,Frontal Lobe: physiology,Humans,Magnetic Resonance Imaging,Male,Memory, Short-Term,Middle Aged,Nerve Net,Nerve Net: physiology,Young Adult},
			number = {37},
			pages = {11678--83},
			pmid = {26324898},
			title = {{Dynamic reconfiguration of frontal brain networks during executive cognition in humans.}},
			url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4577153{\&}tool=pmcentrez{\&}rendertype=abstract},
			volume = {112},
			year = {2015}
			}

			@article{Leunissen2014,
			Author = {Leunissen, Inge and Coxon, James P and Caeyenberghs, Karen and Michiels, Karla and Sunaert, Stefan and Swinnen, Stephan P},
			Date-Added = {2017-07-05 01:15:35 +0000},
			Date-Modified = {2017-07-05 01:15:50 +0000},
			Journal = {Cortex},
			Month = {Feb.},
			Pages = {67--81},
			Title = {Subcortical volume analysis in traumatic brain injury: the importance of the fronto-striato-thalamic circuit in task switching},
			Volume = {51},
			Year = {2014}}


			@article{Medaglia2017,
			Author = {Medaglia, John D and Huang, Weiyu and Segarra, Santiago and Olm, Christopher and Gee, James and Grossman, Murray and Ribeiro, Alejandro and McMillan, Corey T and Bassett, Danielle S},
			Date-Added = {2017-09-16 17:12:26 +0000},
			Date-Modified = {2017-09-16 17:13:18 +0000},
			Journal = {Neurol.},
			Month = {Aug.},
			Number = {13},
			Pages = {1373--1381},
			Title = {Brain network efficiency is influenced by the pathologic source of corticobasal syndrome},
			Volume = {89},
			Year = {2017}}

			@article{Gaetz2014,
			Author = {Gaetz, William and Bloy, Luke and Wang, DJ and Port, RG and Blaskey, Lisa and Levy, SE and Roberts, Timothy PL},
			Date-Added = {2017-09-16 17:16:02 +0000},
			Date-Modified = {2017-09-16 17:16:20 +0000},
			Journal = {Neuroimage},
			Month = {Feb.},
			Number = {1},
			Pages = {1--9},
			Title = {{GABA} estimation in the brains of children on the autism spectrum: measurement precision and regional cortical variation},
			Volume = {86},
			Year = {2014}}

			@article{Archer2017,
			title={A widespread visually-sensitive functional network relates to symptoms in essential tremor},
			author={Archer, Derek B and Coombes, Stephen A and Chu, Winston T and Chung, Jae Woo and Burciu, Roxana G and Okun, Michael S and Wagle Shukla, Aparna and Vaillancourt, David E},
			journal={Brain},
			volume={141},
			number={2},
			pages={472--485},
			year={2017},
			publisher={Oxford University Press}
			}

			@article{Elble2000,
			title={Diagnostic criteria for essential tremor and differential diagnosis.},
			author={Elble, Rodger J},
			journal={Neurology},
			volume={54},
			number={11 Suppl 4},
			pages={S2--6},
			year={2000}
			}

			@article{Fang2016,
			title={Essential tremor is associated with disruption of functional connectivity in the ventral intermediate nucleusÑmotor cortexÑcerebellum circuit},
			author={Fang, Weidong and Chen, Huiyue and Wang, Hansheng and Zhang, Han and Puneet, Munankami and Liu, Mengqi and Lv, Fajin and Luo, Tianyou and Cheng, Oumei and Wang, Xuefeng and others},
			journal={Human brain mapping},
			volume={37},
			number={1},
			pages={165--178},
			year={2016},
			publisher={Wiley Online Library}
			}

			@article{Hua1998,
			title={Thalamic neuronal activity correlated with essential tremor},
			author={Hua, SE and Lenz, FA and Zirh, TA and Reich, SG and Dougherty, Patrick M},
			journal={Journal of Neurology, Neurosurgery \& Psychiatry},
			volume={64},
			number={2},
			pages={273--276},
			year={1998},
			publisher={BMJ Publishing Group Ltd}
			}

			@incollection{Louis2003,
			title={Essential tremor},
			author={Louis, Elan D and Ottman, Ruth},
			booktitle={Genetics of Movement Disorders},
			pages={353--363},
			year={2003},
			publisher={Elsevier}
			}

			@article{Benabid1996,
			title={Chronic electrical stimulation of the ventralis intermedius nucleus of the thalamus as a treatment of movement disorders},
			author={Benabid, Alim Louis and Pollak, Pierre and Gao, Dongming and Hoffmann, Dominique and Limousin, Patricia and Gay, Emmanuel and Payen, Isabelle and Benazzouz, Abdhelhamid},
			journal={Journal of neurosurgery},
			volume={84},
			number={2},
			pages={203--214},
			year={1996},
			publisher={Journal of Neurosurgery Publishing Group}
			}

			@article{Goldman1992,
			title={The symptomatic and functional outcome of stereotactic thalamotomy for medically intractable essential tremor},
			author={Goldman, Marc S and Ahlskog, J Eric and Kelly, Patrick J},
			journal={Journal of neurosurgery},
			volume={76},
			number={6},
			pages={924--928},
			year={1992},
			publisher={Journal of Neurosurgery Publishing Group}
			}

			@article{Kondziolka2008,
			title={Gamma Knife thalamotomy for essential tremor},
			author={Kondziolka, Douglas and Ong, Joseph G and Lee, John YK and Moore, Robert Y and Flickinger, John C and Lunsford, L Dade},
			journal={Journal of neurosurgery},
			volume={108},
			number={1},
			pages={111--117},
			year={2008},
			publisher={American Association of Neurological Surgeons}
			}

			@article{Elias2016,
			title={A randomized trial of focused ultrasound thalamotomy for essential tremor},
			author={Elias, W Jeffrey and Lipsman, Nir and Ondo, William G and Ghanouni, Pejman and Kim, Young G and Lee, Wonhee and Schwartz, Michael and Hynynen, Kullervo and Lozano, Andres M and Shah, Binit B and others},
			journal={New England Journal of Medicine},
			volume={375},
			number={8},
			pages={730--739},
			year={2016},
			publisher={Mass Medical Soc}
			}

			@article{Tuleasca2018,
			title={Clinical response to VimÕs thalamic stereotactic radiosurgery for essential tremor is associated with distinctive functional connectivity patterns},
			author={Tuleasca, Constantin and Najdenovska, Elena and R{\'e}gis, Jean and Witjas, Tatiana and Girard, Nadine and Champoudry, J{\'e}r{\^o}me and Faouzi, Mohamed and Thiran, Jean-Philippe and Cuadra, Meritxell Bach and Levivier, Marc and others},
			journal={Acta neurochirurgica},
			volume={160},
			number={3},
			pages={611--624},
			year={2018},
			publisher={Springer}
			}

			@article{Cesari1999,
			title={The scaling of human grip configurations.},
			author={Cesari, Paola and Newell, Karl M},
			journal={Journal of Experimental Psychology: Human Perception and Performance},
			volume={25},
			number={4},
			pages={927},
			year={1999},
			publisher={American Psychological Association}
			}

			@article{Keogh2004,
			title={Augmented visual feedback increases finger tremor during postural pointing},
			author={Keogh, J and Morrison, S and Barrett, R},
			journal={Experimental Brain Research},
			volume={159},
			number={4},
			pages={467--477},
			year={2004},
			publisher={Springer}
			}

			@article{Scobey1981,
			title={Displacement thresholds for unidirectional and oscillatory movement},
			author={Scobey, Robert P and Johnson, Chris A},
			journal={Vision Research},
			volume={21},
			number={8},
			pages={1297--1302},
			year={1981},
			publisher={Elsevier}
			}

			@article{Vasilakos1998,
			title={Interaction of tremor and magnification in a motor performance task with visual feedback},
			author={Vasilakos, Konstantinon and Glass, Leon and Beuter, Anne},
			journal={Journal of motor behavior},
			volume={30},
			number={2},
			pages={158--168},
			year={1998},
			publisher={Taylor \& Francis}
			}

			@misc{Witjas2013,
			title={Gammaknife Thamamotomy for Intractable Tremors: Clinical Outcome and Correlations with Neuroimaging Features (P05. 032)},
			author={Witjas, Tatiana and Carron, Romain and Eusebio, Alexandre and Azulay, Jean Philippe and Regis, Jean},
			year={2013},
			publisher={AAN Enterprises}
			}

			@article{Benito2019,
			title={Essential tremor severity and anatomical changes in brain areas controlling movement sequencing},
			author={Benito-Le{\'o}n, Juli{\'a}n and Serrano, Jos{\'e} Ignacio and Louis, Elan D and Holobar, Ales and Romero, Juan P and Povalej-Br{\v{z}}an, Petra and Kranjec, Jernej and Bermejo-Pareja, F{\'e}lix and del Castillo, Mar{\'\i}a Dolores and Posada, Ignacio Javier and others},
			journal={Annals of clinical and translational neurology},
			volume={6},
			number={1},
			pages={83--97},
			year={2019},
			publisher={Wiley Online Library}
			}

			@article{Glickstein2000,
			title={How are visual areas of the brain connected to motor areas for the sensory guidance of movement?},
			author={Glickstein, Mitchell},
			journal={Trends in neurosciences},
			volume={23},
			number={12},
			pages={613--617},
			year={2000},
			publisher={Elsevier}
			}

			@article{Tuleasca2018b,
			title={Visually-sensitive networks in essential tremor: evidence from structural and functional imaging},
			author={Tuleasca, Constantin and R{\'e}gis, Jean and Najdenovska, Elena and Witjas, Tatiana and Girard, Nadine and Thiran, Jean-Philippe and Bach Cuadra, Meritxell and Levivier, Marc and Van De Ville, Dimitri},
			journal={Brain},
			volume={141},
			number={6},
			pages={e47--e47},
			year={2018},
			publisher={Oxford University Press}
			}

			@article{Tuleasca2018c,
			title={Essential Tremor.},
			author={Tuleasca, Constantin and R{\'e}gis, Jean and Levivier, Marc},
			journal={The New England journal of medicine},
			volume={379},
			number={6},
			pages={595},
			year={2018}
			}

			@article{Timmermann2002,
			title={The cerebral oscillatory network of parkinsonian resting tremor},
			author={Timmermann, Lars and Gross, Joachim and Dirks, Martin and Volkmann, Jens and Freund, Hans-Joachim and Schnitzler, Alfons},
			journal={Brain},
			volume={126},
			number={1},
			pages={199--212},
			year={2002},
			publisher={Oxford University Press}
			}

			@article{Broersma2016,
			title={Bilateral cerebellar activation in unilaterally challenged essential tremor},
			author={Broersma, Marja and van der Stouwe, Anna MM and Buijink, Arthur WG and de Jong, Bauke M and Groot, Paul FC and Speelman, Johannes D and Tijssen, Marina AJ and van Rootselaar, Anne-Fleur and Maurits, Natasha M},
			journal={NeuroImage: Clinical},
			volume={11},
			pages={1--9},
			year={2016},
			publisher={Elsevier}
			}

			@article{Joutsa2018,
			title={Identifying therapeutic targets from spontaneous beneficial brain lesions},
			author={Joutsa, Juho and Shih, Ludy C and Horn, Andreas and Reich, Martin M and Wu, Ona and Rost, Natalia S and Fox, Michael D},
			journal={Annals of neurology},
			volume={84},
			number={1},
			pages={153--157},
			year={2018},
			publisher={Wiley Online Library}
			}

			@article{Troster2005,
			title={Quality of life in Essential Tremor Questionnaire (QUEST): development and initial validation},
			author={Tr{\"o}ster, Alexander I and Pahwa, Rajesh and Fields, Julie A and Tanner, Caroline M and Lyons, Kelly E},
			journal={Parkinsonism \& related disorders},
			volume={11},
			number={6},
			pages={367--373},
			year={2005},
			publisher={Elsevier}
			}

			@article{Fahn1993,
			title={Clinical rating scale for tremor},
			author={Fahn, Stanley and Tolosa, Eduardo and Mar{\'\i}n, Concepc{\'\i}on},
			journal={ParkinsonÕs disease and movement disorders},
			volume={2},
			pages={271--280},
			year={1993},
			publisher={Williams \& Wilkins Baltimore}
			}

			@article{Bain1993,
			title={Assessing tremor severity.},
			author={Bain, Peter G and Findley, Leslie J and Atchison, P and Behari, M and Vidailhet, M and Gresty, M and Rothwell, JC and Thompson, PD and Marsden, CD},
			journal={Journal of Neurology, Neurosurgery \& Psychiatry},
			volume={56},
			number={8},
			pages={868--873},
			year={1993},
			publisher={BMJ Publishing Group Ltd}
			}

			@article{Witjas2015,
			title={A prospective single-blind study of Gamma Knife thalamotomy for tremor},
			author={Witjas, Tatiana and Carron, Romain and Krack, Paul and Eusebio, Alexandre and Vaugoyeau, Marianne and Hariz, Marwan and Azulay, Jean Philippe and R{\'e}gis, Jean},
			journal={Neurology},
			volume={85},
			number={18},
			pages={1562--1568},
			year={2015},
			publisher={AAN Enterprises}
			}

			@article{Tuleasca2018d,
			title={Pretherapeutic motor thalamus resting-state functional connectivity with visual areas predicts tremor arrest after thalamotomy for essential tremor: tracing the cerebello-thalamo-visuo-motor network},
			author={Tuleasca, Constantin and Najdenovska, Elena and R{\'e}gis, Jean and Witjas, Tatiana and Girard, Nadine and Champoudry, J{\'e}r{\^o}me and Faouzi, Mohamed and Thiran, Jean-Philippe and Cuadra, Meritxell Bach and Levivier, Marc and others},
			journal={World neurosurgery},
			volume={117},
			pages={e438--e449},
			year={2018},
			publisher={Elsevier}
			}

			@article{Neely2014,
			title={Functional brain activity relates to 0--3 and 3--8 Hz force oscillations in essential tremor},
			author={Neely, Kristina A and Kurani, Ajay S and Shukla, Priyank and Planetta, Peggy J and Wagle Shukla, Aparna and Goldman, Jennifer G and Corcos, Daniel M and Okun, Michael S and Vaillancourt, David E},
			journal={Cerebral Cortex},
			volume={25},
			number={11},
			pages={4191--4202},
			year={2014},
			publisher={Oxford University Press}
			}

			@article{Muthuraman2018,
			title={Cerebello-cortical network fingerprints differ between essential, ParkinsonÕs and mimicked tremors},
			author={Muthuraman, Muthuraman and Raethjen, Jan and Koirala, Nabin and Anwar, Abdul Rauf and Mideksa, Kidist G and Elble, Rodger and Groppa, Sergiu and Deuschl, G{\"u}nter},
			journal={Brain},
			volume={141},
			number={6},
			pages={1770--1781},
			year={2018},
			publisher={Oxford University Press}
			}

			@article{Ivanov2015,
			title={(18F)-FDG PET/CT IN ESSENTIAL TREMOR: PRELIMINARY RESULTS},
			author={Ivanov, Borislav D and Kaprelyan, Ara G and Bochev, Pavel H and Dimitrov, Ivan N and Grudkova, Margarita V and Chaushev, Borislav G and Klissarova, Anelia D and Deleva, Nadezhda S},
			journal={JOURNAL OF IMAB},
			volume={21},
			number={4},
			pages={914--921},
			year={2015},
			publisher={GOSPODIN ILIEV PEYTCHINSKI, ED \& PUB DROUJBA BL 116, PLEVEN, 5806, BULGARIA}
			}

			@article{Sharifi2014,
			title={Neuroimaging essentials in essential tremor: a systematic review},
			author={Sharifi, Sarvi and Nederveen, Aart J and Booij, Jan and van Rootselaar, Anne-Fleur},
			journal={NeuroImage: Clinical},
			volume={5},
			pages={217--231},
			year={2014},
			publisher={Elsevier}
			}

			@article{Tuleasca2017,
			title={Assessing the clinical outcome of Vim radiosurgery with voxel-based morphometry: visual areas are linked with tremor arrest!},
			author={Tuleasca, Constantin and Witjas, Tatiana and Najdenovska, Elena and Verger, Antoine and Girard, Nadine and Champoudry, Jerome and Thiran, Jean-Philippe and Van de Ville, Dimitri and Cuadra, Meritxell Bach and Levivier, Marc and others},
			journal={Acta neurochirurgica},
			volume={159},
			number={11},
			pages={2139--2144},
			year={2017},
			publisher={Springer}
			}

			@article{Tuleasca2018e,
			title={Right Brodmann area 18 predicts tremor arrest after Vim radiosurgery: a voxel-based morphometry study},
			author={Tuleasca, Constantin and Witjas, Tatiana and Van de Ville, Dimitri and Najdenovska, Elena and Verger, Antoine and Girard, Nadine and Champoudry, Jerome and Thiran, Jean-Philippe and Cuadra, Meritxell Bach and Levivier, Marc and others},
			journal={Acta neurochirurgica},
			volume={160},
			number={3},
			pages={603--609},
			year={2018},
			publisher={Springer}
			}

			@article{Gironell2012,
			title={Withdrawal of visual feedback in essential tremor},
			author={Gironell, A and Ribosa-Nogue, R and Pagonabarraga, J},
			journal={Parkinsonism \& related disorders},
			volume={18},
			number={4},
			pages={402--403},
			year={2012},
			publisher={Elsevier}
			}

			@article{Bhalsing2015,
			title={White matter correlates of cognitive impairment in essential tremor},
			author={Bhalsing, KS and Kumar, KJ and Saini, J and Yadav, R and Gupta, AK and Pal, PK},
			journal={American Journal of Neuroradiology},
			volume={36},
			number={3},
			pages={448--453},
			year={2015},
			publisher={Am Soc Neuroradiology}
			}

			@article{Llinas1981,
			title={Electrophysiology of mammalian inferior olivary neurones in vitro. Different types of voltage-dependent ionic conductances.},
			author={Llinas, Rodolfo and Yarom, Y},
			journal={The Journal of physiology},
			volume={315},
			number={1},
			pages={549--567},
			year={1981},
			publisher={Wiley Online Library}
			}

			@article{Lipsman2013,
			title={MR-guided focused ultrasound thalamotomy for essential tremor: a proof-of-concept study},
			author={Lipsman, Nir and Schwartz, Michael L and Huang, Yuexi and Lee, Liesly and Sankar, Tejas and Chapman, Martin and Hynynen, Kullervo and Lozano, Andres M},
			journal={The Lancet Neurology},
			volume={12},
			number={5},
			pages={462--468},
			year={2013},
			publisher={Elsevier}
			}

			@article{Schmidt1994,
			title={The Mattis Dementia Rating Scale: normative data from 1,001 healthy volunteers},
			author={Schmidt, Reinhold and Freidl, W and Fazekas, F and Reinhart, B and Grieshofer, P and Koch, M and Eber, B and Schumacher, M and Polmin, K and Lechner, H},
			journal={Neurology},
			volume={44},
			number={5},
			pages={964--964},
			year={1994},
			publisher={AAN Enterprises}
			}

			@article{Campbell2015,
			title={Gamma knife stereotactic radiosurgical thalamotomy for intractable tremor: a systematic review of the literature},
			author={Campbell, Allison M and Glover, Janis and Chiang, Veronica LS and Gerrard, Jason and James, B Yu},
			journal={Radiotherapy and Oncology},
			volume={114},
			number={3},
			pages={296--301},
			year={2015},
			publisher={Elsevier}
			}

			@article{Archer2018,
			title={Reply: Visually-sensitive networks in essential tremor: evidence from structural and functional imaging},
			author={Archer, Derek B and Coombes, Stephen A and Chu, Winston T and Woo Chung, Jae and Burciu, Roxana G and Okun, Michael S and Wagle Shukla, Aparna and Vaillancourt, David E},
			journal={Brain},
			volume={141},
			number={6},
			pages={e48--e48},
			year={2018},
			publisher={Oxford University Press}
			}

			@article{Baumgartner1997,
			author = {Baumgartner, Richard and Scarth, Gordon and Teichtmeiste, Claudia and Somorjai, Ray and Moser, Ewald},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/clustering{\_}90/Baumgartner{\_}et{\_}al-1997-Journal{\_}of{\_}Magnetic{\_}Resonance{\_}Imaging.pdf:pdf},
			journal = {Journal of magnetic resonance imaging},
			number = {6},
			pages = {1094--1101},
			title = {{Fuzzy Clustering of Gradient-Echo Functional MRI in the Human Visual Cortex. Part I: Reproducibility}},
			volume={7},
			year={1997}
			}

			@article{VDV2016,
			title={Resting-state neuroimaging unravels functional organization in the brain},
			author={Van De Ville, Dimitri and Karahanoglu, Fikret Isik},
			journal={SPIE Newsroom},
			year={2016}
			}

			@article{Du2016,
			title={Interaction among subsystems within default mode network diminished in schizophrenia patients: A dynamic connectivity approach},
			author={Du, Yuhui and Pearlson, Godfrey D and Yu, Qingbao and He, Hao and Lin, Dongdong and Sui, Jing and Wu, Lei and Calhoun, Vince D},
			journal={Schizophrenia research},
			volume={170},
			number={1},
			pages={55--65},
			year={2016},
			publisher={Elsevier}
			}

			@article{Kaiser2015,
			title={Dynamic Resting-State Functional Connectivity in Major Depression},
			author={Kaiser, Roselinde H and Whitfield-Gabrieli, Susan and Dillon, Daniel G and Goer, Franziska and Beltzer, Miranda and Minkel, Jared and Smoski, Moria and Dichter, Gabriel and Pizzagalli, Diego A},
			journal={Neuropsychopharmacology},
			year={2015},
			publisher={Nature Publishing Group}
			}

			@article{Hyvarinen2016,
			title={Orthogonal connectivity factorization: Interpretable decomposition of variability in correlation matrices},
			author={Hyv{\"a}rinen, Aapo and Hirayama, Jun-ichiro and Kiviniemi, Vesa and Kawanabe, Motoaki},
			journal={Neural computation},
			year={2016},
			publisher={MIT Press}
			}

			@article{Rashid2016,
			title={Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity},
			author={Rashid, Barnaly and Arbabshirani, Mohammad R and Damaraju, Eswar and Cetin, Mustafa S and Miller, Robyn and Pearlson, Godfrey D and Calhoun, Vince D},
			journal={NeuroImage},
			volume={134},
			pages={645--657},
			year={2016},
			publisher={Elsevier}
			}

			@article{Guidotti2015,
			title={Visual learning induces changes in resting-state fMRI multivariate pattern of information},
			author={Guidotti, Roberto and Del Gratta, Cosimo and Baldassarre, Antonello and Romani, Gian Luca and Corbetta, Maurizio},
			journal={The Journal of Neuroscience},
			volume={35},
			number={27},
			pages={9786--9798},
			year={2015},
			publisher={Soc Neuroscience}
			}

			@article{Smith2012,
			title={Temporally-independent functional modes of spontaneous brain activity},
			author={Smith, Stephen M and Miller, Karla L and Moeller, Steen and Xu, Junqian and Auerbach, Edward J and Woolrich, Mark W and Beckmann, Christian F and Jenkinson, Mark and Andersson, Jesper and Glasser, Matthew F and others},
			journal={Proceedings of the National Academy of Sciences},
			volume={109},
			number={8},
			pages={3131--3136},
			year={2012},
			publisher={National Acad Sciences}
			}

			@article{Shirer2015,
			title={Optimization of rs-fMRI pre-processing for enhanced signal-noise separation, test-retest reliability, and group discrimination},
			author={Shirer, William R and Jiang, Heidi and Price, Collin M and Ng, Bernard and Greicius, Michael D},
			journal={Neuroimage},
			volume={117},
			pages={67--79},
			year={2015},
			publisher={Elsevier}
			}

			@article{VanDijk2010,
			title={Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization},
			author={Van Dijk, Koene RA and Hedden, Trey and Venkataraman, Archana and Evans, Karleyton C and Lazar, Sara W and Buckner, Randy L},
			journal={Journal of neurophysiology},
			volume={103},
			number={1},
			pages={297--321},
			year={2010},
			publisher={Am Physiological Soc}
			}

			@article{Laumann2015,
			title={Functional system and areal organization of a highly sampled individual human brain},
			author={Laumann, Timothy O and Gordon, Evan M and Adeyemo, Babatunde and Snyder, Abraham Z and Joo, Sung Jun and Chen, Mei-Yen and Gilmore, Adrian W and McDermott, Kathleen B and Nelson, Steven M and Dosenbach, Nico UF and others},
			journal={Neuron},
			volume={87},
			number={3},
			pages={657--670},
			year={2015},
			publisher={Elsevier}
			}

			@article{Sami2014,
			title={The time course of task-specific memory consolidation effects in resting state networks},
			author={Sami, Saber and Robertson, Edwin M and Miall, R Chris},
			journal={The Journal of Neuroscience},
			volume={34},
			number={11},
			pages={3982--3992},
			year={2014},
			publisher={Soc Neuroscience}
			}

			@article{Stoeckel2014,
			abstract = {While reducing the burden of brain disorders remains a top priority of organizations like the World Health Organization and National Institutes of Health, the development of novel, safe and effective treatments for brain disorders has been slow. In this paper, we describe the state of the science for an emerging technology, real time functional magnetic resonance imaging (rtfMRI) neuro feedback, in clinical neurotherapeutics. Were view the scientific potential of rtfMRI and outline research strategies to optimize the development and application of rtfMRIneuro feedback as a next generation therapeutic tool. We propose that rtfMRI can be used to address a broad range of clinical problems by improving our understanding of brainbehavior relationships in order to developmore specific and effective interventions for individuals with brain disorders. We focus on the use of rtfMRI neuro feedback as a clinical neurotherapeutic tool to drive plasticity in brain function, cognition, and behavior. Our overall goal is for rtfMRI to advance personalized assessment and intervention approaches to enhance resilience and reduce morbidity by correcting maladaptive patterns of brain function in those with brain disorders.},
			author = {Stoeckel, L. E. and Garrison, K. A. and Ghosh, S. S. and Wighton, P. and Hanlon, C. A. and Gilman, J. M. and Greer, S. and Turk-Browne, N. B. and Debettencourt, M. T. and Scheinost, D. and Craddock, C. and Thompson, T. and Calderon, V. and Bauer, C. C. and George, M. and Breiter, H. C. and Whitfield-Gabrieli, S. and Gabrieli, J. D. and Laconte, S. M. and Hirshberg, L. and Brewer, J. A. and Hampson, M. and {Van Der Kouwe}, A. and Mackey, S. and Evins, A. E.},
			doi = {10.1016/j.nicl.2014.07.002 Review Article},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/Stoeckel 2014.pdf:pdf},
			isbn = {2213-1582},
			issn = {22131582},
			journal = {NeuroImage: Clinical},
			keywords = {Brain-computer interface,Neurofeedback,Neurotherapeutic,Real time fMRI},
			pages = {245--255},
			pmid = {25161891},
			title = {{Optimizing real time fMRI neurofeedback for therapeutic discovery and development}},
			volume = {5},
			year = {2014}
			}


			@article{Gu2015,
			abstract = {Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behaviour. However, fundamental principles constraining these dynamic network processes have remained elusive. Here we use tools from control and network theories to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure. Our results suggest that densely connected areas, particularly in the default mode system, facilitate the movement of the brain to many easily reachable states. Weakly connected areas, particularly in cognitive control systems, facilitate the movement of the brain to difficult-to-reach states. Areas located on the boundary between network communities, particularly in attentional control systems, facilitate the integration or segregation of diverse cognitive systems. Our results suggest that structural network differences between cognitive circuits dictate their distinct roles in controlling trajectories of brain network function.},
			author = {Gu, Shi and Pasqualetti, Fabio and Cieslak, Matthew and Telesford, Qawi K. and Yu, Alfred B. and Kahn, Ari E. and Medaglia, John D. and Vettel, Jean M. and Miller, Michael B. and Grafton, Scott T. and Bassett, Danielle S.},
			doi = {10.1038/ncomms9414},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/Gu2015.pdf:pdf},
			issn = {2041-1723},
			journal = {Nature Communications},
			pages = {8414},
			pmid = {26423222},
			publisher = {Nature Publishing Group},
			title = {{Controllability of structural brain networks}},
			url = {http://www.nature.com/doifinder/10.1038/ncomms9414},
			volume = {6},
			year = {2015}
			}

			@article{Biswal1995,
			title={Functional connectivity in the motor cortex of resting human brain using echo-planar mri},
			author={Biswal, Bharat and Zerrin Yetkin, F and Haughton, Victor M and Hyde, James S},
			journal={Magnetic resonance in medicine},
			volume={34},
			number={4},
			pages={537--541},
			year={1995},
			publisher={Wiley Online Library}
			}

			@article{Gonzales2012,
			title={Whole-brain, time-locked activation with simple tasks revealed using massive averaging and model-free analysis},
			author={Gonzalez-Castillo, Javier and Saad, Ziad S and Handwerker, Daniel A and Inati, Souheil J and Brenowitz, Noah and Bandettini, Peter A},
			journal={Proceedings of the National Academy of Sciences},
			volume={109},
			number={14},
			pages={5487--5492},
			year={2012},
			publisher={National Acad Sciences}
			}

			@article{Golay1998,
			abstract = {Fuzzy logic clustering algorithms are a new class of processing strategies for functional MRI (fMRI). In this study, the ability of such methods to detect brain activation on application of a stimulus task is demonstrated. An optimization of the selected algorithm with regard to different parameters is proposed. These parameters include (a) those defining the pre-processing procedure of the data set; (b) the definition of the distance between two time courses, considered as p-dimensional vectors, where p is the number of sequential images in the fMRI data set; and (c) the number of clusters to be considered. Based on the assumption that such a clustering algorithm should cluster the pixel time courses according to their similarity and not their proximity (in terms of distance), cross-correlation-based distances are defined. A clear mathematical description of the algorithm is proposed, and its convergence is proven when similarity measures are used instead of conventional Euclidean distance. The differences between the membership function given by the algorithm and the probability are clearly exposed. The algorithm was tested on artificial data sets, as well as on data sets from six volunteers undergoing stimulation of the primary visual cortex. The fMRI maps provided by the fuzzy logic algorithm are compared to those achieved by the well established cross-correlation technique.},
			author = {Golay, Xavier and Kollias, Spyros and Stoll, Gautier and Meier, Dieter and Valavanis, Anton and Boesiger, Peter},
			doi = {10.1002/mrm.1910400211},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/clustering{\_}90/Golay1998clustering.pdf:pdf},
			isbn = {0740-3194 (Print)$\backslash$n0740-3194 (Linking)},
			issn = {07403194},
			journal = {Magnetic Resonance in Medicine},
			keywords = {Fuzzy logic,Image processing,Statistics,fMRI},
			number = {2},
			pages = {249--260},
			pmid = {9702707},
			title = {{A new correlation-based fuzzy logic clustering algorithm for fMRI}},
			volume = {40},
			year = {1998}
			}

			@article{Koush2013,
			title={Connectivity-based neurofeedback: dynamic causal modeling for real-time fMRI},
			author={Koush, Yury and Rosa, Maria Joao and Robineau, Fabien and Heinen, Klaartje and Rieger, Sebastian W and Weiskopf, Nikolaus and Vuilleumier, Patrik and Van De Ville, Dimitri and Scharnowski, Frank},
			journal={Neuroimage},
			volume={81},
			pages={422--430},
			year={2013},
			publisher={Elsevier}
			}

			@article{Neurofeedback2014,
			title={Optimizing real time fMRI neurofeedback for therapeutic discovery and development},
			author={Stoeckel, LE and Garrison, Kathleen A and Ghosh, Satrajit S and Wighton, Paul and Hanlon, Colleen A and Gilman, Jodi M and Greer, Stephanie and Turk-Browne, Nicholas B and Scheinost, Dustin and Craddock, Cameron and others},
			journal={NeuroImage: Clinical},
			volume={5},
			pages={245--255},
			year={2014},
			publisher={Elsevier}
			}

			@article{Moser1999,
			abstract = {Identification and separation of artifacts as well as quantification of expected, i.e., stimulus-correlated, and novel information on brain activity are important for both new insights in neuroscience and future developments in functional magnetic resonance imaging (MRI) of the human brain. Here, we present several examples in which gross head motion or physiologic motion (e.g., pulsation, respiration, large veins) could be identified and separated by using fuzzy cluster analysis of fMRI time series. Furthermore, our experience with single-and multislice fMRI (FLASH and EPI; 1.5 and 3 T) data analysis is summarized and several examples, including long echo time and high-resolution fMRI of the motor cortex, are discussed. Explorative signal processing in fMRI, based on fuzzy clustering, represents a robust and powerful tool for screening large fMRI data sets, extracting expected and novel functional activity of the human brain, and obtaining improved reproducibility of fMRI results. Finally, it may help to improve or develop functional brain models which can then be tested by applying statistical models. r 1999 John Wiley {\&} Sons, Inc},
			author = {Moser, E and Baumgartner, R and Barth, M and Windischberger, C},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/clustering{\_}90/Moser{\_}et{\_}al-1999-International{\_}Journal{\_}of{\_}Imaging{\_}Systems{\_}and{\_}Technology.pdf:pdf},
			journal = {International Journal of Imaging Systems and Technology},
			keywords = {Artifacts,Brain,Cluster Analysis,Clustering,Cortex,FMRI time series,Functional,Functional magnetic resonance imaging,Human,Human brain,Imaging,MRI,Magnetic Resonance Imaging,Magnetic resonance,Model,Motion,Motor Cortex,Respiration,Signal processing,Universities,Veins,analysis,fMRI,fMRI data},
			number = {2},
			pages = {166--176},
			title = {{Explorative Signal Processing in Functional MR Imaging}},
			url = {http://www.scopus.com/scopus/inward/record.url?eid=2-s2.0-0032665305{\&}partner=40{\&}rel=R5.0.4},
			volume = {10},
			year = {1999}
			}

			@article{Goutte1999,
			abstract = {Analysis of fMRI time series is often performed by extracting one or more parameters for the individual voxels. Methods based, e.g., on various statistical tests are then used to yield parameters corresponding to probability of activation or activation strength. However, these methods do not indicate whether sets of voxels are activated in a similar way or in different ways. Typically, delays between two activated signals are not identified. In this article, we use clustering methods to detect similarities in activation between voxels. We employ a novel metric that measures the similarity between the activation stimulus and the fMRI signal. We present two different clustering algorithms and use them to identify regions of similar activations in an fMRI experiment involving a visual stimulus.},
			author = {Goutte, C and Toft, P and Rostrup, E and Nielsen, F and Hansen, L K},
			doi = {10.1006/nimg.1998.0391},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/clustering{\_}90/Goutte1998clustering.pdf:pdf},
			isbn = {1053-8119 (Print)},
			issn = {1053-8119},
			journal = {NeuroImage},
			number = {3},
			pages = {298--310},
			pmid = {10075900},
			title = {{On clustering fMRI time series.}},
			volume = {9},
			year = {1999}
			}

			@article{Baumgartner1998,
			abstract = {The potential of functional MRI (fMRI) data analysis using the paradigm independent fuzzy cluster analysis (FCA) applied in the time domain compared to frequently used paradigm based correlation analysis (CA) was studied with simulated and in vivo fMRI data. The performance of FCA and CA was investigated in a typical contrast-to-noise range for fMRI, ranging from 1.33 to 3.33. Using simulated fMRI data the methods were quantitatively compared in terms of generation of true positives, false positives, and the corresponding signal enhancement. Even without prior knowledge about the stimulation paradigm and the actual hemodynamic response function the performance of FCA was comparable to that of CA where extensive prior knowledge has to be added. Furthermore, discrimination of nonanticipated hemodynamic responses by FCA, such as different levels of activation and delayed response, are demonstrated in simulated and in vivo fMRI data. We demonstrate that using CA one cannot differentiate between these responses at least without extensive prior knowledge, i.e., FCA yields a more particular description of fMRI data. This may be worthwhile for analysis and optimization of data quality in fMRI as well as in the final data analysis.},
			author = {Baumgartner, R and Windischberger, C and Moser, E},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/clustering{\_}90/Baumgartner1998.pdf:pdf},
			isbn = {0730-725X (Print) 0730-725X (Linking)},
			journal = {Magn Reson Imaging},
			keywords = {Magnetic Resonance Imaging/methods,Signal Processing,Adult,Brain/*physiology,Cluster Analysis,Computer Simulation,Computer-Assisted,Data Interpretation,Female,Fuzzy Logic,Humans,Movement,Photic Stimulation,Statistical},
			number = {2},
			pages = {115--125},
			pmid = {9508268},
			title = {{Quantification in functional magnetic resonance imaging: fuzzy clustering vs. correlation analysis}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/9508268$\backslash$nhttp://ac.els-cdn.com/S0730725X97002774/1-s2.0-S0730725X97002774-main.pdf?{\_}tid=64fec17c-2e64-11e3-8d3a-00000aacb35e{\&}acdnat=1381049627{\_}f1a01732f494343f770d66987e4a2788},
			volume = {16},
			year = {1998}
			}

			@article{Bullmore2009,
			title={Complex brain networks: graph theoretical analysis of structural and functional systems},
			author={Bullmore, Ed and Sporns, Olaf},
			journal={Nature Reviews Neuroscience},
			volume={10},
			number={3},
			pages={186--198},
			year={2009},
			publisher={Nature Publishing Group}
			}

			@article{Patel2015,
			title={A wavelet-based estimator of the degrees of freedom in denoised fmri time series for probabilistic testing of functional connectivity and brain graphs},
			author={Patel, Ameera X and Bullmore, Edward T},
			journal={NeuroImage},
			year={2015},
			publisher={Elsevier}
			}

			@article{Caballero2013,
			title={Paradigm free mapping with sparse regression automatically detects single-trial functional magnetic resonance imaging blood oxygenation level dependent responses},
			author={Caballero Gaudes, C{\'e}sar and Petridou, Natalia and Francis, Susan T and Dryden, Ian L and Gowland, Penny A},
			journal={Human Brain Mapping},
			volume={34},
			number={3},
			pages={501--518},
			year={2013},
			publisher={Wiley Online Library}
			}

			@article{Laufs2003,
			title={Electroencephalographic signatures of attentional and cognitive default modes in spontaneous brain activity fluctuations at rest},
			author={Laufs, Helmut and Krakow, K and Sterzer, P and Eger, E and Beyerle, A and Salek-Haddadi, A and Kleinschmidt, A},
			journal={Proceedings of the national academy of sciences},
			volume={100},
			number={19},
			pages={11053--11058},
			year={2003},
			publisher={National Acad Sciences}
			}

			@inproceedings{Wee2013b,
			title={Temporally dynamic resting-state functional connectivity networks for early MCI identification},
			author={Wee, Chong-Yaw and Yang, Sen and Yap, Pew-Thian and Shen, Dinggang},
			booktitle={International Workshop on Machine Learning in Medical Imaging},
			pages={139--146},
			year={2013},
			organization={Springer}
			}

			@article{Holmes2015,
			title={Brain Genomics Superstruct Project initial data release with structural, functional, and behavioral measures},
			author={Holmes, Avram J and Hollinshead, Marisa O and O?Keefe, Timothy M and Petrov, Victor I and Fariello, Gabriele R and Wald, Lawrence L and Fischl, Bruce and Rosen, Bruce R and Mair, Ross W and Roffman, Joshua L and others},
			journal={Scientific data},
			volume={2},
			year={2015},
			publisher={Nature Publishing Group}
			}

			@inproceedings{Price2014,
			title={Multiple-network classification of childhood autism using functional connectivity dynamics},
			author={Price, True and Wee, Chong-Yaw and Gao, Wei and Shen, Dinggang},
			booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
			pages={177--184},
			year={2014},
			organization={Springer}
			}

			@article{Nooner2012,
			title={The NKI-Rockland sample: a model for accelerating the pace of discovery science in psychiatry},
			author={Nooner, Kate Brody and Colcombe, Stanley and Tobe, Russell and Mennes, Maarten and Benedict, Melissa and Moreno, Alexis and Panek, Laura and Brown, Shaquanna and Zavitz, Stephen and Li, Qingyang and others},
			journal={Frontiers in neuroscience},
			volume={6},
			pages={152},
			year={2012},
			publisher={Frontiers}
			}

			@article{Smith2013,
			title={Resting-state fMRI in the human connectome project},
			author={Smith, Stephen M and Beckmann, Christian F and Andersson, Jesper and Auerbach, Edward J and Bijsterbosch, Janine and Douaud, Gwena{\"e}lle and Duff, Eugene and Feinberg, David A and Griffanti, Ludovica and Harms, Michael P and others},
			journal={Neuroimage},
			volume={80},
			pages={144--168},
			year={2013},
			publisher={Elsevier}
			}

			@article{Newman2004,
			title={Finding community structure in very large networks},
			author={Clauset, Aaron and Newman, Mark EJ and Moore, Cristopher},
			journal={Physical review E},
			volume={70},
			number={6},
			pages={066111},
			year={2004},
			publisher={APS}
			}

			@article{Buzsaki2004,
			abstract = {Clocks tick, bridges and skyscrapers vibrate, neuronal networks oscillate. Are neuronal oscillations an inevitable by-product, similar to bridge vibrations, or an essential part of the brain's design? Mammalian cortical neurons form behavior-dependent oscillating networks of various sizes, which span five orders of magnitude in frequency. These oscillations are phylogenetically preserved, suggesting that they are functionally relevant. Recent findings indicate that network oscillations bias input selection, temporally link neurons into assemblies, and facilitate synaptic plasticity, mechanisms that cooperatively support temporal representation and long-term consolidation of information.},
			author = {Buzsaki, Gyorgy and Draguhn, Andreas},
			doi = {10.1126/science.1099745},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/new/new/Buzsaki2004.pdf:pdf},
			isbn = {1095-9203 (Electronic)$\backslash$n0036-8075 (Linking)},
			issn = {0036-8075},
			journal = {Science},
			keywords = {Animals,Biological Clocks,Biological Clocks: physiology,Brain,Brain: physiology,Cerebral Cortex,Cerebral Cortex: physiology,Electroencephalography,Humans,Learning,Membrane Potentials,Membrane Potentials: physiology,Nerve Net,Nerve Net: physiology,Neuronal Plasticity,Neurons,Neurons: physiology,Synapses,Synapses: physiology,Synaptic Transmission},
			month = {jun},
			number = {5679},
			pages = {1926--1929},
			pmid = {15218136},
			title = {{Neuronal Oscillations in Cortical Networks}},
			volume = {304},
			year = {2004}
			}

			@article{Jones2012,
			author = {Jones, David T and Vemuri, Prashanthi and Murphy, Matthew C and Gunter, Jeffrey L and Senjem, Matthew L and Machulda, Mary M and Przybelski, Scott A and Gregg, Brian E and Kantarci, Kejal and Knopman, David S and Boeve, Bradley F and Petersen, Ronald C and Jack, Clifford R.},
			doi = {10.1371/journal.pone.0039731},
			editor = {He, Yong},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/Modularity/Jones2012{\_}Plosone.PDF:PDF},
			issn = {1932-6203},
			journal = {PLoS ONE},
			month = {jun},
			number = {6},
			pages = {e39731},
			title = {{Non-Stationarity in the Resting Brain's Modular Architecture}},
			volume = {7},
			year = {2012}
			}

			@article{Ou2015,
			abstract = {Functional connectivity measured from resting state fMRI (R-fMRI) data has been widely used to examine the brain's functional activities and has been recently used to characterize and differentiate brain conditions. However, the dynamical transition patterns of the brain's functional states have been less explored. In this work, we propose a novel computational framework to quantitatively characterize the brain state dynamics via hidden Markov models (HMMs) learned from the observations of temporally dynamic functional connectomics, denoted as functional connectome states. The framework has been applied to the R-fMRI dataset including 44 post-traumatic stress disorder (PTSD) patients and 51 normal control (NC) subjects. Experimental results show that both PTSD and NC brains were undergoing remarkable changes in resting state and mainly transiting amongst a few brain states. Interestingly, further prediction with the best-matched HMM demonstrates that PTSD would enter into, but could not disengage from, a negative mood state. Importantly, 84 {\%} of PTSD patients and 86 {\%} of NC subjects are successfully classified via multiple HMMs using majority voting.},
			author = {Ou, Jinli and Xie, Li and Jin, Changfeng and Li, Xiang and Zhu, Dajiang and Jiang, Rongxin and Chen, Yaowu and Zhang, Jing and Li, Lingjiang and Liu, Tianming},
			doi = {10.1007/s10548-014-0406-2},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/Ou2015.pdf:pdf},
			isbn = {0896-0267},
			issn = {0896-0267},
			journal = {Brain Topography},
			keywords = {fmri {\'{a}} temporal dynamics,{\'{a}} functional},
			month = {sep},
			number = {5},
			pages = {666--679},
			pmid = {25331991},
			publisher = {Springer US},
			title = {{Characterizing and Differentiating Brain State Dynamics via Hidden Markov Models}},
			volume = {28},
			year = {2015}
			}

			@article{Chang2016,
			title={Tracking brain arousal fluctuations with fMRI},
			author={Chang, Catie and Leopold, David A and Sch{\"o}lvinck, Marieke Louise and Mandelkow, Hendrik and Picchioni, Dante and Liu, Xiao and Frank, Q Ye and Turchi, Janita N and Duyn, Jeff H},
			journal={Proceedings of the National Academy of Sciences},
			pages={201520613},
			year={2016},
			publisher={National Acad Sciences}
			}

			@article{Staresina2013,
			title={Awake reactivation predicts memory in humans},
			author={Staresina, Bernhard P and Alink, Arjen and Kriegeskorte, Nikolaus and Henson, Richard N},
			journal={Proceedings of the National Academy of Sciences},
			volume={110},
			number={52},
			pages={21159--21164},
			year={2013},
			publisher={National Acad Sciences}
			}

			@article{Tambini2013,
			title={Persistence of hippocampal multivoxel patterns into postencoding rest is related to memory},
			author={Tambini, Arielle and Davachi, Lila},
			journal={Proceedings of the National Academy of Sciences},
			volume={110},
			number={48},
			pages={19591--19596},
			year={2013},
			publisher={National Acad Sciences}
			}

			@article{Qin2015,
			author = {Qin, Jian and Chen, Shan-Guang and Hu, Dewen and Zeng, Ling-Li and Fan, Yi-Ming and Chen, Xiao-Ping and Shen, Hui},
			doi = {10.3389/fnhum.2015.00418},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/new/Quin2015.pdf:pdf},
			isbn = {1662-5161},
			issn = {1662-5161},
			journal = {Frontiers in Human Neuroscience},
			keywords = {development,fMRI,functional connectivity,low-frequency fluctuation,multivariate pattern analysis},
			month = {jul},
			number = {July},
			pages = {Article 418},
			pmid = {26236224},
			title = {{Predicting individual brain maturity using dynamic functional connectivity}},
			volume = {9},
			year = {2015}
			}

			@article{Liao2014,
			abstract = {Epilepsy is characterized by recurrent and temporary brain dysfunction due to discharges of interconnected groups of neurons. The brain of epilepsy patients has a dynamic bifurcation that switches between epileptic and normal states. The dysfunctional state involves large-scale brain networks. It is very important to understand the network mechanisms of seizure initiation, maintenance, and termination in epilepsy. Absence epilepsy provides a unique model for neuroimaging investigation on dynamic evolutions of brain networks over seizure repertoire. By using a dynamic functional connectivity and graph theoretical analyses to study absence seizures (AS), we aimed to obtain transition of network properties that account for seizure onset and offset. We measured resting-state functional magnetic resonance imaging and simultaneous electroencephalography (EEG) from children with AS. We used simultaneous EEG to define the preictal, ictal and postictal intervals of seizures. We measured dynamic connectivity maps of the thalamus network and the default mode network (DMN), as well as functional connectome topologies, during the three different seizure intervals. The analysis of dynamic changes of anti-correlation between the thalamus and the DMN is consistent with an inhibitory effect of seizures on the default mode of brain function, which gradually fades out after seizure onset. Also, we observed complex transitions of functional network topology, implicating adaptive reconfiguration of functional brain networks. In conclusion, our work revealed novel insights into modifications in large-scale functional connectome during AS, which may contribute to a better understanding the network mechanisms of state bifurcations in epileptogenesis.},
			author = {Liao, Wei and Zhang, Zhiqiang and Mantini, Dante and Xu, Qiang and Ji, Gong-Jun and Zhang, Han and Wang, Jue and Wang, Zhengge and Chen, Guanghui and Tian, Lei and Jiao, Qing and Zang, Yu-Feng and Lu, Guangming},
			doi = {10.1007/s00429-013-0619-2},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/new/Liao2014b.pdf:pdf},
			isbn = {1863-2653},
			issn = {1863-2653},
			journal = {Brain Structure and Function},
			keywords = {Absence seizure,Brain connectome,Dynamic,Functional connectivity,fMRI},
			month = {nov},
			number = {6},
			pages = {2001--2015},
			pmid = {23913255},
			title = {{Dynamical intrinsic functional architecture of the brain during absence seizures}},
			volume = {219},
			year = {2014}
			}

			@article{Beckmann2005,
			abstract = {Inferring resting-state connectivity patterns from functional magnetic resonance imaging (FMRI) data is a challenging task for any analytical technique. In this paper we review a probabilistic independent component analysis (PICA) approach, optimised for the analysis of FMRI data (Beckmann and Smith, 2004), and discuss the role which this exploratory technique can take in scientific investigations into the structure of these effects. We apply PICA to FMRI data acquired at rest in order to characterise the spatiotemporal structure of such data, and demonstrate that this is an effective and robust tool for the identification of low-frequency resting-state patterns from data acquired at various different spatial and temporal resolutions. We show that these networks exhibit high spatial consistency across subjects and closely resemble discrete cortical functional networks such as visual cortical areas or sensory motor cortex.},
			author = {Beckmann, Christian F and DeLuca, M. and Devlin, Joseph T and Smith, Stephen M},
			doi = {10.1098/rstb.2005.1634},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/new/new/Beckmann2005.pdf:pdf},
			isbn = {0962-8436},
			issn = {0962-8436},
			journal = {Philosophical Transactions of the Royal Society B: Biological Sciences},
			keywords = {brain connectivity,functional magnetic resonance imaging,independent com-,resting-state fluctuations},
			month = {may},
			number = {1457},
			pages = {1001--1013},
			pmid = {16087444},
			title = {{Investigations into resting-state connectivity using independent component analysis}},
			url = {http://rstb.royalsocietypublishing.org/cgi/doi/10.1098/rstb.2005.1634},
			volume = {360},
			year = {2005}
			}

			@article{Hansen2015,
			abstract = {Functional connectivity (FC) sheds light on the interactions between different brain regions. Besides basic research, it is clinically relevant for applications in Alzheimer's disease, schizophrenia, presurgical planning, epilepsy, and traumatic brain injury. Simulations of whole-brain mean-field computational models with realistic connectivity determined by tractography studies enable us to reproduce with accuracy aspects of average FC in the resting state. Most computational studies, however, did not address the prominent non-stationarity in resting state FC, which may result in large intra- and inter-subject variability and thus preclude an accurate individual predictability. Here we show that this non-stationarity reveals a rich structure, characterized by rapid transitions switching between a few discrete FC states. We also show that computational models optimized to fit time-averaged FC do not reproduce these spontaneous state transitions and, thus, are not qualitatively superior to simplified linear stochastic models, which account for the effects of structure alone. We then demonstrate that a slight enhancement of the non-linearity of the network nodes is sufficient to broaden the repertoire of possible network behaviors, leading to modes of fluctuations, reminiscent of some of the most frequently observed Resting State Networks. Because of the noise-driven exploration of this repertoire, the dynamics of FC qualitatively change now and display non-stationary switching similar to empirical resting state recordings (Functional Connectivity Dynamics (FCD)). Thus FCD bear promise to serve as a better biomarker of resting state neural activity and of its pathologic alterations.},
			author = {Hansen, Enrique C.A. and Battaglia, Demian and Spiegler, Andreas and Deco, Gustavo and Jirsa, Viktor K.},
			doi = {10.1016/j.neuroimage.2014.11.001},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/Hansen2015.pdf:pdf},
			isbn = {1053-8119},
			issn = {10538119},
			journal = {NeuroImage},
			keywords = {Brain dynamics,Functional connectivity,Functional connectivity dynamics,Resting state,Structural connectivity,Whole brain computational model},
			month = {jan},
			pages = {525--535},
			pmid = {25462790},
			publisher = {The Authors},
			title = {{Functional connectivity dynamics: Modeling the switching behavior of the resting state}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2014.11.001 http://linkinghub.elsevier.com/retrieve/pii/S1053811914009033},
			volume = {105},
			year = {2015}
			}

			@article{Deco2011,
			abstract = {A broad body of experimental work has demonstrated that apparently spontaneous brain activity is not random. At the level of large-scale neural systems, as measured with functional MRI (fMRI), this ongoing activity reflects the organization of a series of highly coherent functional networks. These so-called resting-state networks (RSNs) closely relate to the underlying anatomical connectivity but cannot be understood in those terms alone. Here we review three large-scale neural system models of primate neocortex that emphasize the key contributions of local dynamics, signal transmission delays and noise to the emerging RSNs. We propose that the formation and dissolution of resting-state patterns reflects the exploration of possible functional network configurations around a stable anatomical skeleton.},
			author = {Deco, Gustavo and Jirsa, Viktor K and McIntosh, Anthony R},
			doi = {10.1038/nrn2961},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC reviews/Deco2011REVIEW.pdf:pdf},
			isbn = {1471-0048 (Electronic)$\backslash$r1471-003X (Linking)},
			issn = {1471-003X},
			journal = {Nature Reviews Neuroscience},
			month = {jan},
			number = {1},
			pages = {43--56},
			pmid = {21170073},
			publisher = {Nature Publishing Group},
			title = {{Emerging concepts for the dynamical organization of resting-state activity in the brain}},
			url = {http://dx.doi.org/10.1038/nrn2961 http://www.nature.com/doifinder/10.1038/nrn2961},
			volume = {12},
			year = {2011}
			}

			@article{Yu2015,
			abstract = {Graph theory-based analysis has been widely employed in brain imaging studies, and altered topological properties of brain connectivity have emerged as important features of mental diseases such as schizophrenia. However, most previous studies have focused on graph metrics of stationary brain graphs, ignoring that brain connectivity exhibits fluctuations over time. Here we develop a new framework for accessing dynamic graph properties of time-varying functional brain connectivity in resting-state fMRI data and apply it to healthy controls (HCs) and patients with schizophrenia (SZs). Specifically, nodes of brain graphs are defined by intrinsic connectivity networks (ICNs) identified by group independent component analysis (ICA). Dynamic graph metrics of the time-varying brain connectivity estimated by the correlation of sliding time-windowed ICA time courses of ICNs are calculated. First- and second-level connectivity states are detected based on the correlation of nodal connectivity strength between time-varying brain graphs. Our results indicate that SZs show decreased variance in the dynamic graph metrics. Consistent with prior stationary functional brain connectivity works, graph measures of identified first-level connectivity states show lower values in SZs. In addition, more first-level connectivity states are disassociated with the second-level connectivity state which resembles the stationary connectivity pattern computed by the entire scan. Collectively, the findings provide new evidence about altered dynamic brain graphs in schizophrenia, which may underscore the abnormal brain performance in this mental illness.},
			author = {Yu, Qingbao and Erhardt, Erik B. and Sui, Jing and Du, Yuhui and He, Hao and Hjelm, Devon and Cetin, Mustafa S. and Rachakonda, Srinivas and Miller, Robyn L. and Pearlson, Godfrey and Calhoun, Vince D.},
			doi = {10.1016/j.neuroimage.2014.12.020},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC tools applied to neurodegenerative disorders/Schizophrenia/Yu2015{\_}Schizophrenia.pdf:pdf},
			isbn = {1053-8119},
			issn = {10538119},
			journal = {NeuroImage},
			keywords = {Brain graph,Dynamic,ICA,R-fMRI,Schizophrenia,Time varying},
			month = {feb},
			pages = {345--355},
			pmid = {25514514},
			publisher = {Elsevier Inc.},
			title = {{Assessing dynamic brain graphs of time-varying connectivity in fMRI data: Application to healthy controls and patients with schizophrenia}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2014.12.020 http://linkinghub.elsevier.com/retrieve/pii/S105381191401012X},
			volume = {107},
			year = {2015}
			}

			@article{Zuo2010,
			title={The oscillating brain: complex and reliable},
			author={Zuo, Xi-Nian and Di Martino, Adriana and Kelly, Clare and Shehzad, Zarrar E and Gee, Dylan G and Klein, Donald F and Castellanos, F Xavier and Biswal, Bharat B and Milham, Michael P},
			journal={Neuroimage},
			volume={49},
			number={2},
			pages={1432--1445},
			year={2010},
			publisher={Elsevier}
			}

			@inproceedings{Hyvarinen2014,
			abstract = {In many multivariate time series, the correlation structure is$\backslash$nnon-stationary, i.e. it changes over time. Analysis of such$\backslash$nnon-stationarities is of particular interest in neuroimaging, in which$\backslash$nit leads to investigation of the dynamics of connectivity. A fundamental$\backslash$napproach for such analysis is to estimate connectivities separately in$\backslash$nshort time windows, and use existing machine learning methods, such as$\backslash$nprincipal component analysis (PCA), to summarize or visualize the$\backslash$nchanges in connectivity. Here, we use the PCA approach by Leonardi et al$\backslash$nas the starting point and present two new methods. Our goal is to$\backslash$nsimplify interpretation of the results by finding components in the$\backslash$noriginal data space instead of the connectivity space. First, we show$\backslash$nhow to further analyse the principal components of connectivity matrices$\backslash$nby a tailor-made two-rank matrix approximation, in which the$\backslash$neigenvectors of the conventional low-rank approximation are transformed.$\backslash$nSecond, we show how to incorporate the two-rank constraint in the$\backslash$nestimation of PCA itself to improve the results. We further provide an$\backslash$ninterpretation of the method in terms of estimation of a probabilistic$\backslash$ngenerative model related to blind source separation methods and ICA.$\backslash$nPreliminary experiments on magnetoencephalographic data reveal possibly$\backslash$nmeaningful non-stationarity patterns in power-to-power coherence of$\backslash$nrhythmic sources (i.e. correlation of amplitudes).},
			author = {Hyvarinen, Aapo and Hirayama, Junichiro and Kawanabe, Motoaki},
			booktitle = {2014 International Workshop on Pattern Recognition in Neuroimaging},
			doi = {10.1109/PRNI.2014.6858524},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/HyvarinenPRNI14{\_}DCF.pdf:pdf},
			isbn = {978-1-4799-4149-0},
			issn = {2330-9989},
			month = {jun},
			pages = {1--4},
			publisher = {IEEE},
			title = {{Dynamic connectivity factorization: Interpretable decompositions of non-stationarity}},
			url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6858524},
			year = {2014}
			}

			@article{Gonzalez-Castillo2014,
			abstract = {Resting state functional MRI (rsfMRI) connectivity patterns are not temporally stable, but fluctuate in time at scales shorter than most common rest scan durations (5-10 min). Consequently, connectivity patterns for two different portions of the same scan can differ drastically. To better characterize this temporal variability and understand how it is spatially distributed across the brain, we scanned subjects continuously for 60 min, at a temporal resolution of 1 s, while they rested inside the scanner. We then computed connectivity matrices between functionally-defined regions of interest for non-overlapping 1 min windows, and classified connections according to their strength, polarity, and variability. We found that the most stable connections correspond primarily to inter-hemispheric connections between left/right homologous ROIs. However, only 32{\%} of all within-network connections were classified as most stable. This shows that resting state networks have some long-term stability, but confirms the flexible configuration of these networks, particularly those related to higher order cognitive functions. The most variable connections correspond primarily to inter-hemispheric, across-network connections between non-homologous regions in occipital and frontal cortex. Finally we found a series of connections with negative average correlation, but further analyses revealed that such average negative correlations may be related to the removal of CSF signals during pre-processing. Using the same dataset, we also evaluated how similarity of within-subject whole-brain connectivity matrices changes as a function of window duration (used here as a proxy for scan duration). Our results suggest scanning for a minimum of 10 min to optimize within-subject reproducibility of connectivity patterns across the entire brain, rather than a few predefined networks.},
			author = {Gonzalez-Castillo, Javier and Handwerker, Daniel A. and Robinson, Meghan E. and Hoy, Colin Weir and Buchanan, Laura C. and Saad, Ziad S. and Bandettini, Peter A.},
			doi = {10.3389/fnins.2014.00138},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/new/Gonzalez-Castillos2014.pdf:pdf},
			isbn = {1662-4548 (Print)$\backslash$r1662-453X (Linking)},
			issn = {1662-453X},
			journal = {Frontiers in Neuroscience},
			keywords = {Connectivity dynamics,FMRI,Rest,Sliding window analysis,Stability},
			month = {jun},
			number = {8 JUN},
			pages = {1--19},
			pmid = {24999315},
			title = {{The spatial structure of resting state connectivity stability on the scale of minutes}},
			url = {http://journal.frontiersin.org/article/10.3389/fnins.2014.00138/abstract},
			volume = {8},
			year = {2014}
			}

			@article{Sojoudi2016,
			abstract = {Spontaneous fluctuations of blood-oxygenation level-dependent functional magnetic resonance imaging (BOLD fMRI) signals are highly synchronous between brain regions that serve similar functions. This provides a means to investigate functional networks; however, most analysis techniques assume functional connections are constant over time. This may be problematic in the case of neurological disease, where functional connections may be highly variable. Recently, several methods have been proposed to determine moment-to-moment changes in the strength of functional connections over an imaging session (so called dynamic connectivity). Here a novel analysis framework based on a hierarchical observation modeling approach was proposed, to permit statistical inference of the presence of dynamic connectivity. A two-level linear model composed of overlapping sliding windows of fMRI signals, incorporating the fact that overlapping windows are not independent was described. To test this approach, datasets were synthesized whereby functional connectivity was either constant (significant or insignificant) or modulated by an external input. The method successfully determines the statistical significance of a functional connection in phase with the modulation, and it exhibits greater sensitivity and specificity in detecting regions with variable connectivity, when compared with sliding-window correlation analysis. For real data, this technique possesses greater reproducibility and provides a more discriminative estimate of dynamic connectivity than sliding-window correlation analysis. Hum Brain Mapp, 2016. {\textcopyright} 2016 Wiley Periodicals, Inc.},
			author = {Sojoudi, Alireza and Goodyear, Bradley G},
			doi = {10.1002/hbm.23329},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/new/Sojoudi{\_}et{\_}al-2016-Human{\_}Brain{\_}Mapping.pdf:pdf},
			issn = {10659471},
			journal = {Human Brain Mapping},
			keywords = {Bayesian inference,coefficient of determination,dynamic functional connectivity,fMRI,functional connection variability,hierarchical observation modeling},
			month = {jul},
			number = {July},
			pmid = {27464464},
			title = {{Statistical inference of dynamic resting-state functional connectivity using hierarchical observation modeling}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/27464464 http://doi.wiley.com/10.1002/hbm.23329},
			volume = {00},
			year = {2016}
			}

			@article{Betzel2015,
			author = {Mi{\v{s}}i{\'{c}}, Bratislav and Betzel, Richard F. and Nematzadeh, Azadeh and Go{\~{n}}i, Joaquin and Griffa, Alessandra and Hagmann, Patric and Flammini, Alessandro and Ahn, Yong-Yeol and Sporns, Olaf},
			doi = {10.1016/j.neuron.2015.05.035},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/Misic{\_}models.pdf:pdf},
			issn = {08966273},
			journal = {Neuron},
			month = {jun},
			number = {6},
			pages = {1518--1529},
			title = {{Cooperative and Competitive Spreading Dynamics on the Human Connectome}},
			url = {http://linkinghub.elsevier.com/retrieve/pii/S0896627315004742},
			volume = {86},
			year = {2015}
			}

			@article{Shen2014,
			abstract = {Increasingly more neuroimaging studies have shown that the complex symptoms of schizophrenia are linked to disrupted neural circuits and dysconnectivity of intrinsic connectivity networks. Previous studies have assumed temporal stationarity of resting-state functional connectivity, whereas temporal dynamics have rarely been explored. Here, we utilized resting-state functional MRI with a sliding window approach to measure the amplitude of low-frequency fluctuations (ALFFs) in functional connectivity in 24 patients with schizophrenia and 25 healthy controls. We found that there were significant differences in the ALFFs of specific connections, the majority of which were located between the intrinsic connectivity networks. Importantly, the experimental results of a multivariate pattern analysis of these ALFF measures showed that 81.3{\%} (P{\textless}0.0009) of the participants were correctly classified as either schizophrenic patients or healthy controls by leave-one-out cross-validation. Our results show significant abnormality in the dynamics of internetwork functional connectivity in schizophrenia, which contributes toward the characterization and differentiation of schizophrenic patients, and may be used as a potential biomarker.},
			author = {Shen, Hui and Li, Zhenfeng and Zeng, Ling-Li and Yuan, Lin and Chen, Fanglin and Liu, Zhening and Hu, Dewen},
			doi = {10.1097/WNR.0000000000000267},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/new/Shen2014.pdf:pdf},
			isbn = {0000000000000},
			issn = {0959-4965},
			journal = {NeuroReport},
			keywords = {intrinsic connectivity network,low-frequency fluctuation,mri,resting state,schizophrenia},
			month = {dec},
			number = {17},
			pages = {1344--1349},
			pmid = {25275678},
			title = {{Internetwork dynamic connectivity effectively differentiates schizophrenic patients from healthy controls}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/25275678 http://content.wkhealth.com/linkback/openurl?sid=WKPTLP:landingpage{\&}an=00001756-201412030-00003},
			volume = {25},
			year = {2014}
			}

			@article{Tagliazucchi2012,
			author = {Tagliazucchi, Enzo and von Wegner, Frederic and Morzelewski, Astrid and Brodbeck, Verena and Laufs, Helmut},
			doi = {10.3389/fnhum.2012.00339},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC correlates - EEG or behavior/Tagliazucchi2012{\_}EEGcorr.pdf:pdf},
			issn = {1662-5161},
			journal = {Frontiers in Human Neuroscience},
			keywords = {EEG-fMRI,brain networks,brain oscillations,dynamic connectivity,eeg-fmri,resting state},
			number = {339},
			pages = {1--22},
			title = {{Dynamic BOLD functional connectivity in humans and its electrophysiological correlates}},
			url = {http://journal.frontiersin.org/article/10.3389/fnhum.2012.00339/abstract},
			volume = {6},
			year = {2012}
			}

			@article{Laufs2014,
			abstract = {Temporal lobe epilepsy (TLE) can be conceptualized as a network disease. The network can be characterized by inter-regional functional connectivity, i.e., blood oxygen level-dependent (BOLD) signal correlations between any two regions. However, functional connectivity is not constant over time, thus computing correlation at a given time and then at some later time could give different results (non-stationarity). We hypothesized (1) that non-stationarities can be induced by epilepsy (e.g., interictal epileptic activity) increasing local signal variance and that (2) these transient events contribute to fluctuations in connectivity leading to pathological functioning, i.e., TLE semiology. We analyzed fMRI data from 27 patients with TLE and 22 healthy controls focusing on EEG-confirmed wake epochs only to protect against sleep-induced connectivity changes. Testing hypothesis (1), we identified brain regions where the BOLD signal variance was significantly greater in TLE than in controls: the temporal pole - including the hippocampus. Taking the latter as the seed region and testing hypothesis (2), we calculated the time-varying inter-regional correlation values (dynamic functional connectivity) to other brain regions and found greater connectivity variance in the TLE than the control group mainly in the precuneus, the supplementary and sensorimotor, and the frontal cortices. We conclude that the highest BOLD signal variance in the hippocampi is highly suggestive of a specific epilepsy-related effect. The altered connectivity dynamics in TLE patients might help to explain the hallmark semiological features of dyscognitive seizures including impaired consciousness (precuneus, frontal cortex), sensory disturbance, and motor automatisms (sensorimotor cortices, supplementary motor cortex). Accounting for the non-stationarity and state-dependence of functional connectivity are a prerequisite in the search for potential connectivity-derived biomarkers in TLE.},
			author = {Laufs, Helmut and Rodionov, Roman and Thornton, Rachel and Duncan, John Sydney and Lemieux, Louis and Tagliazucchi, Enzo},
			doi = {10.3389/fneur.2014.00175},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC tools applied to neurodegenerative disorders/Epilepsy/Laufs2014{\_}Epilepsy.pdf:pdf},
			isbn = {1664-2295 (Electronic)$\backslash$r1664-2295 (Linking)},
			issn = {16642295},
			journal = {Frontiers in Neurology},
			keywords = {Biomarker,EEG-fMRI,Functional connectivity,Interictal epileptiform discharges,Non-stationarity,Seizure,Semiology,Temporal lobe epilepsy},
			number = {SEP},
			pages = {1--13},
			pmid = {25309503},
			title = {{Altered fMRI connectivity dynamics in temporal lobe epilepsy might explain seizure semiology}},
			volume = {5},
			year = {2014}
			}

			@article{Sadaghiani2015,
			title={Ongoing dynamics in large-scale functional connectivity predict perception},
			author={Sadaghiani, Sepideh and Poline, Jean-Baptiste and Kleinschmidt, Andreas and D?Esposito, Mark},
			journal={Proceedings of the National Academy of Sciences},
			volume={112},
			number={27},
			pages={8463--8468},
			year={2015},
			publisher={National Acad Sciences}
			}

			@article{Kucyi2016,
			title={Dynamic Brain Network Correlates of Spontaneous Fluctuations in Attention},
			author={Kucyi, Aaron and Hove, Michael J and Esterman, Michael and Hutchison, R Matthew and Valera, Eve M},
			journal={Cerebral Cortex},
			pages={bhw029},
			year={2016},
			publisher={Oxford Univ Press}
			}

			@article{Wang2016,
			title={Spontaneous eyelid closures link vigilance fluctuation with fMRI dynamic connectivity states},
			author={Wang, Chenhao and Ong, Ju Lynn and Patanaik, Amiya and Zhou, Juan and Chee, Michael WL},
			journal={Proceedings of the National Academy of Sciences},
			pages={201523980},
			year={2016},
			publisher={National Acad Sciences}
			}

			@article{Jia2014,
			abstract = {While many previous studies assumed the functional connectivity (FC) between brain regions to be stationary, recent studies have demonstrated that FC dynamically varies across time. However, two challenges have limited the interpretability of dynamic FC information. First, a principled framework for selecting the temporal extent of the window used to examine the dynamics is lacking and this has resulted in ad-hoc selections of window lengths and subsequent divergent results. Second, it is unclear whether there is any behavioral relevance to the dynamics of the functional connectome in addition to that obtained from conventional static FC (SFC). In this work, we address these challenges by first proposing a principled framework for selecting the extent of the temporal windows in a dynamic and data-driven fashion based on statistical tests of the stationarity of time series. Further, we propose a method involving three levels of clustering-across space, time, and subjects-which allow for group-level inferences of the dynamics. Next, using a large resting-state functional magnetic resonance imaging and behavioral dataset from the Human Connectome Project, we demonstrate that metrics derived from dynamic FC can explain more than twice the variance in 75 behaviors across different domains (alertness, cognition, emotion, and personality traits) as compared with SFC in healthy individuals. Further, we found that individuals with brain networks exhibiting greater dynamics performed more favorably in behavioral tasks. This indicates that the ease with which brain regions engage or disengage may provide potential biomarkers for disorders involving altered neural circuitry.},
			author = {Jia, Hao and Hu, Xiaoping and Deshpande, Gopikrishna},
			doi = {10.1089/brain.2014.0300},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/Jia2014.pdf:pdf},
			issn = {2158-0014},
			journal = {Brain Connectivity},
			keywords = {Adult,Behavior,Behavior: physiology,Brain,Brain: blood supply,Cluster Analysis,Computer-Assisted,Connectome,Female,Humans,Image Processing,Individuality,Magnetic Resonance Imaging,Male,Neural Pathways,Neural Pathways: blood supply,Neural Pathways: physiology,Nonlinear Dynamics,Oxygen,Oxygen: blood,Young Adult,adaptive clustering,brain network,dynamic functional connectivity,human behavioral,resting-state fMRI},
			month = {nov},
			number = {9},
			pages = {741--759},
			pmid = {25163490},
			title = {{Behavioral Relevance of the Dynamics of the Functional Brain Connectome}},
			url = {http://online.liebertpub.com/doi/full/10.1089/brain.2014.0300$\backslash$nhttp://online.liebertpub.com/doi/pdf/10.1089/brain.2014.0300$\backslash$nhttp://online.liebertpub.com/doi/abs/10.1089/brain.2014.0300 http://online.liebertpub.com/doi/abs/10.1089/brain.2014.0300},
			volume = {4},
			year = {2014}
			}

			@article{Roberts2016,
			author = {Roberts, Reece P and Hach, Sylvia and Tippett, Lynette J and Addis, Donna Rose},
			doi = {10.1016/j.neuroimage.2016.04.028},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC-related methodological enquiries/NeuroImage2016Roberts{\_}TheSimpsonParadox.pdf:pdf},
			issn = {10538119},
			journal = {NeuroImage},
			keywords = {Episodic SIMULATION,Functional connectivity,Seed PLS,Simpson's paradox,functional connectivity},
			month = {jul},
			pages = {1--15},
			publisher = {Elsevier Inc.},
			title = {{The Simpson's paradox and fMRI: Similarities and differences between functional connectivity measures derived from within-subject and across-subject correlations}},
			url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811916300623},
			volume = {135},
			year = {2016}
			}

			@article{Tagliazucchi2014,
			author = {Tagliazucchi, Enzo and Laufs, Helmut},
			doi = {10.1016/j.neuron.2014.03.020},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC correlates - EEG or behavior/Tagliazucchi2014{\_}Sleep.pdf:pdf},
			issn = {08966273},
			journal = {Neuron},
			month = {may},
			number = {3},
			pages = {695--708},
			publisher = {Elsevier Inc.},
			title = {{Decoding Wakefulness Levels from Typical fMRI Resting-State Data Reveals Reliable Drifts between Wakefulness and Sleep}},
			url = {http://dx.doi.org/10.1016/j.neuron.2014.03.020 http://linkinghub.elsevier.com/retrieve/pii/S0896627314002505},
			volume = {82},
			year = {2014}
			}

			@article{Tagliazucchi2014b,
			author = {Tagliazucchi, Enzo and Carhart-Harris, Robin and Leech, Robert and Nutt, David and Chialvo, Dante R},
			doi = {10.1002/hbm.22562},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC correlates - EEG or behavior/Tagliazucchi2014{\_}PsychedelicExperience.pdf:pdf},
			issn = {10659471},
			journal = {Human Brain Mapping},
			keywords = {fmri,functional connectivity,psilocybin,psychedelic state,resting state},
			month = {nov},
			number = {11},
			pages = {5442--5456},
			title = {{Enhanced repertoire of brain dynamical states during the psychedelic experience}},
			url = {http://doi.wiley.com/10.1002/hbm.22562},
			volume = {35},
			year = {2014}
			}

			@article{Fox2005,
			abstract = {During performance of attention-demanding cognitive tasks, certain regions of the brain routinely increase activity, whereas others routinely decrease activity. In this study, we investigate the extent to which this task-related dichotomy is represented intrinsically in the resting human brain through examination of spontaneous fluctuations in the functional MRI blood oxygen level-dependent signal. We identify two diametrically opposed, widely distributed brain networks on the basis of both spontaneous correlations within each network and anticorrelations between networks. One network consists of regions routinely exhibiting task-related activations and the other of regions routinely exhibiting task-related deactivations. This intrinsic organization, featuring the presence of anticorrelated networks in the absence of overt task performance, provides a critical context in which to understand brain function. We suggest that both task-driven neuronal responses and behavior are reflections of this dynamic, ongoing, functional organization of the brain.},
			archivePrefix = {arXiv},
			arxivId = {NIHMS150003},
			author = {Fox, Michael D and Snyder, Abraham Z and Vincent, Justin L and Corbetta, Maurizio and {Van Essen}, David C and Raichle, Marcus E},
			doi = {10.1073/pnas.0504136102},
			eprint = {NIHMS150003},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/new/new/PNAS-2005-Fox-9673-8.pdf:pdf},
			isbn = {0027-8424 (Print)},
			issn = {0027-8424},
			journal = {Proceedings of the National Academy of Sciences of the United States of America},
			keywords = {Attention,Attention: physiology,Brain,Brain Mapping,Brain: metabolism,Brain: physiology,Electroencephalography,Humans,Magnetic Resonance Imaging,Models, Neurological,Oxygen,Oxygen: blood,Task Performance and Analysis},
			number = {27},
			pages = {9673--8},
			pmid = {15976020},
			title = {{The human brain is intrinsically organized into dynamic, anticorrelated functional networks.}},
			url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1157105{\&}tool=pmcentrez{\&}rendertype=abstract},
			volume = {102},
			year = {2005}
			}

			@article{Xu2015,
			author = {Xu, Yuting and Lindquist, Martin A},
			doi = {10.3389/fnins.2015.00285},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/DCR{\_}changepoints/Xu{\&}Lindquist2015{\_}changepoints.pdf:pdf},
			issn = {1662-453X},
			journal = {Frontiers in Neuroscience},
			keywords = {change point detection,dynamic functional connec,dynamic functional connectivity,functional connectivity,resting state fmri},
			month = {sep},
			number = {September},
			title = {{Dynamic connectivity detection: an algorithm for determining functional connectivity change points in fMRI data}},
			url = {http://journal.frontiersin.org/Article/10.3389/fnins.2015.00285/abstract},
			volume = {9},
			year = {2015}
			}

			@article{Deng2016,
			author = {Deng, Lifu and Sun, Junfeng and Cheng, Lin and Tong, Shanbao},
			doi = {10.1038/srep26976},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/new/Deng2016.pdf:pdf},
			isbn = {2045-2322 (Electronic) 2045-2322 (Linking)},
			issn = {2045-2322},
			journal = {Scientific Reports},
			month = {may},
			number = {February},
			pages = {26976},
			pmid = {27231194},
			publisher = {Nature Publishing Group},
			title = {{Characterizing dynamic local functional connectivity in the human brain}},
			url = {http://www.nature.com/articles/srep26976},
			volume = {6},
			year = {2016}
			}

			@article{Rashid2014,
			abstract = {Schizophrenia (SZ) and bipolar disorder (BP) share significant overlap in clinical symptoms, brain characteristics, and risk genes, and both are associated with dysconnectivity among large-scale brain networks. Resting state functional magnetic resonance imaging (rsfMRI) data facilitates studying macroscopic connectivity among distant brain regions. Standard approaches to identifying such connectivity include seed-based correlation and data-driven clustering methods such as independent component analysis (ICA) but typically focus on average connectivity. In this study, we utilize ICA on rsfMRI data to obtain intrinsic connectivity networks (ICNs) in cohorts of healthy controls (HCs) and age matched SZ and BP patients. Subsequently, we investigated difference in functional network connectivity, defined as pairwise correlations among the timecourses of ICNs, between HCs and patients. We quantified differences in both static (average) and dynamic (windowed) connectivity during the entire scan duration. Disease-specific differences were identified in connectivity within different dynamic states. Notably, results suggest that patients make fewer transitions to some states (states 1, 2, and 4) compared to HCs, with most such differences confined to a single state. SZ patients showed more differences from healthy subjects than did bipolars, including both hyper and hypo connectivity in one common connectivity state (dynamic state 3). Also group differences between SZ and bipolar patients were identified in patterns (states) of connectivity involving the frontal (dynamic state 1) and frontal-parietal regions (dynamic state 3). Our results provide new information about these illnesses and strongly suggest that state-based analyses are critical to avoid averaging together important factors that can help distinguish these clinical groups.},
			author = {Rashid, Barnaly and Damaraju, Eswar and Pearlson, Godfrey D and Calhoun, Vince D},
			doi = {10.3389/fnhum.2014.00897},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC tools applied to neurodegenerative disorders/Schizophrenia/Rashid2014{\_}SchizophreniaAndBIPO.pdf:pdf},
			isbn = {1662-5161},
			issn = {1662-5161},
			journal = {Frontiers in Human Neuroscience},
			keywords = {bipolar disorder,dynamic functional connectivity,independent component analysis,intrinsic connect,intrinsic connectivity networks,schizophrenia},
			month = {nov},
			number = {November},
			pages = {897},
			pmid = {25426048},
			title = {{Dynamic connectivity states estimated from resting fMRI Identify differences among Schizophrenia, bipolar disorder, and healthy control subjects}},
			url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4224100{\&}tool=pmcentrez{\&}rendertype=abstract http://journal.frontiersin.org/article/10.3389/fnhum.2014.00897/abstract},
			volume = {8},
			year = {2014}
			}

			@article{Shen2015,
			abstract = {The functional interaction between the brain's two hemispheres includes a unique set of connections between corresponding regions in opposite hemispheres (i.e., homotopic regions) that are consistently reported to be exceptionally strong compared with other interhemispheric (i.e., heterotopic) connections. The strength of homotopic functional connectivity (FC) is thought to be mediated by the regions' shared functional roles and their structural connectivity. Recently, homotopic FC was reported to be stable over time despite the presence of dynamic FC across both intrahemispheric and heterotopic connections. Here we build on this work by considering whether homotopic FC is also stable across conditions. We additionally test the hypothesis that strong and stable homotopic FC is supported by the underlying structural connectivity. Consistent with previous findings, interhemispheric FC between homotopic regions were significantly stronger in both humans and macaques. Across conditions, homotopic FC was most resistant to change and therefore was more stable than heterotopic or intrahemispheric connections. Across time, homotopic FC had significantly greater temporal stability than other types of connections. Temporal stability of homotopic FC was facilitated by direct anatomical projections. Importantly, temporal stability varied with the change in conductive properties of callosal axons along the anterior-posterior axis. Taken together, these findings suggest a notable role for the corpus callosum in maintaining stable functional communication between hemispheres.},
			archivePrefix = {arXiv},
			arxivId = {arXiv:1408.1149},
			author = {Shen, Kelly and Mi{\v{s}}i{\'{c}}, Bratislav and Cipollini, Ben N and Bezgin, Gleb and Buschkuehl, Martin and Hutchison, R Matthew and Jaeggi, Susanne M and Kross, Ethan and Peltier, Scott J and Everling, Stefan and Jonides, John and McIntosh, Anthony R and Berman, Marc G},
			doi = {10.1073/pnas.1503436112},
			eprint = {arXiv:1408.1149},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/new/Shen2015.pdf:pdf},
			isbn = {1215421109},
			issn = {0027-8424},
			journal = {Proceedings of the National Academy of Sciences},
			keywords = {Animals,Brain Mapping,Corpus Callosum,Corpus Callosum: anatomy {\&} histology,Corpus Callosum: physiology,Female,Functional Laterality,Functional Laterality: physiology,Humans,Macaca,Magnetic Resonance Imaging,Male,Myelinated,Myelinated: physiology,Nerve Fibers,Species Specificity,Synaptic Transmission,Synaptic Transmission: physiology},
			month = {may},
			number = {20},
			pages = {6473--6478},
			pmid = {25941372},
			title = {{Stable long-range interhemispheric coordination is supported by direct anatomical projections}},
			url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4443345{\&}tool=pmcentrez{\&}rendertype=abstract http://www.pnas.org/lookup/doi/10.1073/pnas.1503436112},
			volume = {112},
			year = {2015}
			}

			@article{Wilson2015,
			author = {Wilson, Rebecca S. and Mayhew, Stephen D. and Rollings, David T. and Goldstone, Aimee and Przezdzik, Izabela and Arvanitis, Theodoros N. and Bagshaw, Andrew P.},
			doi = {10.1016/j.neuroimage.2015.02.061},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/Wilson2015.pdf:pdf},
			issn = {10538119},
			journal = {NeuroImage},
			month = {may},
			pages = {169--179},
			title = {{Influence of epoch length on measurement of dynamic functional connectivity in wakefulness and behavioural validation in sleep}},
			url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811915001718},
			volume = {112},
			year = {2015}
			}

			@article{Damaraju2014,
			abstract = {Schizophrenia is a psychotic disorder characterized by functional dysconnectivity or abnormal integration between distant brain regions. Recent functional imaging studies have implicated large-scale thalamo-cortical connectivity as being disrupted in patients. However, observed connectivity differences in schizophrenia have been inconsistent between studies, with reports of hyperconnectivity and hypoconnectivity between the same brain regions. Using resting state eyes-closed functional imaging and independent component analysis on a multi-site data that included 151 schizophrenia patients and 163 age- and gender matched healthy controls, we decomposed the functional brain data into 100 components and identified 47 as functionally relevant intrinsic connectivity networks. We subsequently evaluated group differences in functional network connectivity, both in a static sense, computed as the pairwise Pearson correlations between the full network time courses (5.4 minutes in length), and a dynamic sense, computed using sliding windows (44 s in length) and k-means clustering to characterize five discrete functional connectivity states. Static connectivity analysis revealed that compared to healthy controls, patients show significantly stronger connectivity, i.e., hyperconnectivity, between the thalamus and sensory networks (auditory, motor and visual), as well as reduced connectivity (hypoconnectivity) between sensory networks from all modalities. Dynamic analysis suggests that (1), on average, schizophrenia patients spend much less time than healthy controls in states typified by strong, large-scale connectivity, and (2), that abnormal connectivity patterns are more pronounced during these connectivity states. In particular, states exhibiting cortical-subcortical antagonism (anti-correlations) and strong positive connectivity between sensory networks are those that show the group differences of thalamic hyperconnectivity and sensory hypoconnectivity. Group differences are weak or absent during other connectivity states. Dynamic analysis also revealed hypoconnectivity between the putamen and sensory networks during the same states of thalamic hyperconnectivity; notably, this finding cannot be observed in the static connectivity analysis. Finally, in post-hoc analyses we observed that the relationships between sub-cortical low frequency power and connectivity with sensory networks is altered in patients, suggesting different functional interactions between sub-cortical nuclei and sensorimotor cortex during specific connectivity states. While important differences between patients with schizophrenia and healthy controls have been identified, one should interpret the results with caution given the history of medication in patients. Taken together, our results support and expand current knowledge regarding dysconnectivity in schizophrenia, and strongly advocate the use of dynamic analyses to better account for and understand functional connectivity differences. {\textcopyright} 2014 The Authors. Published by Elsevier Inc.},
			author = {Damaraju, E. and Allen, E.A. and Belger, A. and Ford, J.M. and McEwen, S. and Mathalon, D.H. and Mueller, B.A. and Pearlson, G.D. and Potkin, S.G. and Preda, A. and Turner, J.A. and Vaidya, J.G. and van Erp, T.G. and Calhoun, V.D.},
			doi = {10.1016/j.nicl.2014.07.003},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC tools applied to neurodegenerative disorders/Schizophrenia/Damarajuau2015{\_}schizophrenia.pdf:pdf},
			isbn = {2213-1582 (Electronic) 2213-1582 (Linking)},
			issn = {22131582},
			journal = {NeuroImage: Clinical},
			number = {July},
			pages = {298--308},
			pmid = {25161896},
			publisher = {Elsevier B.V.},
			title = {{Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia}},
			url = {http://dx.doi.org/10.1016/j.nicl.2014.07.003 http://linkinghub.elsevier.com/retrieve/pii/S2213158214000953},
			volume = {5},
			year = {2014}
			}

			@article{Karahanoglu2013,
			title={Total activation: fMRI deconvolution through spatio-temporal regularization},
			author={Karahano{\u{g}}lu, Fikret I{\c{s}}{\i}k and Caballero-Gaudes, C{\'e}sar and Lazeyras, Fran{\c{c}}ois and Van De Ville, Dimitri},
			journal={Neuroimage},
			volume={73},
			pages={121--134},
			year={2013},
			publisher={Elsevier}
			}

			@article{Karahanoglu2015,
			abstract = {Dynamics of resting-state functional magnetic resonance imaging (fMRI) provide a new window onto the organizational principles of brain function. Using state-of-the-art signal processing techniques, we extract innovation-driven co-activation patterns (iCAPs) from resting-state fMRI. The iCAPs' maps are spatially overlapping and their sustained-activity signals temporally overlapping. Decomposing resting-state fMRI using iCAPs reveals the rich spatiotemporal structure of functional components that dynamically assemble known resting-state networks. The temporal overlap between iCAPs is substantial; typically, three to four iCAPs occur simultaneously in combinations that are consistent with their behaviour profiles. In contrast to conventional connectivity analysis, which suggests a negative correlation between fluctuations in the default-mode network (DMN) and task-positive networks, we instead find evidence for two DMN-related iCAPs consisting the posterior cingulate cortex that differentially interact with the attention network. These findings demonstrate how the fMRI resting state can be functionally decomposed into spatially and temporally overlapping building blocks using iCAPs.},
			author = {KarahanoÃ¼glu, Fikret Isik and {Van De Ville}, Dimitri},
			doi = {10.1038/ncomms8751},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/CAPS/Isik{\_}NatureCom.pdf:pdf},
			issn = {2041-1723},
			journal = {Nature Communications},
			pages = {7751},
			pmid = {26178017},
			title = {{Transient brain activity disentangles fMRI resting-state dynamics in terms of spatially and temporally overlapping networks}},
			url = {http://www.nature.com/doifinder/10.1038/ncomms8751},
			volume = {6},
			year = {2015}
			}

			@article{Marusak2016,
			author = {Marusak, Hilary A. and Calhoun, Vince D. and Brown, Suzanne and Crespo, Laura M. and Sala-Hamrick, Kelsey and Gotlib, Ian H. and Thomason, Moriah E.},
			doi = {10.1002/hbm.23346},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/new/Marusak{\_}et{\_}al-2016-Human{\_}Brain{\_}Mapping.pdf:pdf},
			issn = {10659471},
			journal = {Human Brain Mapping},
			keywords = {brain development,central executive,default mode network,dent component analysis,fmri,indepen-,intrinsic activity,intrinsic connectivity,resting-state,salience network,state variability},
			month = {aug},
			number = {August},
			pages = {1--12},
			title = {{Dynamic functional connectivity of neurocognitive networks in children}},
			url = {http://doi.wiley.com/10.1002/hbm.23346},
			volume = {00},
			year = {2016}
			}

			@article{Shine2016,
			abstract = {Little is currently known about the coordination of neural activity over longitudinal time-scales and how these changes relate to behavior. To investigate this issue, we used resting-state fMRI data from a single individual to identify the presence of two distinct temporal states that fluctuated over the course of 18 months. We then demonstrated that these temporal states were associated with distinct neural dynamics within individual scanning sessions. In addition, the temporal states were also related to significant alterations in global efficiency, as well as differences in self-reported attention. These patterns were replicated in a separate longitudinal dataset, providing further supportive evidence for the presence of fluctuations in functional network topology over time. Together, our results underscore the importance of longitudinal phenotyping in cognitive neuroscience.},
			archivePrefix = {arXiv},
			arxivId = {1601.05065},
			author = {Shine, James M. and Koyejo, Oluwasanmi and Poldrack, Russell A.},
			doi = {10.1073/pnas.1604898113},
			eprint = {1601.05065},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/new/Shine2016.pdf:pdf},
			issn = {0027-8424},
			journal = {Proceedings of the National Academy of Sciences},
			month = {aug},
			number = {10},
			pages = {201604898},
			pmid = {27528672},
			title = {{Temporal metastates are associated with differential patterns of time-resolved connectivity, network topology, and attention}},
			url = {http://arxiv.org/abs/1601.05065 http://www.pnas.org/lookup/doi/10.1073/pnas.1604898113},
			year = {2016}
			}

			@article{Yaesoubi2015,
			author = {Yaesoubi, Maziar and Miller, Robyn L and Calhoun, Vince D},
			doi = {10.1016/j.neuroimage.2014.11.054},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/Yaesoubi2015{\_}MutuallyTemporallyIndependentConnectivityPatterns.pdf:pdf},
			issn = {10538119},
			journal = {NeuroImage},
			keywords = {Connectivity anti-state,Connectivity patterns,Connectivity state,Functional connectivity,Functional network connectivity,Temporal ICA,functional connectivity,functional network connectivity},
			month = {feb},
			pages = {85--94},
			publisher = {Elsevier Inc.},
			title = {{Mutually temporally independent connectivity patterns: A new framework to study the dynamics of brain connectivity at rest with application to explain group difference based on gender}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2014.11.054 http://linkinghub.elsevier.com/retrieve/pii/S1053811914009847},
			volume = {107},
			year = {2015}
			}

			@article{Voss2009,
			author = {Eavani, Harini and Satterthwaite, Theodore D. and Gur, Raquel E. and Gur, Ruben C. and Davatzikos, Christos},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/Eevani2013{\_}Unsupervised learning of functional network dynamics in resting state fMRI.pdf:pdf},
			isbn = {1011-2499 (Print)$\backslash$r1011-2499 (Linking)},
			journal = {Brain},
			keywords = {functional connectivity,resting state fmri,temporal network dynamics},
			pages = {426--437},
			title = {{Unsupervised Learning of Functional Network Dynamics in Resting State fMRI}},
			volume = {23},
			year = {2013}
			}

			@article{Mucha2010,
			title={Community structure in time-dependent, multiscale, and multiplex networks},
			author={Mucha, Peter J and Richardson, Thomas and Macon, Kevin and Porter, Mason A and Onnela, Jukka-Pekka},
			journal={science},
			volume={328},
			number={5980},
			pages={876--878},
			year={2010},
			publisher={American Association for the Advancement of Science}
			}

			@article{Yaesoubi2015b,
			abstract = {Many approaches for estimating functional connectivity among brain regions or networks in fMRI have been considered in the literature. More recently, studies have shown that connectivity which is usually estimated by calculating correlation between time series or by estimating coherence as a function of frequency has a dynamic nature, during both task and resting conditions. Sliding-window methods have been commonly used to study these dynamic properties although other approaches such as instantaneous phase synchronization have also been used for similar purposes. Some studies have also suggested that spectral analysis can be used to separate the distinct contributions of motion, respiration and neurophysiological activity from the observed correlation. Several recent studies have merged analysis of coherence with study of temporal dynamics of functional connectivity though these have mostly been limited to a few selected brain regions and frequency bands. Here we propose a novel data-driven framework to estimate time-varying patterns of whole-brain functional network connectivity of resting state fMRI combined with the different frequencies and phase lags at which these patterns are observed. We show that this analysis identifies both broad-band cluster centroids that summarize connectivity patterns observed in many frequency bands, as well as clusters consisting only of functional network connectivity (FNC) from a narrow range of frequencies along with associated phase profiles. The value of this approach is demonstrated by its ability to reveal significant group differences in males versus females regarding occupancy rates of cluster that would not be separable without considering the frequencies and phase lags. The method we introduce provides a novel and informative framework for analyzing time-varying and frequency specific connectivity which can be broadly applied to the study of the healthy and diseased human brain.},
			archivePrefix = {arXiv},
			arxivId = {arXiv:1505.06832v1},
			author = {Yaesoubi, Maziar and Allen, Elena A and Miller, Robyn L and Calhoun, Vince D},
			doi = {10.1016/j.neuroimage.2015.07.002},
			eprint = {arXiv:1505.06832v1},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/Yaesoubi2015{\_}Dynamic coherence analysis of resting fMRI data to jointly capture state-based phase, frequency, and time-domain information.pdf:pdf},
			isbn = {1505414040},
			issn = {10538119},
			journal = {NeuroImage},
			keywords = {FC,FNC,Functional connectivity,Functional network connectivity,ICA,Time-frequency analysis,Wavelet transform,Wavelet transform coherence,functional connectivity,functional network connectivity,independent component analysis},
			month = {oct},
			pages = {133--142},
			pmid = {26162552},
			title = {{Dynamic coherence analysis of resting fMRI data to jointly capture state-based phase, frequency, and time-domain information}},
			url = {http://www.sciencedirect.com/science/article/pii/S1053811915006096 http://linkinghub.elsevier.com/retrieve/pii/S1053811915006096},
			volume = {120},
			year = {2015}
			}

			@article{Calhoun2016,
			author = {Calhoun, Vince D and Adali, Tulay},
			doi = {10.1109/MSP.2015.2478915},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC reviews/CalohunReview2016.pdf:pdf},
			issn = {1053-5888},
			journal = {IEEE Signal Processing Magazine},
			month = {may},
			number = {3},
			pages = {52--66},
			title = {{Time-Varying Brain Connectivity in fMRI Data: Whole-brain data-driven approaches for capturing and characterizing dynamic states}},
			url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7461017},
			volume = {33},
			year = {2016}
			}

			@article{Allan2015,
			abstract = {Functional brain signals are frequently decomposed into a relatively small set of large scale, distributed cortical networks that are associated with different cognitive functions. It is generally assumed that the connectivity of these networks is static in time and constant over the whole network, although there is increasing evidence that this view is too simplistic. This work proposes novel techniques to investigate the contribution of spontaneous BOLD events to the temporal dynamics of functional connectivity as assessed by ultra-high field functional magnetic resonance imaging (fMRI). The results show that: 1) spontaneous events in recognised brain networks contribute significantly to network connectivity estimates; 2) these spontaneous events do not necessarily involve whole networks or nodes, but clusters of voxels which act in concert, forming transiently synchronising sub-networks and 3) a task can significantly alter the number of localised spontaneous events that are detected within a single network. These findings support the notion that spontaneous events are the main driver of the large scale networks that are commonly detected by seed-based correlation and ICA. Furthermore, we found that large scale networks are manifestations of smaller, transiently synchronising sub-networks acting dynamically in concert, corresponding to spontaneous events, and which do not necessarily involve all voxels within the network nodes oscillating in unison.},
			author = {Allan, Thomas W. and Francis, Susan T. and Caballero-Gaudes, Cesar and Morris, Peter G. and Liddle, Elizabeth B. and Liddle, Peter F. and Brookes, Matthew J. and Gowland, Penny A.},
			doi = {10.1371/journal.pone.0124577},
			editor = {Stamatakis, Emmanuel Andreas},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/new/Allan2014.PDF:PDF},
			isbn = {1932-6203 (Electronic)$\backslash$r1932-6203 (Linking)},
			issn = {1932-6203},
			journal = {PLOS ONE},
			month = {apr},
			number = {4},
			pages = {e0124577},
			pmid = {25922945},
			title = {{Functional Connectivity in MRI Is Driven by Spontaneous BOLD Events}},
			url = {http://dx.plos.org/10.1371/journal.pone.0124577},
			volume = {10},
			year = {2015}
			}

			@article{Sourty2016,
			abstract = {Exploring time-varying connectivity networks in neurodegenerative disorders is a recent field of research in functional MRI. Dementia with Lewy bodies (DLB) represents 20{\%} of the neurodegenerative forms of dementia. Fluctuations of cognition and vigilance are the key symptoms of DLB. To date, no dynamic functional connectivity (DFC) investigations of this disorder have been performed. In this paper, we refer to the concept of connectivity state as a piecewise stationary configuration of functional connectivity between brain networks. From this concept, we propose a new method for group-level as well as for subject-level studies to compare and characterize connectivity state changes between a set of resting-state networks (RSNs). Dynamic Bayesian networks, statistical and graph theory-based models, enable one to learn dependencies between interacting state-based processes. Product hidden Markov models (PHMM), an instance of dynamic Bayesian networks, are introduced here to capture both statistical and temporal aspects of DFC of a set of resting-state networks. This analysis was based on sliding-window cross-correlations between seven RSNs extracted from a group independent component analysis performed on 20 healthy elderly subjects and 16 patients with DLB. Statistical models of DFC differed in patients compared to healthy subjects for the occipito-parieto-frontal network, the medial occipital network and the right fronto-parietal network. In addition, pairwise comparisons of DFC of resting-state networks revealed a decrease of dependency between these two visual networks (occipito-parieto-frontal and medial occipital networks) and the right fronto-parietal control network. The analysis of DFC state changes thus pointed out networks related to the cognitive functions that are known to be impaired in dementia with Lewy bodies: visual processing as well as attentional and executive functions. Besides this context, product HMM applied to resting-state networks cross-correlations offers a promising new approach to investigate structural and temporal aspects of brain DFC.},
			author = {Sourty, Marion and Thoraval, Laurent and Roquet, Daniel and Armspach, Jean-Paul and Foucher, Jack and Blanc, Fr{\'{e}}d{\'{e}}ric},
			doi = {10.3389/fncom.2016.00060},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/new/Sourty2016.pdf:pdf},
			isbn = {1662-5188 (Electronic) 1662-5188 (Linking)},
			issn = {1662-5188},
			journal = {Frontiers in Computational Neuroscience},
			keywords = {Dementia with Lewy bodies,Dynamic Bayesian Networks,Dynamic Functional Connectivity,Product HMM,Resting-state fMRI},
			month = {jun},
			number = {June},
			pages = {60},
			pmid = {27445778},
			title = {{Identifying Dynamic Functional Connectivity Changes in Dementia with Lewy Bodies Based on Product Hidden Markov Models}},
			url = {http://journal.frontiersin.org/Article/10.3389/fncom.2016.00060/abstract},
			volume = {10},
			year = {2016}
			}

			@article{Nomi2016,
			abstract = {The human insular cortex consists of functionally diverse subdivisions that engage during tasks ranging from interoception to cognitive control. The multiplicity of functions subserved by insular subdivisions calls for a nuanced investigation of their functional connectivity profiles. Four insula subdivisions (dorsal anterior, dAI; ventral, VI; posterior, PI; middle, MI) derived using a data-driven approach were subjected to static- and dynamic functional network connectivity (s-FNC and d-FNC) analyses. Static-FNC analyses replicated previous work demonstrating a cognition-emotion-interoception division of the insula, where the dAI is functionally connected to frontal areas, the VI to limbic areas, and the PI and MI to sensorimotor areas. Dynamic-FNC analyses consisted of k-means clustering of sliding windows to identify variable insula connectivity states. The d-FNC analysis revealed that the most frequently occurring dynamic state mirrored the cognition-emotion-interoception division observed from the s-FNC analysis, with less frequently occurring states showing overlapping and unique subdivision connectivity profiles. In two of the states, all subdivisions exhibited largely overlapping profiles, consisting of subcortical, sensory, motor, and frontal connections. Two other states showed the dAI exhibited a unique connectivity profile compared with other insula subdivisions. Additionally, the dAI exhibited the most variable functional connections across the s-FNC and d-FNC analyses, and was the only subdivision to exhibit dynamic functional connections with regions of the default mode network. These results highlight how a d-FNC approach can capture functional dynamics masked by s-FNC approaches, and reveal dynamic functional connections enabling the functional flexibility of the insula across time. Hum Brain Mapp, 2016. {\textcopyright} 2016 Wiley Periodicals, Inc.},
			author = {Nomi, Jason S. and Farrant, Kristafor and Damaraju, Eswar and Rachakonda, Srinivas and Calhoun, Vince D. and Uddin, Lucina Q.},
			doi = {10.1002/hbm.23135},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/new/Nomi{\_}et{\_}al-2016-Human{\_}Brain{\_}Mapping.pdf:pdf},
			issn = {10659471},
			journal = {Human Brain Mapping},
			keywords = {Default mode network,Dynamic functional network connectivity,Flexibility,Insular cortex,Limbic system,Resting state fMRI,Salience network},
			month = {may},
			number = {5},
			pages = {1770--1787},
			pmid = {26880689},
			title = {{Dynamic functional network connectivity reveals unique and overlapping profiles of insula subdivisions}},
			url = {http://doi.wiley.com/10.1002/hbm.23135},
			volume = {37},
			year = {2016}
			}

			@article{Roy2014,
			abstract = {Spontaneous brain activity, that is, activity in the absence of controlled stimulus input or an explicit active task, is topologically organized in multiple functional networks (FNs) maintaining a high degree of coherence. These "resting state networks" are constrained by the underlying anatomical connectivity between brain areas. They are also influenced by the history of task-related activation. The precise rules that link plastic changes and ongoing dynamics of resting-state functional connectivity (rs-FC) remain unclear. Using the framework of the open source neuroinformatics platform "The Virtual Brain," we identify potential computational mechanisms that alter the dynamical landscape, leading to reconfigurations of FNs. Using a spiking neuron model, we first demonstrate that network activity in the absence of plasticity is characterized by irregular oscillations between low-amplitude asynchronous states and high-amplitude synchronous states. We then demonstrate the capability of spike-timing-dependent plasticity (STDP) combined with intrinsic alpha (8-12 Hz) oscillations to efficiently influence learning. Further, we show how alpha-state-dependent STDP alters the local area dynamics from an irregular to a highly periodic alpha-like state. This is an important finding, as the cortical input from the thalamus is at the rate of alpha. We demonstrate how resulting rhythmic cortical output in this frequency range acts as a neuronal tuner and, hence, leads to synchronization or de-synchronization between brain areas. Finally, we demonstrate that locally restricted structural connectivity changes influence local as well as global dynamics and lead to altered rs-FC.},
			author = {Roy, Dipanjan and Sigala, Rodrigo and Breakspear, Michael and McIntosh, Anthony Randal and Jirsa, Viktor K and Deco, Gustavo and Ritter, Petra},
			doi = {10.1089/brain.2014.0252},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/Roi2014{\_}TVB.pdf:pdf},
			isbn = {10.1089/brain.2014.0252},
			issn = {2158-0014},
			journal = {Brain Connectivity},
			keywords = {Action Potentials,Alpha Rhythm,Cerebral Cortex,Computer Simulation,Humans,Models, Neurological,Nerve Net,Neural Networks (Computer),Neurological,Neuronal Plasticity,Neurons,Rest,Software,physiology},
			month = {dec},
			number = {10},
			pages = {791--811},
			pmid = {25131838},
			title = {{Using the Virtual Brain to Reveal the Role of Oscillations and Plasticity in Shaping Brain's Dynamical Landscape}},
			url = {http://online.liebertpub.com/doi/abs/10.1089/brain.2014.0252},
			volume = {4},
			year = {2014}
			}

			@article{Tagliazucchi2010,
			abstract = {Recent brain functional magnetic resonance imaging (fMRI) studies have shown that chronic back pain (CBP) alters brain dynamics beyond the feeling of pain. In particular, the response of the brain default mode network (DMN) during an attention task was found abnormal. In the present work similar alterations are demonstrated for spontaneous resting patterns of fMRI brain activity over a population of CBP patients (n= 12, 29-67 years old, mean = 51.2). Results show abnormal correlations of three out of four highly connected sites of the DMN with bilateral insular cortex and regions in the middle frontal gyrus (p{\textless} 0.05), in comparison with a control group of healthy subjects (n= 20, 21-60 years old, mean = 38.4). The alterations were confirmed by the calculation of triggered averages, which demonstrated increased coactivation of the DMN and the former regions. These findings demonstrate that CBP disrupts normal activity in the DMN even during the brain resting state, highlighting the impact of enduring pain over brain structure and function. ?? 2010 Elsevier Ireland Ltd.},
			archivePrefix = {arXiv},
			arxivId = {NIHMS150003},
			author = {Tagliazucchi, Enzo and Balenzuela, Pablo and Fraiman, Daniel and Chialvo, Dante R.},
			doi = {10.1016/j.neulet.2010.08.053},
			eprint = {NIHMS150003},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/new/Tagliazucchi2010.pdf:pdf},
			isbn = {1872-7972},
			issn = {03043940},
			journal = {Neuroscience Letters},
			keywords = {Brain,Chronic pain,Default mode network,FMRI,Functional connectivity,Resting state networks},
			month = {nov},
			number = {1},
			pages = {26--31},
			pmid = {20800649},
			title = {{Brain resting state is disrupted in chronic back pain patients}},
			url = {http://linkinghub.elsevier.com/retrieve/pii/S0304394010011110},
			volume = {485},
			year = {2010}
			}

			@article{Keilholz2014,
			abstract = {Dynamic network analysis based on resting-state magnetic resonance imaging (rsMRI) is a fairly new and potentially powerful tool for neuroscience and clinical research. Dynamic analysis can be sensitive to changes that occur in psychiatric or neurologic disorders and can detect variations related to performance on individual trials in healthy subjects. However, the appearance of time-varying connectivity can also arise in signals that share no temporal information, complicating the interpretation of dynamic functional connectivity studies. Researchers have begun utilizing simultaneous imaging and electrophysiological recording to elucidate the neural basis of the networks and their variability in animals and in humans. In this article, we review findings that link changes in electrically recorded brain states to changes in the networks obtained with rsMRI and discuss some of the challenges inherent in interpretation of these studies. The literature suggests that multiple brain processes may contribute to the dynamics observed, and we speculate that it may be possible to separate particular aspects of the rsMRI signal to enhance sensitivity to certain types of neural activity, providing new tools for basic neuroscience and clinical research.},
			author = {Keilholz, Shella Dawn},
			doi = {10.1089/brain.2014.0250},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC reviews/Keilholz2014Review.pdf:pdf},
			isbn = {2158-0022 (Electronic)},
			issn = {2158-0014},
			journal = {Brain Connectivity},
			keywords = {EEG,dynamic analysis,functional connectivity,neural activity,resting-state fMRI},
			month = {dec},
			number = {10},
			pages = {769--779},
			pmid = {24975024},
			title = {{The Neural Basis of Time-Varying Resting-State Functional Connectivity}},
			url = {http://online.liebertpub.com/doi/abs/10.1089/brain.2014.0250 http://www.ncbi.nlm.nih.gov/pubmed/24975024 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4268576 http://online.liebertpub.com/doi/abs/10.1089/brain.2014.0250},
			volume = {4},
			year = {2014}
			}

			@article{Rack-Gomer2012,
			author = {Rack-Gomer, Anna Leigh and Liu, Thomas T},
			doi = {10.1016/j.neuroimage.2011.10.001},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC correlates - EEG or behavior/Rack-Gomer2012{\_}Caffeine.pdf:pdf},
			issn = {10538119},
			journal = {NeuroImage},
			keywords = {correlation,functional mri,nonstationary,resting-state network,spectral,temporal dynamics},
			month = {feb},
			number = {3},
			pages = {2994--3002},
			title = {{Caffeine increases the temporal variability of resting-state BOLD connectivity in the motor cortex}},
			url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811911011591},
			volume = {59},
			year = {2012}
			}

			@article{Wee2016b,
			title={Sparse temporally dynamic resting-state functional connectivity networks for early MCI identification},
			author={Wee, Chong-Yaw and Yang, Sen and Yap, Pew-Thian and Shen, Dinggang and AlzheimerÕs Disease Neuroimaging Initiative and others},
			journal={Brain imaging and behavior},
			volume={10},
			number={2},
			pages={342--356},
			year={2016},
			publisher={Springer}
			}

			@article{Wee2016,
			author = {Wee, Chong-Yaw and Yap, Pew-Thian and Shen, Dinggang},
			doi = {10.1111/cns.12499},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC tools applied to neurodegenerative disorders/Autism/Wee2016{\_}Autism.pdf:pdf},
			issn = {17555930},
			journal = {CNS Neuroscience {\&} Therapeutics},
			keywords = {brain activation patterns,functional connectivity,representation,resting-state,sparse,spatiotemporal dynamics},
			month = {mar},
			number = {3},
			pages = {212--219},
			title = {{Diagnosis of Autism Spectrum Disorders Using Temporally Distinct Resting-State Functional Connectivity Networks}},
			url = {http://doi.wiley.com/10.1111/cns.12499},
			volume = {22},
			year = {2016}
			}

			@article{Thompson2015,
			abstract = {When studying brain connectivity using fMRI, signal intensity time-series are typically correlated with each other in time to compute estimates of the degree of interaction between different brain regions and/or networks. In the static connectivity case, the problem of defining which connections that should be considered significant in the analysis can be addressed in a rather straightforward manner by a statistical thresholding that is based on the magnitude of the correlation coefficients. More recently, interest has come to focus on the dynamical aspects of brain connectivity and the problem of deciding which brain connections that are to be considered relevant in the context of dynamical changes in connectivity provides further options. Since we, in the dynamical case, are interested in changes in connectivity over time, the variance of the correlation time-series becomes a relevant parameter. In this study, we discuss the relationship between the mean and variance of brain connectivity time-series and show that by studying the relation between them, two conceptually different strategies to analyze dynamic functional brain connectivity become available. Using both simulated data as well as resting-state fMRI data from a cohort of 46 subjects, we show that the mean of fMRI connectivity time-series scales negatively with its variance. This finding leads to the suggestion that magnitude- versus variance-based thresholding strategies will induce different results in studies of dynamic functional brain connectivity. Our assertion is exemplified by showing that the magnitude-based strategy is more sensitive to within-RSN connectivity compared to between-RSN connectivity whereas the opposite holds true for a variance-based analysis strategy. The implications of our findings for dynamical functional brain connectivity studies are discussed.},
			author = {Thompson, William H. and Fransson, Peter},
			doi = {10.3389/fnhum.2015.00398},
			issn = {1662-5161},
			journal = {Frontiers in Human Neuroscience},
			keywords = {Signal variance,brain connectivity,dynamics,fMRI,mean,resting-state},
			month = {jul},
			number = {398},
			pages = {1--7},
			pmid = {26236216},
			title = {{The mean-variance relationship reveals two possible strategies for dynamic brain connectivity analysis in fMRI}},
			url = {http://journal.frontiersin.org/article/10.3389/fnhum.2015.00398/abstract http://journal.frontiersin.org/Article/10.3389/fnhum.2015.00398/abstract},
			volume = {9},
			year = {2015}
			}

			@article{Kucyi2013,
			abstract = {Human minds often wander away from their immediate sensory environment. It remains unknown whether such mind wandering is unsystematic or whether it lawfully relates to an individual's tendency to attend to salient stimuli such as pain and their associated brain structure/function. Studies of pain-cognition interactions typically examine explicit manipulation of attention rather than spontaneous mind wandering. Here we sought to better represent natural fluctuations in pain in daily life, so we assessed behavioral and neural aspects of spontaneous disengagement of attention from pain. We found that an individual's tendency to attend to pain related to the disruptive effect of pain on his or her cognitive task performance. Next, we linked behavioral findings to neural networks with strikingly convergent evidence from functional magnetic resonance imaging during pain coupled with thought probes of mind wandering, dynamic resting state activity fluctuations, and diffusion MRI. We found that (i) pain-induced default mode network (DMN) deactivations were attenuated during mind wandering away from pain; (ii) functional connectivity fluctuations between the DMN and periaqueductal gray (PAG) dynamically tracked spontaneous attention away from pain; and (iii) across individuals, stronger PAG-DMN structural connectivity and more dynamic resting state PAG-DMN functional connectivity were associated with the tendency to mind wander away from pain. These data demonstrate that individual tendencies to mind wander away from pain, in the absence of explicit manipulation, are subserved by functional and structural connectivity within and between default mode and antinociceptive descending modulation networks.},
			author = {Kucyi, a and Salomons, T V and Davis, K D},
			doi = {10.1073/pnas.1312902110},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/new/Kucyi2013.pdf:pdf},
			isbn = {0027-8424},
			issn = {0027-8424},
			journal = {Proceedings of the National Academy of Sciences},
			keywords = {ACTIVI,ATTENTION,EXECUTIVE CONTROL,EXPERIENCE,FUNCTIONAL CONNECTIVITY,PERCEPTION,PLACEBO ANALGESIA,SYSTEM,VARIABILITY,attention network,experience sampling,pain modulation,salience network,stimulus-indepe},
			month = {nov},
			number = {46},
			pages = {18692--18697},
			pmid = {24167282},
			title = {{Mind wandering away from pain dynamically engages antinociceptive and default mode brain networks}},
			url = {{\textless}Go to ISI{\textgreater}://CCC:000326830900083$\backslash$nhttp://www4.nationalacademies.org/nas/nashome.nsf http://www.pnas.org/cgi/doi/10.1073/pnas.1312902110},
			volume = {110},
			year = {2013}
			}

			@article{Zalesky2014,
			author = {Zalesky, Andrew and Fornito, Alex and Cocchi, Luca and Gollo, Leonardo L and Breakspear, Michael},
			doi = {10.1073/pnas.1400181111},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/Zalesky2014PNAS.pdf:pdf},
			issn = {0027-8424},
			journal = {Proceedings of the National Academy of Sciences},
			month = {jul},
			number = {28},
			pages = {10341--10346},
			title = {{Time-resolved resting-state brain networks}},
			url = {http://www.pnas.org/cgi/doi/10.1073/pnas.1400181111},
			volume = {111},
			year = {2014}
			}

			@article{Chiang2016,
			abstract = {Brain graphs provide a useful way to computationally model the network structure of the connectome, and this has led to increasing interest in the use of graph theory to quantitate and investigate the topological characteristics of the healthy brain and brain disorders on the network level. The majority of graph theory investigations of functional connectivity have relied on the assumption of temporal stationarity. However, recent evidence increasingly suggests that functional connectivity fluctuates over the length of the scan. In this study, we investigate the stationarity of brain network topology using a Bayesian hidden Markov model (HMM) approach that estimates the dynamic structure of graph theoretical measures of whole-brain functional connectivity. In addition to extracting the stationary distribution and transition probabilities of commonly employed graph theory measures, we propose two estimators of temporal stationarity: the S-index and N-index. These indexes can be used to quantify different aspects of the temporal stationarity of graph theory measures. We apply the method and proposed estimators to resting-state functional MRI data from healthy controls and patients with temporal lobe epilepsy. Our analysis shows that several graph theory measures, including small-world index, global integration measures, and betweenness centrality, may exhibit greater stationarity over time and therefore be more robust. Additionally, we demonstrate that accounting for subject-level differences in the level of temporal stationarity of network topology may increase discriminatory power in discriminating between disease states. Our results confirm and extend findings from other studies regarding the dynamic nature of functional connectivity, and suggest that using statistical models which explicitly account for the dynamic nature of functional connectivity in graph theory analyses may improve the sensitivity of investigations and consistency across investigations.},
			author = {Chiang, Sharon and Cassese, Alberto and Guindani, Michele and Vannucci, Marina and Yeh, Hsiang J. and Haneef, Zulfi and Stern, John M.},
			doi = {10.1016/j.neuroimage.2015.10.070},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/new/Chiang2016.pdf:pdf},
			issn = {10538119},
			journal = {NeuroImage},
			keywords = {Dynamic functional connectivity,Functional magnetic resonance imaging,Graph theory,Hidden Markov Model,Temporal lobe epilepsy},
			month = {jan},
			pages = {601--615},
			pmid = {26518632},
			title = {{Time-dependence of graph theory metrics in functional connectivity analysis}},
			url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811915009878},
			volume = {125},
			year = {2016}
			}

			@article{Tagliazucchi2015,
			author = {Tagliazucchi, Enzo and Laufs, Helmut},
			doi = {10.3389/fneur.2015.00010},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC reviews/TagliazucchiREVIEW2015.pdf:pdf},
			issn = {1664-2295},
			journal = {Frontiers in Neurology},
			keywords = {EEG,EEGâfMRI,dynamic connectivity,eeg,fMRI,fmri,functional connectivity,resting-state,sleep,wakefulness},
			month = {feb},
			number = {10},
			pages = {1--9},
			title = {{Multimodal Imaging of Dynamic Functional Connectivity}},
			url = {http://journal.frontiersin.org/Article/10.3389/fneur.2015.00010/abstract},
			volume = {6},
			year = {2015}
			}

			@article{Zalesky2015,
			author = {Zalesky, Andrew and Breakspear, Michael},
			doi = {10.1016/j.neuroimage.2015.03.047},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/Zalesky2015{\_}NeuroImage.pdf:pdf},
			issn = {10538119},
			journal = {NeuroImage},
			keywords = {Dynamic connectivity,Functional connectivity,Non-stationarity,Sliding window,Time-resolved networks,dynamic connectivity,functional connectivity},
			month = {jul},
			pages = {466--470},
			publisher = {Elsevier Inc.},
			title = {{Towards a statistical test for functional connectivity dynamics}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2015.03.047 http://linkinghub.elsevier.com/retrieve/pii/S1053811915002293},
			volume = {114},
			year = {2015}
			}

			@article{Thompson2014,
			title={Quasi-periodic patterns (QPP): large-scale dynamics in resting state fMRI that correlate with local infraslow electrical activity},
			author={Thompson, Garth John and Pan, Wen-Ju and Magnuson, Matthew Evan and Jaeger, Dieter and Keilholz, Shella Dawn},
			journal={Neuroimage},
			volume={84},
			pages={1018--1031},
			year={2014},
			publisher={Elsevier}
			}

			@article{Allen2014,
			abstract = {Spontaneous fluctuations are a hallmark of recordings of neural signals, emergent over time scales spanning milliseconds and tens of minutes. However, investigations of intrinsic brain organization based on resting-state functional magnetic resonance imaging have largely not taken into account the presence and potential of temporal variability, as most current approaches to examine functional connectivity (FC) implicitly assume that relationships are constant throughout the length of the recording. In this work, we describe an approach to assess whole-brain FC dynamics based on spatial independent component analysis, sliding time window correlation, and k-means clustering of windowed correlation matrices. The method is applied to resting-state data from a large sample (n = 405) of young adults. Our analysis of FC variability highlights particularly flexible connections between regions in lateral parietal and cingulate cortex, and argues against a labeling scheme where such regions are treated as separate and antagonistic entities. Additionally, clustering analysis reveals unanticipated FC states that in part diverge strongly from stationary connectivity patterns and challenge current descriptions of interactions between large-scale networks. Temporal trends in the occurrence of different FC states motivate theories regarding their functional roles and relationships with vigilance/arousal. Overall, we suggest that the study of time-varying aspects of FC can unveil flexibility in the functional coordination between different neural systems, and that the exploitation of these dynamics in further investigations may improve our understanding of behavioral shifts and adaptive processes.},
			author = {Allen, Elena A. and Damaraju, Eswar and Plis, Sergey M. and Erhardt, Erik B. and Eichele, Tom and Calhoun, Vince D.},
			doi = {10.1093/cercor/bhs352},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/Allen2014.pdf:pdf},
			isbn = {1460-2199},
			issn = {1047-3211},
			journal = {Cerebral Cortex},
			keywords = {dynamics,fMRI,functional connectivity,independent component analysis,intrinsic activity,resting state,state variability},
			month = {mar},
			number = {3},
			pages = {663--676},
			pmid = {23146964},
			title = {{Tracking Whole-Brain Connectivity Dynamics in the Resting State}},
			volume = {24},
			year = {2014}
			}

			@article{Thompson2015b,
			author = {Thompson, William Hedley and Fransson, Peter},
			doi = {10.1016/j.neuroimage.2015.07.022},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/Thompson2015{\_}The frequency dimension of fMRI dynamic connectivity- Network connectivity, functional hubs and integration in the resting brain.pdf:pdf},
			issn = {10538119},
			journal = {NeuroImage},
			keywords = {Cortical hubs,Dynamic connectivity,Frequency,Resting-state,fMRI},
			month = {nov},
			pages = {227--242},
			pmid = {26169321},
			publisher = {Elsevier B.V.},
			title = {{The frequency dimension of fMRI dynamic connectivity: Network connectivity, functional hubs and integration in the resting brain}},
			url = {http://www.sciencedirect.com/science/article/pii/S105381191500631X http://linkinghub.elsevier.com/retrieve/pii/S105381191500631X},
			volume = {121},
			year = {2015}
			}

			@article{Thompson2013,
			author = {Thompson, Garth John and Magnuson, Matthew Evan and Merritt, Michael Donelyn and Schwarb, Hillary and Pan, Wen-ju and McKinley, Andrew and Tripp, Lloyd D and Schumacher, Eric H and Keilholz, Shella Dawn},
			doi = {10.1002/hbm.22140},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC correlates - EEG or behavior/Thompson2013HBM{\_}vigilance.pdf:pdf},
			issn = {10659471},
			journal = {Human Brain Mapping},
			keywords = {correlation,default mode,functional connectivity,large scale cerebral networks,performance prediction,psychomotor vigilance task,pvt,resting state,spontaneous fluctuations,task positive,windowed},
			month = {dec},
			number = {12},
			pages = {3280--3298},
			title = {{Short-time windows of correlation between large-scale functional brain networks predict vigilance intraindividually and interindividually}},
			url = {http://doi.wiley.com/10.1002/hbm.22140},
			volume = {34},
			year = {2013}
			}

			@article{Li2014,
			abstract = {Noise and individual differences arise from disturbances in the effective use of resting-state functional magnetic resonance image (fMRI) datasets. In this study, the point process is used to treat fMRI datasets of healthy controls and patients with diabetes; then, functional brain networks of subjects are established using two sets of BOLD signals. The results illustrate that differences between the healthy controls and the patients were more obvious in point process signals than nonpoint process signals. Our results also suggest that there is a higher recognition accuracy of the signals by preprocessing with the point process. These findings may suggest that the point process approach can reduce BOLD signals noise, providing a new method for functional magnetic resonance data preprocessing, and may provide a promising method for early data preprocessing in computer-aided disease diagnostics. ?? 2014 Elsevier B.V.},
			author = {Li, Wei and Li, Yapeng and Hu, Chunhong and Chen, Xi and Dai, Hui},
			doi = {10.1016/j.neucom.2014.05.045},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC tools applied to neurodegenerative disorders/Diabetes/Li2014{\_}PPAOnDiabetes.pdf:pdf},
			issn = {09252312},
			journal = {Neurocomputing},
			keywords = {Functional brain networks,Point process,Resting-state fMRI},
			month = {dec},
			pages = {182--189},
			publisher = {Elsevier},
			title = {{Point process analysis in brain networks of patients with diabetes}},
			url = {http://dx.doi.org/10.1016/j.neucom.2014.05.045 http://linkinghub.elsevier.com/retrieve/pii/S0925231214006948},
			volume = {145},
			year = {2014}
			}

			@article{Liu2013b,
			author = {Liu, Xiao and Chang, Catie and Duyn, Jeff H},
			doi = {10.3389/fnsys.2013.00101},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/CAPS/Liu2013Frontiers.pdf:pdf},
			issn = {1662-5137},
			journal = {Frontiers in Systems Neuroscience},
			keywords = {clustering,dynamic,network dynamics,non-stationary connectivity,resting-state network},
			number = {December},
			pages = {1--11},
			title = {{Decomposition of spontaneous brain activity into distinct fMRI co-activation patterns}},
			url = {http://journal.frontiersin.org/article/10.3389/fnsys.2013.00101/abstract},
			volume = {7},
			year = {2013}
			}

			@article{Gutierrez2019,
			title={Infraslow state fluctuations govern spontaneous fMRI network dynamics},
			author={Gutierrez-Barragan, Daniel and Basson, M Albert and Panzeri, Stefano and Gozzi, Alessandro},
			journal={Current Biology},
			volume={29},
			number={14},
			pages={2295--2306},
			year={2019},
			publisher={Elsevier}
			}

			@article{VanDeVille2019,
			title={Brain Dynamics: Global Pulse and Brain State Switching},
			author={Van De Ville, Dimitri},
			journal={Current Biology},
			volume={29},
			number={14},
			pages={R690--R692},
			year={2019},
			publisher={Elsevier}
			}

			@article{Chang2013,
			author = {Chang, Catie and Liu, Zhongming and Chen, Michael C and Liu, Xiao and Duyn, Jeff H},
			doi = {10.1016/j.neuroimage.2013.01.049},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC correlates - EEG or behavior/Chang2013{\_}EEGcorr.pdf:pdf},
			issn = {10538119},
			journal = {NeuroImage},
			month = {may},
			pages = {227--236},
			publisher = {Elsevier B.V.},
			title = {{EEG correlates of time-varying BOLD functional connectivity}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2013.01.049 http://linkinghub.elsevier.com/retrieve/pii/S1053811913000967},
			volume = {72},
			year = {2013}
			}

			@article{Chang2013b,
			title={Association between heart rate variability and fluctuations in resting-state functional connectivity},
			author={Chang, Catie and Metzger, Coraline D and Glover, Gary H and Duyn, Jeff H and Heinze, Hans-Jochen and Walter, Martin},
			journal={Neuroimage},
			volume={68},
			pages={93--104},
			year={2013},
			publisher={Elsevier}
			}

			@article{Shakil2016,
			abstract = {A promising recent development in the study of brain function is the dynamic analysis of resting-state functional MRI scans, which can enhance understanding of normal cognition and alterations that result from brain disorders. One widely used method of capturing the dynamics of functional connectivity is sliding window correlation (SWC). However, in the absence of a "gold standard" for comparison, evaluating the performance of the SWC in typical resting-state data is challenging. This study uses simulated networks (SNs) with known transitions to examine the effects of parameters such as window length, window offset, window type, noise, filtering, and sampling rate on the SWC performance. The SWC time course was calculated for all node pairs of each SN and then clustered using the k-means algorithm to determine how resulting brain states match known configurations and transitions in the SNs. The outcomes show that the detection of state transitions and durations in the SWC is most strongly influenced by the window length and offset, followed by noise and filtering parameters. The effect of the image sampling rate was relatively insignificant. Tapered windows provide less sensitivity to state transitions than rectangular windows, which could be the result of the sharp transitions in the SNs. Overall, the SWC gave poor estimates of correlation for each brain state. Clustering based on the SWC time course did not reliably reflect the underlying state transitions unless the window length was comparable to the state duration, highlighting the need for new adaptive window analysis techniques.},
			author = {Shakil, Sadia and Lee, Chin-Hui and Keilholz, Shella Dawn},
			doi = {10.1016/j.neuroimage.2016.02.074},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/new/Shakil2016.pdf:pdf},
			issn = {10538119},
			journal = {NeuroImage},
			keywords = {Functional connectivity,K-Means,Network dynamics,Resting-state functional MRI,Sliding window correlation,States},
			month = {jun},
			pages = {111--128},
			pmid = {26952197},
			publisher = {Elsevier Inc.},
			title = {{Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2016.02.074 http://linkinghub.elsevier.com/retrieve/pii/S1053811916001920},
			volume = {133},
			year = {2016}
			}

			@article{Sakoglu2010,
			abstract = {In this paper, we develop a dynamic functional network connectivity (FNC) analysis approach using correlations between windowed time-courses of different brain networks (components) estimated via spatial independent component analysis (sICA). We apply the developed method to fMRI data to evaluate it and to study task-modulation of functional connections.},
			author = {SakogÃ¼lu, {\"{U}}nal and Pearlson, Godfrey D. and Kiehl, Kent A. and Wang, Y. Michelle and Michael, Andrew M. and Calhoun, Vince D.},
			doi = {10.1007/s10334-010-0197-8},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC tools applied to neurodegenerative disorders/Schizophrenia/Sakoglu2010{\_}schizophrenia.pdf:pdf},
			isbn = {0968-5243 (Print)$\backslash$r0968-5243 (Linking)},
			issn = {0968-5243},
			journal = {Magnetic Resonance Materials in Physics, Biology and Medicine},
			keywords = {Auditory oddball task,Brain,Dynamic,Functional magnetic resonance imaging,Functional network connectivity,Independent component analysis,Schizophrenia,fMRI},
			month = {dec},
			number = {5-6},
			pages = {351--366},
			pmid = {20162320},
			title = {{A method for evaluating dynamic functional network connectivity and task-modulation: application to schizophrenia}},
			url = {http://link.springer.com/10.1007/s10334-010-0197-8},
			volume = {23},
			year = {2010}
			}

			@article{Tagliazucchi2012b,
			author = {Tagliazucchi, Enzo and Balenzuela, Pablo and Fraiman, Daniel and Chialvo, Dante R},
			doi = {10.3389/fphys.2012.00015},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/CAPS/Tagliazucchi2012PPA.pdf:pdf},
			issn = {1664-042X},
			journal = {Frontiers in Physiology},
			keywords = {brain dynamics,criticality,fMRI,fmri,point processes},
			number = {February},
			pages = {1--12},
			title = {{Criticality in Large-Scale Brain fMRI Dynamics Unveiled by a Novel Point Process Analysis}},
			url = {http://journal.frontiersin.org/article/10.3389/fphys.2012.00015/abstract},
			volume = {3},
			year = {2012}
			}

			@article{Shen2016,
			abstract = {An increasing number of neuroimaging studies have suggested that the fluctuations of low-frequency resting-state functional connectivity (FC) are not noise but are instead linked to the shift between distinct cognitive states. However, there is very limited knowledge about whether and how the fluctuations of FC at rest are influenced by long-term training and experience. Here, we investigated how the dynamics of resting-state FC are linked to driving behavior by comparing 20 licensed taxi drivers with 20 healthy non-drivers using a sliding window approach. We found that the driving experience could be effectively decoded with 90{\%} (p{\textless} 0.001) accuracy by the amplitude of low-frequency fluctuations in some specific connections, based on a multivariate pattern analysis technique. Interestingly, the majority of these connections fell within a set of distributed regions named "the vigilance network". Moreover, the decreased amplitude of the FC fluctuations within the vigilance network in the drivers was negatively correlated with the number of years that they had driven a taxi. Furthermore, temporally quasi-stable functional connectivity segmentation revealed significant differences between the drivers and non-drivers in the dwell time of specific vigilance-related transient brain states, although the brain's repertoire of functional states was preserved. Overall, these results suggested a significant link between the changes in the time-dependent aspects of resting-state FC within the vigilance network and long-term driving experiences. The results not only improve our understanding of how the brain supports driving behavior but also shed new light on the relationship between the dynamics of functional brain networks and individual behaviors.},
			author = {Shen, Hui and Li, Zhenfeng and Qin, Jian and Liu, Qiang and Wang, Lubin and Zeng, Ling-Li and Li, Hong and Hu, Dewen},
			doi = {10.1016/j.neuroimage.2015.09.010},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/new/Shen2016.pdf:pdf},
			isbn = {1095-9572 (Electronic)$\backslash$r1053-8119 (Linking)},
			issn = {10538119},
			journal = {NeuroImage},
			keywords = {Driving,Dynamic functional connectivity,Functional MRI,Resting-state,Vigilance network},
			month = {jan},
			pages = {367--378},
			pmid = {26363345},
			publisher = {Elsevier Inc.},
			title = {{Changes in functional connectivity dynamics associated with vigilance network in taxi drivers}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2015.09.010 http://linkinghub.elsevier.com/retrieve/pii/S1053811915008071},
			volume = {124},
			year = {2016}
			}

			@article{Hutchison2013,
			abstract = {Characterization of large-scale brain networks using blood-oxygenation-level-dependent functional magnetic resonance imaging is typically based on the assumption of network stationarity across the duration of scan. Recent studies in humans have questioned this assumption by showing that within-network functional connectivity fluctuates on the order of seconds to minutes. Time-varying profiles of resting-state networks (RSNs) may relate to spontaneously shifting, electrophysiological network states and are thus mechanistically of particular importance. However, because these studies acquired data from awake subjects, the fluctuating connectivity could reflect various forms of conscious brain processing such as passive mind wandering, active monitoring, memory formation, or changes in attention and arousal during image acquisition. Here, we characterize RSN dynamics of anesthetized macaques that control for these accounts, and compare them to awake human subjects. We find that functional connectivity among nodes comprising the "oculomotor (OCM) network" strongly fluctuated over time during awake as well as anaesthetized states. For time dependent analysis with short windows ({\textless}60 s), periods of positive functional correlations alternated with prominent anticorrelations that were missed when assessed with longer time windows. Similarly, the analysis identified network nodes that transiently link to the OCM network and did not emerge in average RSN analysis. Furthermore, time-dependent analysis reliably revealed transient states of large-scale synchronization that spanned all seeds. The results illustrate that resting-state functional connectivity is not static and that RSNs can exhibit nonstationary, spontaneous relationships irrespective of conscious, cognitive processing. The findings imply that mechanistically important network information can be missed when using average functional connectivity as the single network measure.},
			author = {Hutchison, R. Matthew and Womelsdorf, Thilo and Gati, Joseph S. and Everling, Stefan and Menon, Ravi S.},
			doi = {10.1002/hbm.22058},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/Hutchison2012.pdf:pdf},
			isbn = {1097-0193 (Electronic)$\backslash$r1065-9471 (Linking)},
			issn = {10659471},
			journal = {Human Brain Mapping},
			keywords = {Dynamics,Fluctuations,Functional MRI (fMRI),Functional connectivity,Macaque,Nonstationary,Resting-state,Spontaneous activity},
			month = {sep},
			number = {9},
			pages = {2154--2177},
			pmid = {22438275},
			title = {{Resting-state networks show dynamic functional connectivity in awake humans and anesthetized macaques}},
			url = {http://doi.wiley.com/10.1002/hbm.22058},
			volume = {34},
			year = {2013}
			}

			@article{Keilholz2013,
			author = {Keilholz, Shella D and Magnuson, Matthew E and Pan, Wen-ju and Willis, Martha and Thompson, Garth J},
			doi = {10.1089/brain.2012.0115},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/Keilholz2012{\_}Rodent.pdf:pdf},
			issn = {2158-0014},
			journal = {Brain Connectivity},
			keywords = {dynamic,functional connectivity,resting state mri,rodent,sliding window correlation somatosen-},
			month = {feb},
			number = {1},
			pages = {31--40},
			title = {{Dynamic Properties of Functional Connectivity in the Rodent}},
			url = {http://online.liebertpub.com/doi/abs/10.1089/brain.2012.0115},
			volume = {3},
			year = {2013}
			}

			@article{Liu2013,
			abstract = {Recent functional magnetic resonance imaging studies have shown that the brain is remarkably active even in the absence of overt behavior, and this activity occurs in spatial patterns that are reproducible across subjects and follow the brain's established functional subdivision. Investigating the distribution of these spatial patterns is an active area of research with the goal of obtaining a better understanding of the neural networks underlying brain function. One intriguing aspect of spontaneous activity is an apparent nonstationarity, or variability of interaction between brain regions. It was recently proposed that spontaneous brain activity may be dominated by brief traces of activity, possibly originating from a neuronal avalanching phenomenon. Such traces may involve different subregions in a network at different times, potentially reflecting functionally relevant relationships that are not captured with conventional data analysis. To investigate this, we examined publicly available functional magnetic resonance imaging data with a dedicated analysis method and found indications that functional networks inferred from conventional correlation analysis may indeed be driven by activity at only a few critical time points. Subsequent analysis of the activity at these critical time points revealed multiple spatial patterns, each distinctly different from the established functional networks. The spatial distribution of these patterns suggests a potential functional relevance.},
			author = {Liu, Xiao and Duyn, Jeff H},
			doi = {10.1073/pnas.1216856110},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/CAPS/LiuCAPS.pdf:pdf},
			isbn = {1091-6490 (Electronic)$\backslash$n0027-8424 (Linking)},
			issn = {1091-6490},
			journal = {Proceedings of the National Academy of Sciences of the United States of America},
			keywords = {Adolescent,Adult,Brain,Brain: physiology,Brain: radiography,Databases, Factual,Female,Humans,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Male,Models, Neurological},
			number = {11},
			pages = {4392--7},
			pmid = {23440216},
			title = {{Time-varying functional network information extracted from brief instances of spontaneous brain activity.}},
			url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3600481{\&}tool=pmcentrez{\&}rendertype=abstract},
			volume = {110},
			year = {2013}
			}

			@article{Shen2015b,
			author = {Shen, K. and Hutchison, R. M. and Bezgin, G. and Everling, S. and McIntosh, A. R.},
			doi = {10.1523/JNEUROSCI.4903-14.2015},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/new/Shen2015b.pdf:pdf},
			isbn = {1529-2401 (Electronic)$\backslash$r0270-6474 (Linking)},
			issn = {0270-6474},
			journal = {Journal of Neuroscience},
			keywords = {functional connectivity,functional mri,large-scale dynamics,rich club organization,structural connectivity},
			month = {apr},
			number = {14},
			pages = {5579--5588},
			pmid = {25855174},
			title = {{Network Structure Shapes Spontaneous Functional Connectivity Dynamics}},
			url = {http://www.jneurosci.org/cgi/doi/10.1523/JNEUROSCI.4903-14.2015},
			volume = {35},
			year = {2015}
			}

			@article{Shine2015,
			author = {Shine, James M and Koyejo, Oluwasanmi and Bell, Peter T and Gorgolewski, Krzysztof J and Gilat, Moran and Poldrack, Russell A},
			doi = {10.1016/j.neuroimage.2015.07.064},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/Shine2015MTD.pdf:pdf},
			issn = {10538119},
			journal = {NeuroImage},
			month = {nov},
			pages = {399--407},
			publisher = {Elsevier Inc.},
			title = {{Estimation of dynamic functional connectivity using Multiplication of Temporal Derivatives}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2015.07.064 http://linkinghub.elsevier.com/retrieve/pii/S1053811915006849},
			volume = {122},
			year = {2015}
			}

			@article{Zalesky2010,
			title={Whole-brain anatomical networks: does the choice of nodes matter?},
			author={Zalesky, Andrew and Fornito, Alex and Harding, Ian H and Cocchi, Luca and Y{\"u}cel, Murat and Pantelis, Christos and Bullmore, Edward T},
			journal={Neuroimage},
			volume={50},
			number={3},
			pages={970--983},
			year={2010},
			publisher={Elsevier}
			}

			@book{Sporns2011,
			Author = {Sporns, Olaf},
			Date-Added = {2017-07-04 00:26:32 +0000},
			Date-Modified = {2017-07-04 00:30:13 +0000},
			Publisher = {MIT Press},
			Title = {Networks of the Brain},
			Year = {2011}}

			@inproceedings{Preti2016,
			title={Eigenmaps of dynamic functional connectivity: Voxel-level dominant patterns through eigenvector centrality},
			author={Preti, Maria Giulia and Van De Ville, Dimitri},
			booktitle={2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)},
			pages={988--991},
			year={2016},
			organization={IEEE}
			}

			@article{Desikan2006,
			Author = {Desikan, Rahul S and S{\'e}gonne, Florent and Fischl, Bruce and Quinn, Brian T and Dickerson, Bradford C and Blacker, Deborah and Buckner, Randy L and Dale, Anders M and Maguire, R Paul and Hyman, Bradley T and others},
			Date-Added = {2017-07-05 00:23:27 +0000},
			Date-Modified = {2017-07-05 00:23:41 +0000},
			Journal = {Neuroimage},
			Month = {Jul.},
			Number = {3},
			Pages = {968--980},
			Title = {An automated labeling system for subdividing the human cerebral cortex on {MRI} scans into gyral based regions of interest},
			Volume = {31},
			Year = {2006}}

			@article{Kennedy1998,
			Author = {Kennedy, DN and Lange, N and Makris, N and Bates, J and Meyer, J and Caviness, VS},
			Date-Added = {2017-07-05 00:22:57 +0000},
			Date-Modified = {2017-07-05 00:23:15 +0000},
			Journal = {Cereb. Cortex},
			Month = {Jun.},
			Number = {4},
			Pages = {372--384},
			Title = {Gyri of the human neocortex: an {MRI}-based analysis of volume and variance.},
			Volume = {8},
			Year = {1998}}


			@article{Hagmann2008,
			Author = {Hagmann, Patric and Cammoun, Leila and Gigandet, Xavier and Meuli, Reto and Honey, Christopher J and Wedeen, Van J and Sporns, Olaf},
			Date-Added = {2017-07-05 00:41:24 +0000},
			Date-Modified = {2017-07-05 00:41:40 +0000},
			Journal = {PLoS Biol.},
			Month = {Jul.},
			Number = {7},
			Pages = {e159},
			Title = {Mapping the structural core of human cerebral cortex},
			Volume = {6},
			Year = {2008}}

			@article{Murphy2009,
			title={The impact of global signal regression on resting state correlations: are anti-correlated networks introduced?},
			author={Murphy, Kevin and Birn, Rasmus M and Handwerker, Daniel A and Jones, Tyler B and Bandettini, Peter A},
			journal={Neuroimage},
			volume={44},
			number={3},
			pages={893--905},
			year={2009},
			publisher={Elsevier}
			}

			@inproceedings{Shakil2015,
			title={On frequency dependencies of sliding window correlation},
			author={Shakil, Sadia and Keilholz, Shella Dawn and Lee, Chin-Hui},
			booktitle={Bioinformatics and Biomedicine (BIBM), 2015 IEEE International Conference on},
			pages={363--368},
			year={2015},
			organization={IEEE}
			}

			@article{Power2015,
			title={Recent progress and outstanding issues in motion correction in resting state fMRI},
			author={Power, Jonathan D and Schlaggar, Bradley L and Petersen, Steven E},
			journal={Neuroimage},
			volume={105},
			pages={536--551},
			year={2015},
			publisher={Elsevier}
			}

			@article{Siegel2016,
			title={Data Quality Influences Observed Links Between Functional Connectivity and Behavior},
			author={Siegel, Joshua S and Mitra, Anish and Laumann, Timothy O and Seitzman, Benjamin A and Raichle, Marcus and Corbetta, Maurizio and Snyder, Abraham Z},
			journal={Cerebral Cortex},
			year={2016},
			publisher={Oxford Univ Press}
			}

			@article{Laumann2016,
			title={On the Stability of BOLD fMRI Correlations},
			author={Laumann, Timothy O and Snyder, Abraham Z and Mitra, Anish and Gordon, Evan M and Gratton, Caterina and Adeyemo, Babatunde and Gilmore, Adrian W and Nelson, Steven M and Berg, Jeff J and Greene, Deanna J and others},
			journal={Cerebral Cortex},
			year={2016},
			publisher={Oxford Univ Press}
			}

			@article{Shine2015b,
			title={Dynamic fluctuations in integration and segregation within the human functional connectome},
			author={Shine, James M and Bell, Peter T and Koyejo, Oluwasanmi and Gorgolewski, Krzysztof J and Moodie, Craig A and Poldrack, Russell A},
			journal={arXiv preprint arXiv:1511.02976},
			year={2015}
			}

			@article{Bassett2011,
			author = {Bassett, Danielle S and Wymbs, Nicholas F and Porter, Mason A and Mucha, Peter J and Carlson, Jean M and Grafton, Scott T},
			doi = {10.1073/pnas.1018985108},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/Modularity/BassettPNAS2011{\_}modularity.pdf:pdf},
			issn = {0027-8424},
			journal = {Proceedings of the National Academy of Sciences},
			month = {may},
			number = {18},
			pages = {7641--7646},
			title = {{Dynamic reconfiguration of human brain networks during learning}},
			url = {http://www.pnas.org/cgi/doi/10.1073/pnas.1018985108},
			volume = {108},
			year = {2011}
			}

			@article{Bassett2015,
			title={Learning-induced autonomy of sensorimotor systems},
			author={Bassett, Danielle S and Yang, Muzhi and Wymbs, Nicholas F and Grafton, Scott T},
			journal={Nature neuroscience},
			volume={18},
			number={5},
			pages={744--751},
			year={2015},
			publisher={Nature Publishing Group}
			}

			@article{Grigg2010,
			title={Task-related effects on the temporal and spatial dynamics of resting-state functional connectivity in the default network},
			author={Grigg, Omer and Grady, Cheryl L},
			journal={PLoS One},
			volume={5},
			number={10},
			pages={e13311},
			year={2010},
			publisher={Public Library of Science}
			}

			@article{Choe2015,
			title={Reproducibility and temporal structure in weekly resting-state fMRI over a period of 3.5 years},
			author={Choe, Ann S and Jones, Craig K and Joel, Suresh E and Muschelli, John and Belegu, Visar and Caffo, Brian S and Lindquist, Martin A and van Zijl, Peter CM and Pekar, James J},
			journal={PloS one},
			volume={10},
			number={10},
			pages={e0140134},
			year={2015},
			publisher={Public Library of Science}
			}

			@article{Poldrack2015,
			title={Long-term neural and physiological phenotyping of a single human},
			author={Poldrack, Russell A and Laumann, Timothy O and Koyejo, Oluwasanmi and Gregory, Brenda and Hover, Ashleigh and Chen, Mei-Yen and Gorgolewski, Krzysztof J and Luci, Jeffrey and Joo, Sung Jun and Boyd, Ryan L and others},
			journal={Nature communications},
			volume={6},
			year={2015},
			publisher={Nature Publishing Group}
			}

			@article{Hutchison2014,
			abstract = {Despite their widespread use, the effect of anesthetic agents on the brain's functional architecture remains poorly understood. This is particularly true of alterations that occur beyond the point of induced unconsciousness. Here, we examined the distributed intrinsic connectivity of macaques across six isoflurane levels using resting-state functional MRI (fMRI) following the loss of consciousness. The results from multiple analysis strategies showed stable functional connectivity (FC) patterns between 1.00{\%} and 1.50{\%} suggesting this as a suitable range for anesthetized nonhuman primate resting-state investigations. Dose-dependent effects were evident at moderate to high dosages showing substantial alteration of the functional topology and a decrease or complete loss of interhemispheric cortical FC strength including that of contralateral homologues. The assessment of dynamic FC patterns revealed that the functional repertoire of brain states is related to anesthesia depth and most strikingly, that the number of state transitions linearly decreases with increased isoflurane dosage. Taken together, the results indicate dose-specific spatial and temporal alterations of FC that occur beyond the typically defined endpoint of consciousness. Future work will be necessary to determine how these findings generalize across anesthetic types and extend to the transition between consciousness and unconsciousness.},
			author = {Hutchison, R. Matthew and Hutchison, Melina and Manning, Kathryn Y. and Menon, Ravi S. and Everling, Stefan},
			doi = {10.1002/hbm.22583},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/Hutchison{\_}et{\_}al-2014-Human{\_}Brain{\_}Mapping.pdf:pdf},
			isbn = {1097-0193 (Electronic)$\backslash$r1065-9471 (Linking)},
			issn = {10659471},
			journal = {Human Brain Mapping},
			keywords = {Anesthesia,Connectivity,Consciousness,Functional repertoire resting-state functional MRI},
			month = {dec},
			number = {12},
			pages = {5754--5775},
			pmid = {25044934},
			title = {{Isoflurane induces dose-dependent alterations in the cortical connectivity profiles and dynamic properties of the brain's functional architecture}},
			url = {http://doi.wiley.com/10.1002/hbm.22583},
			volume = {35},
			year = {2014}
			}

			@article{Elton2015,
			abstract = {Two new directions of functional connectivity investigation are emerging to advance studies of the brain's functional organization. First, the identification of task-related dynamics of functional connectivity has elicited a growing interest in characterizing the brain's functional reorganization due to task demands. Second, the nonstationarity of functional connectivity [i.e., functional connectivity variability (FCV)] within a single brain state has been increasingly recognized and studied. However, a combined investigation of these two avenues of research to explore the potential task-modulation of FCV is lacking, which, nevertheless, could both improve our understanding of the potential sources of FCV and also reveal new strategies to study the neural correlates of task performance. In this study, 19 human subjects underwent four functional magnetic resonance imaging (fMRI) scans including both resting and task states to study task-related modulation of FCV. Consistent with the hypothesis that FCV is partly underpinned by unconstrained mind wandering, FCV demonstrated significant task-related decreases measured at the regional, network and system levels, which was greater for between-network interactions than within-network connections. Conversely, there remained a significant degree of residual variability during the task scans, suggesting that FCV is not specific to the resting state and likely includes an intrinsic, physiologically driven component. Finally, the degree of task-induced decreases in FCV was significantly correlated with task performance accuracy, supporting its behavior significance. Overall, task modulation of FCV may represent an important direction for future studies, not only to provide insight into normal brain functioning but also to reveal potential biomarkers of various brain disorders.},
			author = {Elton, Amanda and Gao, Wei},
			doi = {10.1002/hbm.22847},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC correlates - EEG or behavior/Elton2015{\_}Task-related modulation of functional connectivity variability and its behavioral correlations - Elton - 2015 - Human Brain Mapping - Wiley Online Library{\_}.pdf:pdf},
			issn = {10659471},
			journal = {Human Brain Mapping},
			keywords = {Behavior,FMRI,Neural interconnections,Neural networks,Task performance},
			month = {aug},
			number = {8},
			pages = {3260--3272},
			pmid = {26015070},
			title = {{Task-related modulation of functional connectivity variability and its behavioral correlations}},
			url = {http://doi.wiley.com/10.1002/hbm.22847},
			volume = {36},
			year = {2015}
			}

			@inproceedings{Preti2015,
			author = {Preti, Maria Giulia and Haller, Sven and Giannakopoulos, Panteleimon and {Van De Ville}, Dimitri},
			booktitle = {2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)},
			doi = {10.1109/ISBI.2015.7163811},
			isbn = {978-1-4799-2374-8},
			month = {apr},
			pages = {38--41},
			publisher = {IEEE},
			title = {{Decomposing dynamic functional connectivity onto phase-dependent eigenconnectivities using the Hilbert transform}},
			url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7163811},
			year = {2015}
			}

			@article{Calhoun2014,
			author = {Calhoun, Vince D. and Miller, Robyn and Pearlson, Godfrey and AdalÆÂ±, Tulay},
			doi = {10.1016/j.neuron.2014.10.015},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC reviews/Chaloun2015review{\_}chronnectome.pdf:pdf},
			issn = {08966273},
			journal = {Neuron},
			month = {oct},
			number = {2},
			pages = {262--274},
			title = {{The Chronnectome: Time-Varying Connectivity Networks as the Next Frontier in fMRI Data Discovery}},
			url = {http://linkinghub.elsevier.com/retrieve/pii/S0896627314009131},
			volume = {84},
			year = {2014}
			}

			@article{Madhyastha2014,
			author = {Madhyastha, Tara M and Grabowski, Thomas J},
			doi = {10.1089/brain.2013.0205},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC correlates - EEG or behavior/Madhystha2013{\_}age.pdf:pdf},
			issn = {2158-0014},
			journal = {Brain Connectivity},
			keywords = {aging,default mode network,dynamic connectivity,fmri,functional connectivity,resting state},
			month = {may},
			number = {4},
			pages = {231--241},
			title = {{Age-Related Differences in the Dynamic Architecture of Intrinsic Networks}},
			url = {http://online.liebertpub.com/doi/abs/10.1089/brain.2013.0205},
			volume = {4},
			year = {2014}
			}

			@article{Damoiseaux2006,
			abstract = {Functional MRI (fMRI) can be applied to study the functional connectivity of the human brain. It has been suggested that fluctuations in the blood oxygenation level-dependent (BOLD) signal during rest reflect the neuronal baseline activity of the brain, representing the state of the human brain in the absence of goal-directed neuronal action and external input, and that these slow fluctuations correspond to functionally relevant resting-state networks. Several studies on resting fMRI have been conducted, reporting an apparent similarity between the identified patterns. The spatial consistency of these resting patterns, however, has not yet been evaluated and quantified. In this study, we apply a data analysis approach called tensor probabilistic independent component analysis to resting-state fMRI data to find coherencies that are consistent across subjects and sessions. We characterize and quantify the consistency of these effects by using a bootstrapping approach, and we estimate the BOLD amplitude modulation as well as the voxel-wise cross-subject variation. The analysis found 10 patterns with potential functional relevance, consisting of regions known to be involved in motor function, visual processing, executive functioning, auditory processing, memory, and the so-called default-mode network, each with BOLD signal changes up to 3{\%}. In general, areas with a high mean percentage BOLD signal are consistent and show the least variation around the mean. These findings show that the baseline activity of the brain is consistent across subjects exhibiting significant temporal dynamics, with percentage BOLD signal change comparable with the signal changes found in task-related experiments.},
			author = {Damoiseaux, J S and Rombouts, S A R B and Barkhof, F and Scheltens, P and Stam, C J and Smith, S M and Beckmann, C F},
			doi = {10.1073/pnas.0601417103},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/new/new/PNAS-2006-Damoiseaux-13848-53.pdf:pdf},
			isbn = {0027-8424},
			issn = {0027-8424},
			journal = {Proceedings of the National Academy of Sciences of the United States of America},
			keywords = {Brain,Brain Mapping,Brain: physiology,Health,Humans,Magnetic Resonance Imaging,Rest,Rest: physiology},
			number = {37},
			pages = {13848--53},
			pmid = {16945915},
			title = {{Consistent resting-state networks across healthy subjects.}},
			url = {http://www.pnas.org/content/103/37/13848.short},
			volume = {103},
			year = {2006}
			}

			@article{Schaefer2014,
			author = {Schaefer, Alexander and Margulies, Daniel S and Lohmann, Gabriele and Gorgolewski, Krzysztof J and Smallwood, Jonathan and Kiebel, Stefan J and Villringer, Arno},
			doi = {10.3389/fnhum.2014.00195},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/Schaefer2014{\_}DynamicNetworkParticipation.pdf:pdf},
			issn = {1662-5161},
			journal = {Frontiers in Human Neuroscience},
			keywords = {brain networks,graphs,mind wandering,self-generated thoughts},
			month = {may},
			number = {195},
			pages = {1--13},
			title = {{Dynamic network participation of functional connectivity hubs assessed by resting-state fMRI}},
			url = {http://journal.frontiersin.org/article/10.3389/fnhum.2014.00195/abstract},
			volume = {8},
			year = {2014}
			}

			@article{Falahpour2016,
			author = {Falahpour, Maryam and Thompson, Wesley K. and Abbott, Angela E. and Jahedi, Afrooz and Mulvey, Mark E. and Datko, Michael and Liu, Thomas T. and M{\"{u}}ller, Ralph-Axel},
			doi = {10.1089/brain.2015.0389},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/new/Falahpour2016.pdf:pdf},
			issn = {2158-0014},
			journal = {Brain Connectivity},
			keywords = {2010,2013,autism spectrum disorder,default mode network,fmri,functional connectivity,intra-individual,mediation analysis,resting state,technique,the validity of the,van dijk et al,variability},
			month = {jun},
			number = {5},
			pages = {403--414},
			pmid = {26973154},
			title = {{Underconnected, But Not Broken? Dynamic Functional Connectivity MRI Shows Underconnectivity in Autism Is Linked to Increased Intra-Individual Variability Across Time}},
			url = {http://online.liebertpub.com/doi/10.1089/brain.2015.0389},
			volume = {6},
			year = {2016}
			}

			@article{Hudetz2015,
			author = {Hudetz, Anthony G and Liu, Xiping and Pillay, Siveshigan},
			doi = {10.1089/brain.2014.0230},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/CAPS/Hudotz2015{\_}caps{\_}rat.pdf:pdf},
			issn = {2158-0014},
			journal = {Brain Connectivity},
			keywords = {anesthesia,consciousness,default mode network,fmri,functional connectivity},
			month = {feb},
			number = {1},
			pages = {10--22},
			title = {{Dynamic Repertoire of Intrinsic Brain States Is Reduced in Propofol-Induced Unconsciousness}},
			url = {http://online.liebertpub.com/doi/abs/10.1089/brain.2014.0230},
			volume = {5},
			year = {2015}
			}

			@article{Handwerker2012,
			archivePrefix = {arXiv},
			arxivId = {NIHMS150003},
			author = {Handwerker, Daniel A. and Roopchansingh, Vinai and Gonzalez-Castillo, Javier and Bandettini, Peter A.},
			doi = {10.1016/j.neuroimage.2012.06.078},
			eprint = {NIHMS150003},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/Handwerker2012.pdf:pdf},
			isbn = {2156623929},
			issn = {10538119},
			journal = {NeuroImage},
			keywords = {colorectal tumor,hepatoma,hsv,immunotherapy,oncolytic therapy},
			month = {nov},
			number = {3},
			pages = {1712--1719},
			pmid = {1000000221},
			title = {{Periodic changes in fMRI connectivity}},
			url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811912007124},
			volume = {63},
			year = {2012}
			}

			@article{Betzel2016,
			author = {Betzel, Richard F and Fukushima, Makoto and He, Ye and Zuo, Xi-nian and Sporns, Olaf},
			doi = {10.1016/j.neuroimage.2015.12.001},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/Modularity/Betzel2016{\_}modularity.pdf:pdf},
			issn = {10538119},
			journal = {NeuroImage},
			month = {feb},
			pages = {287--297},
			title = {{Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks}},
			url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811915011143},
			volume = {127},
			year = {2016}
			}

			@article{Tagliazucchi2011,
			abstract = {Recent neuroimaging studies have demonstrated that the spontaneous brain activity reflects, to a large extent, the same activation patterns measured in response to cognitive and behavioral tasks. This correspondence between activation and rest has been explored with a large repertoire of computational methods, ranging from analysis of pairwise interactions between areas of the brain to the global brain networks yielded by independent component analysis. In this paper we describe an alternative method based on the averaging of the BOLD signal at a region of interest (target) triggered by spontaneous increments in activity at another brain area (seed). The resting BOLD event triggered averages (" rBeta") can be used to estimate functional connectivity at resting state. Using two simple examples, here we illustrate how the analysis of the average response triggered by spontaneous increases/decreases in the BOLD signal is sufficient to capture the aforementioned correspondence in a variety of circumstances. The computation of the non linear response during rest here described allows for a direct comparison with results obtained during task performance, providing an alternative measure of functional interaction between brain areas. ?? 2010 Elsevier Ireland Ltd.},
			archivePrefix = {arXiv},
			arxivId = {NIHMS150003},
			author = {Tagliazucchi, Enzo and Balenzuela, Pablo and Fraiman, Daniel and Montoya, Pedro and Chialvo, Dante R.},
			doi = {10.1016/j.neulet.2010.11.020},
			eprint = {NIHMS150003},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/CAPS/Tagliazucchi{\_}2011{\_}NeuroSciLetters{\_}PPA.pdf:pdf},
			isbn = {1872-7972},
			issn = {03043940},
			journal = {Neuroscience Letters},
			keywords = {Functional connectivity,Functional magnetic resonance imaging,Resting state,Triggered averages},
			month = {jan},
			number = {2},
			pages = {158--163},
			pmid = {21078369},
			publisher = {Elsevier Ireland Ltd},
			title = {{Spontaneous BOLD event triggered averages for estimating functional connectivity at resting state}},
			url = {http://dx.doi.org/10.1016/j.neulet.2010.11.020 http://linkinghub.elsevier.com/retrieve/pii/S0304394010014746},
			volume = {488},
			year = {2011}
			}

			@inproceedings{Miller2014,
			abstract = {Assessments of functional connectivity between brain networks is a fixture of resting state fMRI research. Until very recently most of this work proceeded from an assumption of stationarity in resting state network connectivity. In the last few years however, interest in moving beyond this simplifying assumption has grown considerably. Applying group temporal independent component analysis (tICA) to a set of time-varying functional network connectivity (FNC) matrices derived from a large multi-site fMRI dataset (N=314; 163 healthy, 151 schizophrenia patients), we obtain a set of five basic correlation patterns (component spatial maps (SMs)) from which observed FNCs can be expressed as mutually independent linear combinations, i.e., the coefficient on each SM in the linear combination is maximally independent of the others. We study dynamic properties of network connectivity as they are reflected in this five-dimensional space, and report stark differences in connectivity dynamics between schizophrenia patients and healthy controls. We also find that the most important global differences in FNC dynamism between patient and control groups are replicated when the same dynamical analysis is performed on sets of correlation patterns obtained from either PCA or spatial ICA, giving us additional confidence in the results.},
			author = {Miller, Robyn L. and Yaesoubi, Maziar and Calhoun, Vince D.},
			booktitle = {2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
			doi = {10.1109/EMBC.2014.6944460},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC tools applied to neurodegenerative disorders/Schizophrenia/Miller2014{\_}Schizophrenia.pdf:pdf},
			isbn = {978-1-4244-7929-0},
			issn = {1557170X},
			keywords = {Blind source separation,Connectivity measurements,Nonstationary processing of biomedical signals},
			month = {aug},
			pages = {3837--3840},
			pmid = {25570828},
			publisher = {IEEE},
			title = {{Higher dimensional analysis shows reduced dynamism of time-varying network connectivity in schizophrenia patients}},
			url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6944460},
			volume = {2014},
			year = {2014}
			}

			@article{Di2013,
			abstract = {Studies of large-scale brain functional connectivity using the resting-state functional magnetic resonance imaging have advanced our understanding of human brain functions. Although the evidence of dynamic functional connectivity is accumulating, the variations of functional connectivity over time have not been well characterized. In the present study, we aimed to associate the variations of functional connectivity with the intrinsic activities of resting-state networks during a single resting-state scan by comparing functional connectivity differences between when a network had higher and lower intrinsic activities. The activities of the salience network, default mode network (DMN), and motor network were associated with changes of resting-state functional connectivity. Higher activity of the salience network was accompanied by greater functional connectivity between the fronto-parietal regions and the DMN regions, and between the regions within the DMN. Higher DMN activity was associated with less connectivity between the regions within the DMN, and greater connectivity between the regions within the fronto-parietal network. Higher motor network activity was correlated with greater connectivity between the regions within the motor network, and smaller connectivity between the DMN regions and fronto-parietal regions, and between the DMN regions and the motor regions. In addition, the whole brain network modularity was positively correlated with the motor network activity, suggesting that the brain is more segregated as sub-systems when the motor network is intrinsically activated. Together, these results demonstrate the association between the resting-state connectivity variations and the intrinsic activities of specific networks, which can provide insights on the dynamic changes in large-scale brain connectivity and network configurations.},
			author = {Di, Xin and Biswal, Bharat B.},
			doi = {10.1007/s00429-013-0634-3},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/Di{\_}Dynamic brain functional connectivity modulated by resting-state networks{\_}2013.pdf:pdf},
			isbn = {1863-2661 (Electronic)$\backslash$r1863-2653 (Linking)},
			issn = {1863-2653},
			journal = {Brain Structure and Function},
			keywords = {Default mode network,Dynamic connectivity,Nonlinear connectivity,Resting-state,Salience network,fMRI},
			month = {jan},
			number = {1},
			pages = {37--46},
			pmid = {24077799},
			title = {{Dynamic brain functional connectivity modulated by resting-state networks}},
			url = {http://link.springer.com/10.1007/s00429-013-0634-3},
			volume = {220},
			year = {2015}
			}

			@article{Petridou2013,
			abstract = {fMRI studies of brain activity at rest study slow ({\textless}0.1 Hz) intrinsic fluctuations in the blood-oxygenation-level-dependent (BOLD) signal that are observed in a temporal scale of several minutes. The origin of these fluctuations is not clear but has previously been associated with slow changes in rhythmic neuronal activity resulting from changes in cortical excitability or neuronal synchronization. In this work, we show that individual spontaneous BOLD events occur during rest, in addition to slow fluctuations. Individual spontaneous BOLD events were identified by deconvolving the hemodynamic impulse response function for each time point in the fMRI time series, thus requiring no information on timing or a-priori spatial information of events. The patterns of activation detected were related to the motor, visual, default-mode, and dorsal attention networks. The correspondence between spontaneous events and slow fluctuations in these networks was assessed using a sliding window, seed-correlation analysis, where seed regions were selected based on the individual spontaneous event BOLD activity maps. We showed that the correlation varied considerably over time, peaking at the time of spontaneous events in these networks. By regressing spontaneous events out of the fMRI signal, we showed that both the correlation strength and the power in spectral frequencies {\textless}0.1 Hz decreased, indicating that spontaneous activation events contribute to low-frequency fluctuations observed in resting state networks with fMRI. This work provides new insights into the origin of signals detected in fMRI studies of functional connectivity. Hum Brain Mapp, 2013. {\textcopyright} 2012 Wiley Periodicals, Inc.},
			author = {Petridou, Natalia and Gaudes, C{\'{e}}sar Caballero and Dryden, Ian L. and Francis, Susan T. and Gowland, Penny A.},
			doi = {10.1002/hbm.21513},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/Petridou2013.pdf:pdf},
			isbn = {1097-0193},
			issn = {10659471},
			journal = {Human Brain Mapping},
			keywords = {BOLD,Brain mapping,Fluctuations,Nonstationary spontaneous activity,Resting-state},
			month = {jun},
			number = {6},
			pages = {1319--1329},
			pmid = {22331588},
			title = {{Periods of rest in fMRI contain individual spontaneous events which are related to slowly fluctuating spontaneous activity}},
			url = {http://doi.wiley.com/10.1002/hbm.21513},
			volume = {34},
			year = {2013}
			}

			@article{Cordes2001,
			abstract = {BACKGROUND AND PURPOSE In subjects performing no specific cognitive task ("resting state"), time courses of voxels within functionally connected regions of the brain have high cross-correlation coefficients ("functional connectivity"). The purpose of this study was to measure the contributions of low frequencies and physiological noise to cross-correlation maps. METHODS In four healthy volunteers, task-activation functional MR imaging and resting-state data were acquired. We obtained four contiguous slice locations in the "resting state" with a high sampling rate. Regions of interest consisting of four contiguous voxels were selected. The correlation coefficient for the averaged time course and every other voxel in the four slices was calculated and separated into its component frequency contributions. We calculated the relative amounts of the spectrum that were in the low-frequency (0 to 0.1 Hz), the respiratory-frequency (0.1 to 0.5 Hz), and cardiac-frequency range (0.6 to 1.2 Hz). RESULTS For each volunteer, resting-state maps that resembled task-activation maps were obtained. For the auditory and visual cortices, the correlation coefficient depended almost exclusively on low frequencies ({\textless}0.1 Hz). For all cortical regions studied, low-frequency fluctuations contributed more than 90{\%} of the correlation coefficient. Physiological (respiratory and cardiac) noise sources contributed less than 10{\%} to any functional connectivity MR imaging map. In blood vessels and cerebrospinal fluid, physiological noise contributed more to the correlation coefficient. CONCLUSION Functional connectivity in the auditory, visual, and sensorimotor cortices is characterized predominantly by frequencies slower than those in the cardiac and respiratory cycles. In functionally connected regions, these low frequencies are characterized by a high degree of temporal coherence.},
			author = {Cordes, D. and Haughton, V. M. and Arfanakis, K. and Carew, J. D. and Turski, P. A. and Moritz, C. H. and Quigley, M. A. and Meyerand, M. E.},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/new/new/Cordes2001.pdf:pdf},
			isbn = {0195-6108 (Print)},
			issn = {01956108},
			journal = {American Journal of Neuroradiology},
			number = {7},
			pages = {1326--1333},
			pmid = {11498421},
			title = {{Frequencies contributing to functional connectivity in the cerebral cortex in "resting-state" data}},
			volume = {22},
			year = {2001}
			}

			@article{Cribben2012,
			author = {Cribben, Ivor and Haraldsdottir, Ragnheidur and Atlas, Lauren Y and Wager, Tor D and Lindquist, Martin A},
			doi = {10.1016/j.neuroimage.2012.03.070},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/DCR{\_}changepoints/Cribben{\_}2012{\_}Neuroimage{\_}DCR.pdf:pdf},
			issn = {10538119},
			journal = {NeuroImage},
			keywords = {Change point analysis,Functional connectivity,Graphical lasso,Regression trees,fMRI,functional connectivity},
			month = {jul},
			number = {4},
			pages = {907--920},
			publisher = {Elsevier Inc.},
			title = {{Dynamic connectivity regression: Determining state-related changes in brain connectivity}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2012.03.070 http://linkinghub.elsevier.com/retrieve/pii/S1053811912003515},
			volume = {61},
			year = {2012}
			}

			@article{Barttfeld2015,
			author = {Barttfeld, Pablo and Uhrig, Lynn and Sitt, Jacobo D and Sigman, Mariano and Jarraya, B{\'{e}}chir and Dehaene, Stanislas},
			doi = {10.1073/pnas.1418031112},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC correlates - EEG or behavior/Barttfeld2015{\_}Consciousness.pdf:pdf},
			issn = {0027-8424},
			journal = {Proceedings of the National Academy of Sciences},
			month = {jan},
			number = {3},
			pages = {887--892},
			title = {{Signature of consciousness in the dynamics of resting-state brain activity}},
			url = {http://www.pnas.org/lookup/doi/10.1073/pnas.1515029112 http://www.pnas.org/lookup/doi/10.1073/pnas.1418031112},
			volume = {112},
			year = {2015}
			}

			@article{Ma2014,
			author = {Ma, Sai and Calhoun, Vince D and Phlypo, Ronald and AdalÆÂ±, T{\"{u}}lay},
			doi = {10.1016/j.neuroimage.2013.12.063},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/Ma{\_}Neuroimage2014{\_}IVA.pdf:pdf},
			issn = {10538119},
			journal = {NeuroImage},
			keywords = {dynamic spatial change,independent vector analysis,spatial functional network connectivity},
			month = {apr},
			pages = {196--206},
			title = {{Dynamic changes of spatial functional network connectivity in healthy individuals and schizophrenia patients using independent vector analysis}},
			url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811914000093},
			volume = {90},
			year = {2014}
			}

			@article{Li2013,
			abstract = {Diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) have been widely used to study structural and functional brain connectivity in recent years. A common assumption used in many previous functional brain connectivity studies is the temporal stationarity. However, accumulating literature evidence has suggested that functional brain connectivity is under temporal dynamic changes in different time scales. In this paper, a novel and intuitive approach is proposed to model and detect dynamic changes of functional brain states based on multimodal fMRI/DTI data. The basic idea is that functional connectivity patterns of all fiber-connected cortical voxels are concatenated into a descriptive functional feature vector to represent the brain's state, and the temporal change points of brain states are decided by detecting the abrupt changes of the functional vector patterns via the sliding window approach. Our extensive experimental results have shown that meaningful brain state change points can be detected in task-based fMRI/DTI, resting state fMRI/DTI, and natural stimulus fMRI/DTI data sets. Particularly, the detected change points of functional brain states in task-based fMRI corresponded well to the external stimulus paradigm administered to the participating subjects, thus partially validating the proposed brain state change detection approach. The work in this paper provides novel perspective on the dynamic behaviors of functional brain connectivity and offers a starting point for future elucidation of the complex patterns of functional brain interactions and dynamics.},
			archivePrefix = {arXiv},
			arxivId = {NIHMS150003},
			author = {Li, Xiang and Lim, Chulwoo and Li, Kaiming and Guo, Lei and Liu, Tianming},
			doi = {10.1007/s12021-012-9157-y},
			eprint = {NIHMS150003},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/new/Li2013.pdf:pdf},
			isbn = {1202101291},
			issn = {1539-2791},
			journal = {Neuroinformatics},
			keywords = {Brain connectivity,Brain state change,Diffusion tensor imaging,Functional MRI},
			month = {apr},
			number = {2},
			pages = {193--210},
			pmid = {22941508},
			title = {{Detecting Brain State Changes via Fiber-Centered Functional Connectivity Analysis}},
			url = {http://link.springer.com/10.1007/s12021-012-9157-y},
			volume = {11},
			year = {2013}
			}

			@article{Rogers2007,
			author = {Rogers, Baxter P. and Morgan, Victoria L. and Newton, Allen T. and Gore, John C.},
			doi = {10.1016/j.mri.2007.03.007},
			file = {:Users/gpreti/Documents/Articles/FC/Assessing Functional Connectivity in the Human Brain by FMRI.pdf:pdf},
			issn = {0730725X},
			journal = {Magnetic Resonance Imaging},
			month = {dec},
			number = {10},
			pages = {1347--1357},
			pmid = {17499467},
			title = {{Assessing functional connectivity in the human brain by fMRI}},
			url = {http://linkinghub.elsevier.com/retrieve/pii/S0730725X07002238},
			volume = {25},
			year = {2007}
			}

			@article{Su2016,
			author = {Su, Jianpo and Shen, Hui and Zeng, Ling-Li and Qin, Jian and Liu, Zhening and Hu, Dewen},
			doi = {10.1097/WNR.0000000000000622},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC tools applied to neurodegenerative disorders/Schizophrenia/Su2016{\_}schizophrenia.pdf:pdf},
			isbn = {0000000000000},
			issn = {0959-4965},
			journal = {NeuroReport},
			keywords = {dynamic functional connectivity,functional mri,heredity,schizophrenia,sibling,trait-related connectivity},
			month = {aug},
			number = {11},
			pages = {843--848},
			title = {{Heredity characteristics of schizophrenia shown by dynamic functional connectivity analysis of resting-state functional MRI scans of unaffected siblings}},
			url = {http://content.wkhealth.com/linkback/openurl?sid=WKPTLP:landingpage{\&}an=00001756-201608010-00009},
			volume = {27},
			year = {2016}
			}

			@article{Basile2013,
			abstract = {BACKGROUND: In multiple sclerosis (MS), the location of focal lesions does not always correlate with clinical symptoms, suggesting disconnection as a major pathophysiological mechanism. Resting-state (RS) functional magnetic resonance imaging (fMRI) is believed to reflect brain functional connectivity (FC) within specific neuronal networks.$\backslash$n$\backslash$nOBJECTIVE: RS-fMRI was used to investigate changes in FC within two critical networks for the understanding of MS disabilities, namely, the sensory-motor network (SMN) and the default-mode network (DMN), respectively, implicated in sensory-motor and cognitive functions.$\backslash$n$\backslash$nMETHODS: Thirty-four relapsing-remitting (RR), 14 secondary progressive (SP) MS patients and 25 healthy controls underwent MRI at 3T, including conventional images, T1-weighted volumes, and RS-fMRI sequences. Independent component analysis (ICA) was employed to extract maps of the relevant RS networks for every participant. Group analyses were performed to assess changes in FC within the SMN and DMN in the two MS phenotypes.$\backslash$n$\backslash$nRESULTS: Increased FC was found in both networks of MS patients. Interestingly, specific changes in either direction were observed also between RR and SP MS groups.$\backslash$n$\backslash$nCONCLUSIONS: FC changes seem to parallel patients' clinical state and capability of compensating for the severity of clinical/cognitive disabilities.},
			author = {Basile, B and Castelli, M and Monteleone, F and Nocentini, U and Caltagirone, C and Centonze, D and Cercignani, M and Bozzali, M},
			doi = {10.1177/1352458513515082},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC tools applied to neurodegenerative disorders/Multiple Sclerosis/Basile2013{\_}Mult Scler.pdf:pdf},
			issn = {1352-4585},
			journal = {Multiple Sclerosis Journal},
			keywords = {10 july 2013,2 november 2013,23 september 2013,accepted,date received,functional connectivity,relapsing,remitting multiple sclerosis,resonance imaging,resting-state functional magnetic,revised,secondary progressive multiple sclerosis},
			month = {jul},
			number = {8},
			pages = {1050--1057},
			pmid = {24326671},
			title = {{Functional connectivity changes within specific networks parallel the clinical evolution of multiple sclerosis}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/24326671 http://msj.sagepub.com/cgi/doi/10.1177/1352458513515082},
			volume = {20},
			year = {2014}
			}

			@article{Hindriks2016,
			author = {Hindriks, R and Adhikari, M.H. and Murayama, Y and Ganzetti, M and Mantini, D and Logothetis, N.K. and Deco, G},
			doi = {10.1016/j.neuroimage.2015.11.055},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC-related methodological enquiries/Hindriks2015{\_}Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI{\_}.pdf:pdf},
			issn = {10538119},
			journal = {NeuroImage},
			keywords = {Dynamic functional connectivity,Functional MRI,Resting state,Surrogate data},
			month = {feb},
			pages = {242--256},
			publisher = {The Authors},
			title = {{Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI?}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2015.11.055 http://linkinghub.elsevier.com/retrieve/pii/S1053811915010782},
			volume = {127},
			year = {2016}
			}

			@article{Demirtas2016,
			abstract = {Resting-state fMRI (RS-fMRI) has become a useful tool to investigate the connectivity structure of mental health disorders. In the case of major depressive disorder (MDD), recent studies regarding the RS-fMRI have found abnormal connectivity in several regions of the brain, particularly in the default mode network (DMN). Thus, the relevance of the DMN to self-referential thoughts and ruminations has made the use of the resting-state approach particularly important for MDD. The majority of such research has relied on the grand averaged functional connectivity measures based on the temporal correlations between the BOLD time series of various brain regions. We, in our study, investigated the variations in the functional connectivity over time at global and local level using RS-fMRI BOLD time series of 27 MDD patients and 27 healthy control subjects. We found that global synchronization and temporal stability were significantly increased in the MDD patients. Furthermore, the participants with MDD showed significantly increased overall average (static) functional connectivity (sFC) but decreased variability of functional connectivity (vFC) within specific networks. Static FC increased to predominance among the regions pertaining to the default mode network (DMN), while the decreased variability of FC was observed in the connections between the DMN and the frontoparietal network. Hum Brain Mapp, 2016. {\textcopyright} 2016 Wiley Periodicals, Inc.},
			author = {DemirtasÃ¼, Murat and Tornador, Cristian and Falc{\'{o}}n, Carles and L{\'{o}}pez-Sol{\`{a}}, Marina and Hern{\'{a}}ndez-Ribas, Rosa and Pujol, Jes{\'{u}}s and Mench{\'{o}}n, Jos{\'{e}} M and Ritter, Petra and Cardoner, Narcis and Soriano-Mas, Carles and Deco, Gustavo},
			doi = {10.1002/hbm.23215},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC tools applied to neurodegenerative disorders/Depression and co/Demirats2016{\_}majorDepressiveDisorder..pdf:pdf},
			issn = {10659471},
			journal = {Human Brain Mapping},
			month = {aug},
			number = {8},
			pages = {2918--2930},
			pmid = {27120982},
			title = {{Dynamic functional connectivity reveals altered variability in functional connectivity among patients with major depressive disorder}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/27120982 http://doi.wiley.com/10.1002/hbm.23215},
			volume = {37},
			year = {2016}
			}

			@inproceedings{Sourty2016b,
			author = {Sourty, Marion and Thoraval, Laurent and Armspach, Jean-paul and Foucher, Jack},
			booktitle = {2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)},
			doi = {10.1109/ISBI.2016.7493503},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/new/Sourty2016b.pdf:pdf},
			isbn = {978-1-4799-2349-6},
			keywords = {Connectivity analysis,Probabilistic and statistical models {\&} methods,fMRI analysis},
			month = {apr},
			pages = {1291--1294},
			publisher = {IEEE},
			title = {{Product Hidden Markov Models for subject-based dynamic functional connectivity analysis of the resting state brain}},
			url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7493503},
			year = {2016}
			}

			@article{Han2011,
			title={Frequency-dependent changes in the amplitude of low-frequency fluctuations in amnestic mild cognitive impairment: a resting-state fMRI study},
			author={Han, Ying and Wang, Jinhui and Zhao, Zhilian and Min, Baoquan and Lu, Jie and Li, Kuncheng and He, Yong and Jia, Jianping},
			journal={Neuroimage},
			volume={55},
			number={1},
			pages={287--295},
			year={2011},
			publisher={Elsevier}
			}

			@article{Wee2012,
			title={Resting-state multi-spectrum functional connectivity networks for identification of MCI patients},
			author={Wee, Chong-Yaw and Yap, Pew-Thian and Denny, Kevin and Browndyke, Jeffrey N and Potter, Guy G and Welsh-Bohmer, Kathleen A and Wang, Lihong and Shen, Dinggang},
			journal={PloS one},
			volume={7},
			number={5},
			pages={e37828},
			year={2012},
			publisher={Public Library of Science}
			}

			@article{Wee2013,
			abstract = {In conventional resting-state functional MRI (R-fMRI) analysis, functional connectivity is assumed to be temporally stationary, overlooking neural activities or interactions that may happen within the scan duration. Dynamic changes of neural interactions can be reflected by variations of topology and correlation strength in temporally correlated functional connectivity networks. These connectivity networks may potentially capture subtle yet short neural connectivity disruptions induced by disease pathologies. Accordingly, we are motivated to utilize disrupted temporal network properties for improving control-patient classification performance. Specifically, a sliding window approach is firstly employed to generate a sequence of overlapping R-fMRI sub-series. Based on these sub-series, sliding window correlations, which characterize the neural interactions between brain regions, are then computed to construct a series of temporal networks. Individual estimation of these temporal networks using conventional network construction approaches fails to take into consideration intrinsic temporal smoothness among successive overlapping R-fMRI sub-series. To preserve temporal smoothness of R-fMRI sub-series, we suggest to jointly estimate the temporal networks by maximizing a penalized log likelihood using a fused sparse learning algorithm. This sparse learning algorithm encourages temporally correlated networks to have similar network topology and correlation strengths. We design a disease identification framework based on the estimated temporal networks, and group level network property differences and classification results demonstrate the importance of including temporally dynamic R-fMRI scan information to improve diagnosis accuracy of mild cognitive impairment patients.},
			author = {Wee, Chong-Yaw and Yang, Sen and Yap, Pew-Thian and Shen, Dinggang},
			doi = {10.1007/s11682-015-9408-2},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC tools applied to neurodegenerative disorders/Mild Cognitive Impairment (MCI)/Wee2016{\_}MCI.pdf:pdf},
			isbn = {9783319022666},
			issn = {1931-7557},
			journal = {Brain Imaging and Behavior},
			keywords = {Mild Cognitive Impairment (MCI),Resting-state functional MRI (R-fMRI),Sliding window correlation,Sparse temporal networks,Temporal dynamics,Temporal smoothness,correlation,mci,mild cognitive impairment,r-fmri,resting-state functional mri,sliding window,sparse temporal networks,temporal dynamics,temporal smoothness},
			month = {jun},
			number = {2},
			pages = {342--356},
			pmid = {26123390},
			title = {{Sparse temporally dynamic resting-state functional connectivity networks for early MCI identification}},
			url = {http://link.springer.com/10.1007/978-3-319-02267-3{\_}18 http://link.springer.com/10.1007/s11682-015-9408-2},
			volume = {10},
			year = {2016}
			}

			@article{Nielsen2016,
			abstract = {Dynamic functional connectivity (FC) has in recent years become a topic of interest in the neuroimaging community. Several models and methods exist for both functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), and the results point towards the conclusion that FC exhibits dynamic changes. The existing approaches modeling dynamic connectivity have primarily been based on time-windowing the data and k-means clustering. We propose a non-parametric generative model for dynamic FC in fMRI that does not rely on specifying window lengths and number of dynamic states. Rooted in Bayesian statistical modeling we use the predictive likelihood to investigate if the model can discriminate between a motor task and rest both within and across subjects. We further investigate what drives dynamic states using the model on the entire data collated across subjects and task/rest. We find that the number of states extracted are driven by subject variability and preprocessing differences while the individual states are almost purely defined by either task or rest. This questions how we in general interpret dynamic FC and points to the need for more research on what drives dynamic FC.},
			archivePrefix = {arXiv},
			arxivId = {1601.00496},
			author = {Nielsen, S{\o}ren F V and Madsen, Kristoffer H and R{\o}ge, Rasmus and Schmidt, Mikkel N. and M{\o}rup, Morten},
			eprint = {1601.00496},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/new/Nielsen.pdf:pdf},
			keywords = {bayesian nonparametric,dynamic functional connectivity,hidden markov modeling,modeling,predic-,wishart mixture modeling},
			month = {jan},
			title = {{Nonparametric Modeling of Dynamic Functional Connectivity in fMRI Data}},
			url = {http://arxiv.org/abs/1601.00496},
			year = {2016}
			}

			@article{Morgan2014,
			abstract = {Abstract This study presents a cross-sectional investigation of functional networks in temporal lobe epilepsy (TLE) as they evolve over years of disease. Networks of interest were identified based on a priori hypotheses: the network of seizure propagation ipsilateral to the seizure focus, the same regions contralateral to seizure focus, the cross hemisphere network of the same regions, and a cingulate midline network. Resting functional magnetic resonance imaging data were acquired for 20 min in 12 unilateral TLE patients, and 12 age- and gender-matched healthy controls. Functional changes within and between the four networks as they evolve over years of disease were quantified by standard measures of static functional connectivity and novel measures of dynamic functional connectivity. The results suggest an initial disruption of cross-hemispheric networks and an increase in static functional connectivity in the ipsilateral temporal network accompanying the onset of TLE seizures. As seizures progress over years, the static functional connectivity across the ipsilateral network diminishes, while dynamic functional connectivity measures show the functional independence of this ipsilateral network from the network of midline regions of the cingulate declines. This implies a gradual breakdown of the seizure onset and early propagation network involving the ipsilateral hippocampus and temporal lobe as it becomes more synchronous with the network of regions responsible for secondary generalization of the seizures, a process that may facilitate the spread of seizures across the brain. Ultimately, the significance of this evolution may be realized in relating it to symptoms and treatment outcomes of TLE.},
			author = {Morgan, Victoria L and Abou-Khalil, Bassel and Rogers, Baxter P},
			doi = {10.1089/brain.2014.0251},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC tools applied to neurodegenerative disorders/Epilepsy/Morgan2015{\_}TLE.pdf:pdf},
			issn = {2158-0022},
			journal = {Brain connectivity},
			keywords = {brain,functional connectivity,functional magnetic resonance imaging,network,seizure propagation,temporal lobe epilepsy},
			number = {1},
			pages = {35--44},
			pmid = {24901036},
			title = {{Evolution of Functional Connectivity of Brain Networks and Their Dynamic Interaction in Temporal Lobe Epilepsy.}},
			url = {http://online.liebertpub.com/doi/abs/10.1089/brain.2014.0251?url{\_}ver=Z39.88-2003{\&}rfr{\_}id=ori:rid:crossref.org{\&}rfr{\_}dat=cr{\_}pub=pubmed{\&}},
			volume = {5},
			year = {2014}
			}

			@article{Glerean2012,
			author = {Glerean, Enrico and Salmi, Juha and Lahnakoski, Juha M and J{\"{a}}{\"{a}}skel{\"{a}}inen, Iiro P. and Sams, Mikko},
			doi = {10.1089/brain.2011.0068},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/Glerean2012{\_}PhaseSync.pdf:pdf},
			issn = {2158-0014},
			journal = {Brain Connectivity},
			keywords = {circular statistics,dynamic functional connectivity,fmri,instantaneous,nonlinear time series analy-,phase synchronization,sis},
			month = {apr},
			number = {2},
			pages = {91--101},
			title = {{Functional Magnetic Resonance Imaging Phase Synchronization as a Measure of Dynamic Functional Connectivity}},
			url = {http://online.liebertpub.com/doi/abs/10.1089/brain.2011.0068},
			volume = {2},
			year = {2012}
			}

			@inproceedings{Shakil2014,
			abstract = {Different regions in the resting brain exhibit non-stationary functional connectivity (FC) over time. In this paper, a simple and efficient framework of clustering the variability in FC of a rat's brain at rest is proposed. This clustering process reveals areas that are always connected with a chosen region, called seed voxel, along with the areas exhibiting variability in the FC. This addresses an issue common to most dynamic FC analysis techniques, which is the assumption that the spatial extent of a given network remains constant over time. We increase the voxel size and reduce the spatial resolution to analyze variable FC of the whole resting brain. We hypothesize that the adjacent voxels in resting state functional magnetic resonance imaging (rsfMRI), just as in task-based fMRI, exhibit similar intensities, so they can be averaged to obtain larger voxels without any significant loss of information. Sliding window correlation is used to compute variable patterns of the rat's whole brain FC with the seed voxel in the sensorimotor cortex. These patterns are grouped based on their spatial similarities using binary transformed feature vectors in k-means clustering, not only revealing the variable and nonvariable portions of FC in the resting brain but also detecting the extent of the variability of these patterns.},
			author = {Shakil, Sadia and Magnuson, Matthew E. and Keilholz, Shella D. and {Chin-Hui Lee}},
			booktitle = {2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
			doi = {10.1109/EMBC.2014.6943757},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/Shakil2014.pdf:pdf},
			isbn = {978-1-4244-7929-0},
			issn = {1557-170X},
			keywords = {Clustering,Dunn's Index,Functional Connectivity,Functional MRI,Resting State Functional MRI,Sliding Window Correlation,k-means},
			month = {aug},
			pages = {982--985},
			pmid = {25570125},
			publisher = {IEEE},
			title = {{Cluster-based analysis for characterizing dynamic functional connectivity}},
			url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6943757},
			year = {2014}
			}

			@article{Cribben2013,
			abstract = {Recently in functional magnetic resonance imaging (fMRI) studies there has been an increased interest in understanding the dynamic manner in which brain regions communicate with one another, as subjects perform a set of experimental tasks or as their psychological state changes. Dynamic Connectivity Regression (DCR) is a data-driven technique used for detecting temporal change points in functional connectivity between brain regions where the number and location of the change points are unknown a priori. After finding the change points, DCR estimates a graph or set of relationships between the brain regions for data that falls between pairs of change points. In previous work, the method was predominantly validated using multi-subject data. In this paper, we concentrate on single-subject data and introduce a new DCR algorithm. The new algorithm increases accuracy for individual subject data with a small number of observations and reduces the number of false positives in the estimated undirected graphs. We also introduce a new Likelihood Ratio test for comparing sparse graphs across (or within) subjects; thus allowing us to determine whether data should be combined across subjects. We perform an extensive simulation analysis on vector autoregression (VAR) data as well as to an fMRI data set from a study (n = 23) of a state anxiety induction using a socially evaluative threat challenge. The focus on single-subject data allows us to study the variation between individuals and may provide us with a deeper knowledge of the workings of the brain.},
			author = {Cribben, Ivor and Wager, Tor D and Lindquist, Martin a},
			doi = {10.3389/fncom.2013.00143},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/DCR{\_}changepoints/Cribben2013.pdf:pdf},
			isbn = {1662-5188 (Electronic)$\backslash$r1662-5188 (Linking)},
			issn = {1662-5188},
			journal = {Frontiers in computational neuroscience},
			keywords = {detection,dynamic connectivity,functional connectivity,functional connectivity, graph based change point,graph based change point,graphical lasso,network change points,stability selection,stationary bootstrap},
			number = {October},
			pages = {143},
			pmid = {24198781},
			title = {{Detecting functional connectivity change points for single-subject fMRI data.}},
			url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3812660{\&}tool=pmcentrez{\&}rendertype=abstract},
			volume = {7},
			year = {2013}
			}

			@article{Lee2013,
			abstract = {Current resting-state network analysis often looks for coherent spontaneous BOLD signal fluctuations at frequencies below 0.1Hz in a multiple-minutes scan. However hemodynamic signal variation can occur at a faster rate, causing changes in functional connectivity at a smaller time scale. In this study we proposed to use MREG technique to increase the temporal resolution of resting-state fMRI. A three-dimensional single-shot concentric shells trajectory was used instead of conventional EPI, with a TR of 100ms and a nominal spatial resolution of 4âÃ³4âÃ³4mm3. With this high sampling rate we were able to resolve frequency components up to 5Hz, which prevents major physiological noises from aliasing with the BOLD signal of interest. We used a sliding-window method on signal components at different frequency bands, to look at the non-stationary connectivity maps over the course of each scan session. The aim of the study paradigm was to specifically observe visual and motor resting-state networks. Preliminary results have found corresponding networks at frequencies above 0.1Hz. These networks at higher frequencies showed better stability in both spatial and temporal dimensions from the sliding-window analysis of the time series, which suggests the potential of using high temporal resolution MREG sequences to track dynamic resting-state networks at sub-minute time scale. {\textcopyright} 2012 Elsevier Inc..},
			author = {Lee, Hsu-Lei and Zahneisen, Benjamin and Hugger, Thimo and LeVan, Pierre and Hennig, J{\"{u}}rgen},
			doi = {10.1016/j.neuroimage.2012.10.015},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/new/Lee2012.pdf:pdf},
			isbn = {1095-9572 (Electronic)$\backslash$n1053-8119 (Linking)},
			issn = {10538119},
			journal = {NeuroImage},
			keywords = {Functional connectivity,MREG,Resting-state networks},
			month = {jan},
			pages = {216--222},
			pmid = {23069810},
			publisher = {Elsevier Inc.},
			title = {{Tracking dynamic resting-state networks at higher frequencies using MR-encephalography}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2012.10.015 http://linkinghub.elsevier.com/retrieve/pii/S105381191201018X},
			volume = {65},
			year = {2013}
			}

			@article{Leonardi2015,
			abstract = {Functional brain networks reconfigure spontaneously during rest. Such network dynamics can be studied by dynamic functional connectivity (dynFC); i.e., sliding-window correlations between regional brain activity. Key parameters-such as window length and cut-off frequencies for filtering-are not yet systematically studied. In this letter we provide the fundamental theory from signal processing to address these parameter choices when estimating and interpreting dynFC. We guide the reader through several illustrative cases, both simple analytical models and experimental fMRI BOLD data. First, we show how spurious fluctuations in dynFC can arise due to the estimation method when the window length is shorter than the largest wavelength present in both signals, even for deterministic signals with a fixed relationship. Second, we study how real fluctuations of dynFC can be explained using a frequency-based view, which is particularly instructive for signals with multiple frequency components such as fMRI BOLD, demonstrating that fluctuations in sliding-window correlation emerge by interaction between frequency components similar to the phenomenon of beat frequencies. We conclude with practical guidelines for the choice and impact of the window length.},
			author = {Leonardi, Nora and {Van De Ville}, Dimitri},
			doi = {10.1016/j.neuroimage.2014.09.007},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC-related methodological enquiries/LeonardiVDV2015.pdf:pdf},
			isbn = {1095-9572 (Electronic) 1053-8119 (Linking)},
			issn = {10538119},
			journal = {NeuroImage},
			keywords = {Dynamic functional connectivity,Non-stationarity,Resting state,Sliding-window correlation,fMRI},
			month = {jan},
			pages = {430--436},
			pmid = {25234118},
			publisher = {Elsevier Inc.},
			title = {{On spurious and real fluctuations of dynamic functional connectivity during rest}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2014.09.007 http://linkinghub.elsevier.com/retrieve/pii/S1053811914007496},
			volume = {104},
			year = {2015}
			}

			@inproceedings{Meskaldji2015,
			author = {Meskaldji, Djalel Eddine and Morgenthaler, Stephan and Ville, Dimitri Van De},
			booktitle = {Proceedings of the Twelfth IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI'15),},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/meskaldji1502.pdf:pdf},
			isbn = {9781479923748},
			keywords = {Brain,Functional imaging (e.g. fMRI),Quantification and estimation},
			pages = {26--29},
			title = {{New measures of brain functional connectivity by temporal analysis of extreme events}},
			year = {2015}
			}

			@article{Majeed2011,
			author = {Majeed, Waqas and Magnuson, Matthew and Hasenkamp, Wendy and Schwarb, Hillary and Schumacher, Eric H and Barsalou, Lawrence and Keilholz, Shella D},
			doi = {10.1016/j.neuroimage.2010.08.030},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/Majeed2011.pdf:pdf},
			issn = {10538119},
			journal = {NeuroImage},
			keywords = {Functional connectivity,Low frequency fluctuations,Spatiotemporal dynamics,Spontaneous neural activity,functional connectivity,low frequency fluctuations,spontaneous neural activity},
			month = {jan},
			number = {2},
			pages = {1140--1150},
			publisher = {Elsevier Inc.},
			title = {{Spatiotemporal dynamics of low frequency BOLD fluctuations in rats and humans}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2010.08.030 http://linkinghub.elsevier.com/retrieve/pii/S1053811910011122},
			volume = {54},
			year = {2011}
			}

			@incollection{Larson-Prior2013,
			author = {Larson-Prior, Linda J. and Power, Jonathan D. and Vincent, Justin L. and Nolan, Tracy S. and Coalson, Rebecca S. and Zempel, John and Snyder, Abraham Z. and Schlaggar, Bradley L. and Raichle, Marcus E. and Petersen, Steven E.},
			booktitle = {Progress in Brain Research},
			doi = {10.1016/B978-0-444-53839-0.00018-1},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC correlates - EEG or behavior/LarsonPrior2011{\_}sleep.pdf:pdf},
			isbn = {9780444538390},
			keywords = {alpha eeg,brain networks,eeg,fmri,functional connectivity,graph theory,sleep},
			pages = {277--294},
			title = {{Modulation of the brain's functional network architecture in the transition from wake to sleep}},
			url = {http://linkinghub.elsevier.com/retrieve/pii/B9780444538390000181},
			volume = {193},
			year = {2011}
			}

			@Article{Leonardi2013b,
			Title = {Tight Wavelet Frames on Multislice Graphs},
			Author = {Leonardi, Nora and Van De Ville, Dimitri},
			Journal = {IEEE Trans. Signal Process.},
			Year = {2013},
			Month = {Jul.},
			Number = {13},
			Pages = {3357--3367},
			Volume = {61},
			Doi = {10.1109/TSP.2013.2259825},
			File = {:leonardi1302.pdf:PDF},
			Owner = {dvdevill},
			Timestamp = {2013.02.08},
			Url = {/index.php/software/wsgt}
			}

			@article{Leonardi2013,
			abstract = {Functional connectivity (FC) as measured by correlation between fMRI BOLD time courses of distinct brain regions has revealed meaningful organization of spontaneous fluctuations in the resting brain. However, an increasing amount of evidence points to non-stationarity of FC; i.e., FC dynamically changes over time reflecting additional and rich information about brain organization, but representing new challenges for analysis and interpretation. Here, we propose a data-driven approach based on principal component analysis (PCA) to reveal hidden patterns of coherent FC dynamics across multiple subjects. We demonstrate the feasibility and relevance of this new approach by examining the differences in dynamic FC between 13 healthy control subjects and 15 minimally disabled relapse-remitting multiple sclerosis patients. We estimated whole-brain dynamic FC of regionally-averaged BOLD activity using sliding time windows. We then used PCA to identify FC patterns, termed "eigenconnectivities", that reflect meaningful patterns in FC fluctuations. We then assessed the contributions of these patterns to the dynamic FC at any given time point and identified a network of connections centered on the default-mode network with altered contribution in patients. Our results complement traditional stationary analyses, and reveal novel insights into brain connectivity dynamics and their modulation in a neurodegenerative disease. ?? 2013 Elsevier Inc.},
			author = {Leonardi, Nora and Richiardi, Jonas and Gschwind, Markus and Simioni, Samanta and Annoni, Jean-Marie and Schluep, Myriam and Vuilleumier, Patrik and {Van De Ville}, Dimitri},
			doi = {10.1016/j.neuroimage.2013.07.019},
			file = {:Users/gpreti/Documents/Articles/marta{\&}giorgio/dynamicFC/Leonardi2013.pdf:pdf},
			isbn = {1053-8119},
			issn = {10538119},
			journal = {NeuroImage},
			keywords = {Dynamics,FMRI,Functional connectivity,Multiple sclerosis,Resting state},
			month = {dec},
			pages = {937--950},
			pmid = {23872496},
			publisher = {Elsevier Inc.},
			title = {{Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2013.07.019 http://linkinghub.elsevier.com/retrieve/pii/S105381191300774X},
			volume = {83},
			year = {2013}
			}

			@article{Thompson2013b,
			author = {Thompson, Garth John and Merritt, Michael Donelyn and Pan, Wen-ju and Magnuson, Matthew Evan and Grooms, Joshua Koehler and Jaeger, Dieter and Keilholz, Shella Dawn},
			doi = {10.1016/j.neuroimage.2013.07.036},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC correlates - EEG or behavior/Thompson2013{\_}neuralcorr{\_}rat.pdf:pdf},
			issn = {10538119},
			journal = {NeuroImage},
			keywords = {Dynamic,Functional connectivity,Global signal,Neural basis,Sliding window,Time varying,functional connectivity},
			month = {dec},
			pages = {826--836},
			publisher = {Elsevier Inc.},
			title = {{Neural correlates of time-varying functional connectivity in the rat}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2013.07.036 http://linkinghub.elsevier.com/retrieve/pii/S1053811913007921},
			volume = {83},
			year = {2013}
			}

			@article{Liao2014b,
			abstract = {The brain connectome collects the complex network architectures, looking at both static and dynamic functional connectivity. The former normally requires stationary signals and connections. However, the human brain activity and connections are most likely time dependent and dynamic, and related to ongoing rhythmic activity. We developed an open-source MATLAB toolbox DynamicBC with user-friendly graphical user interfaces, implementing both dynamic functional and effective connectivity for tracking brain dynamics from functional MRI. We provided two strategies for dynamic analysis: (1) the commonly utilized sliding-window analysis and (2) the flexible least squares based time-varying parameter regression strategy. The toolbox also implements multiple functional measures including seed-to-voxel analysis, region of interest (ROI)-to-ROI analysis, and voxel-to-voxel analysis. We describe the principles of the implemented algorithms, and then present representative results from simulations and empirical data applications. We believe that this toolbox will help neuroscientists and neurologists to easily map dynamic brain connectomics.},
			author = {Liao, Wei and Wu, Guo-Rong and Xu, Qiang and Ji, Gong-Jun and Zhang, Zhiqiang and Zang, Yu-Feng and Lu, Guangming},
			doi = {10.1089/brain.2014.0253},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/new/Liao2014.pdf:pdf},
			isbn = {2158-0022 (Electronic)},
			issn = {2158-0014},
			journal = {Brain Connectivity},
			keywords = {Brain,Brain: physiology,Computer Simulation,Computer-Assisted,Computer-Assisted: methods,Connectome,Connectome: methods,Humans,Image Processing,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Nerve Net,Nerve Net: physiology,Rest,Software},
			month = {dec},
			number = {10},
			pages = {780--790},
			pmid = {25083734},
			title = {{DynamicBC : A MATLAB Toolbox for Dynamic Brain Connectome Analysis}},
			url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4268585{\&}tool=pmcentrez{\&}rendertype=abstract http://online.liebertpub.com/doi/abs/10.1089/brain.2014.0253},
			volume = {4},
			year = {2014}
			}

			@article{Li2014b,
			abstract = {Functional connectomes (FCs) have been recently shown to be powerful in characterizing brain conditions. However, many previous studies assumed temporal stationarity of FCs, while their temporal dynamics are rarely explored. Here, based on the structural connectomes constructed from diffusion tensor imaging data, FCs are derived from resting-state fMRI (R-fMRI) data and are then temporally divided into quasi-stable segments via a sliding time window approach. After integrating and pooling over a large number of those temporally quasi-stable FC segments from 44 post-traumatic stress disorder (PTSD) patients and 51 healthy controls, common FC (CFC) patterns are derived via effective dictionary learning and sparse coding algorithms. It is found that there are 16 CFC patterns that are reproducible across healthy controls, and interestingly, two additional CFC patterns with altered connectivity patterns [termed signature FC (SFC) here] exist dominantly in PTSD subjects. These two SFC patterns alone can successfully differentiate 80{\%} of PTSD subjects from healthy controls with only 2{\%} false positive. Furthermore, the temporal transition dynamics of CFC patterns in PTSD subjects are substantially different from those in healthy controls. These results have been replicated in separate testing datasets, suggesting that dynamic functional connectomics signatures can effectively characterize and differentiate PTSD patients.},
			archivePrefix = {arXiv},
			arxivId = {NIHMS150003},
			author = {Li, Xiang and Zhu, Dajiang and Jiang, Xi and Jin, Changfeng and Zhang, Xin and Guo, Lei and Zhang, Jing and Hu, Xiaoping and Li, Lingjiang and Liu, Tianming},
			doi = {10.1002/hbm.22290},
			eprint = {NIHMS150003},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC tools applied to neurodegenerative disorders/Post-Traumatic Stress Disorder/Li{\_}et{\_}al-2014-Human{\_}Brain{\_}Mapping copy.pdf:pdf},
			isbn = {1097-0193 (Electronic)$\backslash$n1065-9471 (Linking)},
			issn = {10659471},
			journal = {Human Brain Mapping},
			keywords = {Connectivity,Diffusion tensor imaging,Resting state fMRI},
			month = {apr},
			number = {4},
			pages = {1761--1778},
			pmid = {23671011},
			title = {{Dynamic functional connectomics signatures for characterization and differentiation of PTSD patients}},
			url = {http://doi.wiley.com/10.1002/hbm.22290},
			volume = {35},
			year = {2014}
			}

			@article{Leonardi2014,
			author = {Leonardi, Nora and Shirer, William R and Greicius, Michael D and {Van De Ville}, Dimitri},
			doi = {10.1002/hbm.22599},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/Leonardi2014{\_}HBM.pdf:pdf},
			issn = {10659471},
			journal = {Human Brain Mapping},
			keywords = {dynamic functional connectivity,functional magnetic resonance imaging,matrix factorization,resting state},
			month = {dec},
			number = {12},
			pages = {5984--5995},
			title = {{Disentangling dynamic networks: Separated and joint expressions of functional connectivity patterns in time}},
			url = {http://doi.wiley.com/10.1002/hbm.22599},
			volume = {35},
			year = {2014}
			}

			@article{Hutchison2015,
			abstract = {The transition from childhood to adulthood is marked by pronounced functional and structural brain transformations that impact cognition and behavior. Here, we use a functional imaging approach to reveal dynamic changes in coupling strength between networks and the expression of discrete brain configurations over human development during rest and a cognitive control task. Although the brain's repertoire of functional states was generally preserved across ages, state-specific temporal features, such as the frequency of expression and the amount of time spent in select states, varied by age in ways that were dependent on condition. Increasing age was associated with greater variability of connection strengths across time at rest, while there was a selective inversion of this effect in higher-order networks during implementation of cognitive control. The results suggest that development is characterized by the modi-fication of dynamic coupling to both maximize and constrain functional variability in response to ongoing cognitive and behavioral requirements.},
			author = {Hutchison, R. Matthew and Morton, J. Bruce},
			doi = {10.1523/JNEUROSCI.4638-14.2015},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/new/Hutchinson2015.pdf:pdf},
			issn = {0270-6474},
			journal = {Journal of Neuroscience},
			keywords = {brain states,cognitive control,fmri,neural noise,resting state},
			month = {apr},
			number = {17},
			pages = {6849--6859},
			pmid = {25926460},
			title = {{Tracking the Brain's Functional Coupling Dynamics over Development}},
			url = {http://www.jneurosci.org/cgi/doi/10.1523/JNEUROSCI.4638-14.2015},
			volume = {35},
			year = {2015}
			}

			@inproceedings{Ou2013,
			abstract = {Functional connectivities constructed via resting state fMRI (R-fMRI) data have been widely used to study the brain's functional activities and to characterize the brain's states. However, the temporal dynamic transition patterns of the brain's functional states have been rarely investigated before. In this paper, we present a novel algorithmic framework to cluster and label the brain's functional states, and learn their hidden Markov models (HMMs). Here, the brain's functional state is compactly represented by a large-scale functional connectivity matrix, called functional connectome state (FCS), and the temporal FCS sequences are derived via an overlapping sliding time window approach. The best-matched HMM learned for ADHD patients revealed a meaningful phenomenon of psychiatric conditions, that is, the tendency to enter into, and inability to disengage from, a negative mood state. Experimental results demonstrated 87{\%} of ADHD patients and 89{\%} of normal controls are successfully classified via multiple HMMs by using majority voting.},
			author = {Ou, Jinli and Xie, Li and Wang, Peng and Li, Xiang and Zhu, Dajiang and Jiang, Rongxin and Wang, Yufeng and Chen, Yaowu and Zhang, Jing and Liu, Tianming},
			booktitle = {2013 6th International IEEE/EMBS Conference on Neural Engineering (NER)},
			doi = {10.1109/NER.2013.6695998},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/new/Ou2013.pdf:pdf},
			isbn = {978-1-4673-1969-0},
			issn = {1948-3546},
			keywords = {Brain models,Clustering algorithms,Computational modeling,Educational institutions,HMM,Hidden Markov models,Markov processes,biomedical MRI,brain,brain functional activities,brain functional dynamics modeling,diseases,functional connectivities,functional connectome state,hidden Markov models,large-scale functional connectivity matrix,medical disorders,medical signal processing,negative mood state,neurophysiology,novel algorithmic framework,overlapping sliding time window approach,psychiatric conditions,psychology,resting state fMRI data,temporal FCS sequences,temporal dynamic transition patterns},
			month = {nov},
			number = {c},
			pages = {569--572},
			publisher = {IEEE},
			title = {{Modeling brain functional dynamics via hidden Markov models}},
			url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6695998},
			year = {2013}
			}

			@inproceedings{Kunegis2010,
			Author = {Kunegis, J{\'e}r{\^o}me and Schmidt, Stephan and Lommatzsch, Andreas and Lerner, J{\"u}rgen and De Luca, Ernesto W and Albayrak, Sahin},
			Booktitle = {SIAM Int. Conf. Data Min.},
			Date-Added = {2017-09-17 23:16:19 +0000},
			Date-Modified = {2017-09-17 23:17:14 +0000},
			Month = {Apr.},
			Pages = {559--570},
			Title = {Spectral analysis of signed graphs for clustering, prediction and visualization},
			Year = {2010}}

			@article{Mhaskar2017,
			author = {Mhaskar, H. N.},
			title = {A unified framework for harmonic analysis of functions on directed graphs and changing data},
			journal = {Appl. Comput. Harmon. Anal.},
			year = {in press}
			}

			@article{Fatima2016,
			author = {Fatima, Zainab and Kovacevic, Natasha and Misic, Bratislav and McIntosh, Anthony Randal},
			doi = {10.1002/hbm.23285},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC correlates - EEG or behavior/Fatima{\_}et{\_}al-2016-Human{\_}Brain{\_}Mapping.pdf:pdf},
			issn = {10659471},
			journal = {Human Brain Mapping},
			keywords = {conditional associative learning,human learning curve,least squares,magnetoencephalography,neuropsychological assessment,partial,principal component analysis},
			month = {jun},
			title = {{Dynamic functional connectivity shapes individual differences in associative learning}},
			url = {http://doi.wiley.com/10.1002/hbm.23285},
			year = {2016}
			}

			@inproceedings{Ma2016,
			Author = {Ma, Jeremy and Huang, Weiyu and Segarra, Santiago and Ribeiro, Alejandro},
			Booktitle = {IEEE Int. Conf. Acoust., Speech, Signal Process.},
			Date-Added = {2017-09-17 23:14:20 +0000},
			Date-Modified = {2017-09-17 23:15:22 +0000},
			Month = {Mar.},
			Pages = {4563--4567},
			Title = {Diffusion filtering of graph signals and its use in recommendation systems},
			Year = {2016}}

			@article{Preti2019,
			title={Decoupling of brain function from structure reveals regional behavioral specialization in humans},
			author={Preti, Maria Giulia and Van De Ville, Dimitri},
			journal={Nature communications},
			volume={10},
			number={1},
			pages={1--7},
			year={2019},
			publisher={Nature Publishing Group}
			}

			@article{Madhyastha2015,
			abstract = {Consistent spatial patterns of coherent activity, representing large-scale networks, have been reliably identified in multiple populations. Most often, these studies have examined "stationary" connectivity. However, there is a growing recognition that there is a wealth of information in the time-varying dynamics of networks which has neural underpinnings, which changes with age and disease and that supports behavior. Using factor analysis of overlapping sliding windows across 25 participants with Parkinson disease (PD) and 21 controls (ages 41-86), we identify factors describing the covarying correlations of regions (dynamic connectivity) within attention networks and the default mode network, during two baseline resting-state and task runs. Cortical regions that support attention networks are affected early in PD, motivating the potential utility of dynamic connectivity as a sensitive way to characterize physiological disruption to these networks. We show that measures of dynamic connectivity are more reliable than comparable measures of stationary connectivity. Factors in the dorsal attention network (DAN) and fronto-parietal task control network, obtained at rest, are consistently related to the alerting and orienting reaction time effects in the subsequent Attention Network Task. In addition, the same relationship between the same DAN factor and the alerting effect was present during tasks. Although reliable, dynamic connectivity was not invariant, and changes between factor scores across sessions were related to changes in accuracy. In summary, patterns of time-varying correlations among nodes in an intrinsic network have a stability that has functional relevance.},
			author = {Madhyastha, Tara M and Askren, Mary K and Boord, Peter and Grabowski, Thomas J},
			doi = {10.1089/brain.2014.0248},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC correlates - EEG or behavior/Madhyastha2015{\_}predictiontaskperformance.pdf:pdf},
			isbn = {1206685670},
			issn = {2158-0014},
			journal = {Brain Connectivity},
			keywords = {attention network task,dynamic functional connectivity,ity,parkinson disease,resting-state connectiv-,task connectivity},
			month = {feb},
			number = {1},
			pages = {45--59},
			pmid = {25014419},
			title = {{Dynamic Connectivity at Rest Predicts Attention Task Performance}},
			url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4313397{\&}tool=pmcentrez{\&}rendertype=abstract http://online.liebertpub.com/doi/abs/10.1089/brain.2014.0248},
			volume = {5},
			year = {2015}
			}

			@article{Kang2011,
			author = {Kang, Jin and Wang, Liang and Yan, Chaogan and Wang, Jinhui and Liang, Xia and He, Yong},
			doi = {10.1016/j.neuroimage.2011.03.033},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/Kang2011.pdf:pdf},
			issn = {10538119},
			journal = {NeuroImage},
			keywords = {Connectivity,Default mode,Dynamics,Networks,Resting-state fMRI,Spontaneous brain activity},
			month = {jun},
			number = {3},
			pages = {1222--1234},
			publisher = {Elsevier Inc.},
			title = {{Characterizing dynamic functional connectivity in the resting brain using variable parameter regression and Kalman filtering approaches}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2011.03.033 http://linkinghub.elsevier.com/retrieve/pii/S1053811911003090},
			volume = {56},
			year = {2011}
			}

			@article{Monti2014,
			abstract = {At the forefront of neuroimaging is the understanding of the functional architecture of the human brain. In most applications functional networks are assumed to be stationary, resulting in a single network estimated for the entire time course. However recent results suggest that the connectivity between brain regions is highly non-stationary even at rest. As a result, there is a need for new brain imaging methodologies that comprehensively account for the dynamic nature of functional networks. In this work we propose the Smooth Incremental Graphical Lasso Estimation (SINGLE) algorithm which estimates dynamic brain networks from fMRI data. We apply the proposed algorithm to functional MRI data from 24 healthy patients performing a Choice Reaction Task to demonstrate the dynamic changes in network structure that accompany a simple but attentionally demanding cognitive task. Using graph theoretic measures we show that the properties of the Right Inferior Frontal Gyrus and the Right Inferior Parietal lobe dynamically change with the task. These regions are frequently reported as playing an important role in cognitive control. Our results suggest that both these regions play a key role in the attention and executive function during cognitively demanding tasks and may be fundamental in regulating the balance between other brain regions.},
			archivePrefix = {arXiv},
			arxivId = {1310.3863},
			author = {Monti, Ricardo Pio and Hellyer, Peter and Sharp, David and Leech, Robert and Anagnostopoulos, Christoforos and Montana, Giovanni},
			doi = {10.1016/j.neuroimage.2014.07.033},
			eprint = {1310.3863},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/new/Monti2014.pdf:pdf},
			isbn = {1095-9572 (Electronic) 1053-8119 (Linking)},
			issn = {10538119},
			journal = {NeuroImage},
			month = {dec},
			pages = {427--443},
			pmid = {25107854},
			publisher = {Elsevier B.V.},
			title = {{Estimating time-varying brain connectivity networks from functional MRI time series}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2014.07.033 http://linkinghub.elsevier.com/retrieve/pii/S1053811914006168},
			volume = {103},
			year = {2014}
			}

			@article{Liegeois2015,
			author = {Li{\'{e}}geois, Rapha{\"{e}}l and Ziegler, Erik and Phillips, Christophe and Geurts, Pierre and G{\'{o}}mez, Francisco and Bahri, Mohamed Ali and Yeo, B. T. Thomas and Soddu, Andrea and Vanhaudenhuyse, Audrey and Laureys, Steven and Sepulchre, Rodolphe},
			doi = {10.1007/s00429-015-1083-y},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC pipelines for brain state disentanglement/Liegois2015{\_}Cerebral functional connectivity periodically (de)synchronizes with anatomical constraints.pdf:pdf},
			issn = {1863-2653},
			journal = {Brain Structure and Function},
			keywords = {consciousness,dwi,dynamics,fmri,functional connectivity,multimodal imaging,spontaneous activity,structural connectivity,windowing},
			month = {jul},
			number = {6},
			pages = {2985--2997},
			title = {{Cerebral functional connectivity periodically (de)synchronizes with anatomical constraints}},
			url = {http://link.springer.com/10.1007/s00429-015-1083-y},
			volume = {221},
			year = {2016}
			}

			@article{Chen2016b,
			abstract = {Brain functional connectivity (FC) network, estimated with resting-state functional magnetic resonance imaging (RS-fMRI) technique, has emerged as a promising approach for accurate diagnosis of neurodegenerative diseases. However, the conventional FC network is essentially low-order in the sense that only the correlations among brain regions (in terms of RS-fMRI time series) are taken into account. The features derived from this type of brain network may fail to serve as an effective disease biomarker. To overcome this drawback, we propose extraction of novel high-order FC correlations that characterize how the low-order correlations between different pairs of brain regions interact with each other. Specifically, for each brain region, a sliding window approach is first performed over the entire RS-fMRI time series to generate multiple short overlapping segments. For each segment, a low-order FC network is constructed, measuring the short-term correlation between brain regions. These low-order networks (obtained from all segments) describe the dynamics of short-term FC along the time, thus also forming the correlation time series for every pair of brain regions. To overcome the curse of dimensionality, we further group the correlation time series into a small number of different clusters according to their intrinsic common patterns. Then, the correlation between the respective mean correlation time series of different clusters is calculated to represent the high-order correlation among different pairs of brain regions. Finally, we design a pattern classifier, by combining features of both low-order and high-order FC networks. Experimental results verify the effectiveness of the high-order FC network on disease diagnosis. Hum Brain Mapp, 2016. {\textcopyright} 2016 Wiley Periodicals, Inc.},
			author = {Chen, Xiaobo and Zhang, Han and Gao, Yue and Wee, Chong-Yaw and Li, Gang and Shen, Dinggang},
			doi = {10.1002/hbm.23240},
			file = {:Users/gpreti/Documents/Articles/dFC{\_}Review{\_}Complete/dFC tools applied to neurodegenerative disorders/Mild Cognitive Impairment (MCI)/Chen2016{\_}MCI.pdf:pdf},
			issn = {10659471},
			journal = {Human Brain Mapping},
			keywords = {brain disease diagnosis,functional connectivity,low-order and high-order networks,mild cognitive impairment},
			month = {sep},
			number = {9},
			pages = {3282--3296},
			pmid = {27144538},
			title = {{High-order resting-state functional connectivity network for MCI classification}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/27144538 http://doi.wiley.com/10.1002/hbm.23240},
			volume = {37},
			year = {2016}
			}

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			organization={IEEE}
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			year={2009},
			publisher={Wiley Online Library}
			}

			@article{Honey2009,
			title={Predicting human resting-state functional connectivity from structural connectivity},
			author={Honey, CJ and Sporns, O and Cammoun, Leila and Gigandet, Xavier and Thiran, Jean-Philippe and Meuli, Reto and Hagmann, Patric},
			journal={Proceedings of the National Academy of Sciences},
			volume={106},
			number={6},
			pages={2035--2040},
			year={2009},
			publisher={National Acad Sciences}
			}

			@article{Plis2018,
			title={Reading the (functional) writing on the (structural) wall: {M}ultimodal fusion of brain structure and function via a deep neural network based translation approach reveals novel impairments in schizophrenia},
			author={Plis, Sergey M and Amin, Md Faijul and Chekroud, Adam and Hjelm, Devon and Damaraju, Eswar and Lee, Hyo Jong and Bustillo, Juan R and Cho, KyungHyun and Pearlson, Godfrey D and Calhoun, Vince D},
			journal={NeuroImage},
			volume={181},
			pages={734--747},
			year={2018},
			publisher={Elsevier}
			}

			@article{Medaglia2018,
			title={Functional alignment with anatomical networks is associated with cognitive flexibility},
			author={Medaglia, John D and Huang, Weiyu and Karuza, Elisabeth A and Kelkar, Apoorva and Thompson-Schill, Sharon L and Ribeiro, Alejandro and Bassett, Danielle S},
			journal={Nature Human Behaviour},
			volume={2},
			number={2},
			pages={156},
			year={2018},
			publisher={Nature Publishing Group}
			}

			@article{Greicius2009,
			title={Resting-state functional connectivity reflects structural connectivity in the default mode network},
			author={Greicius, Michael D and Supekar, Kaustubh and Menon, Vinod and Dougherty, Robert F},
			journal={Cerebral cortex},
			volume={19},
			number={1},
			pages={72--78},
			year={2009},
			publisher={Oxford University Press}
			}

			@article{Golestani2016,
			title={The association between cerebrovascular reactivity and resting-state f{MRI} functional connectivity in healthy adults: {T}he influence of basal carbon dioxide},
			author={Golestani, Ali M and Kwinta, Jonathan B and Strother, Stephen C and Khatamian, Yasha B and Chen, J Jean},
			journal={Neuroimage},
			volume={132},
			pages={301--313},
			year={2016},
			publisher={Elsevier}
			}

			@article{Robinson2016,
			author= {Robinson, P. A. and Zhao, X. and Aquino, K. M. and Griffiths, J. and Sarkar, S. and Mehta-Pandejee, G.},
			title = {Eigenmodes of brain activity: Neural field theory predictions and comparison with experiment},
			journal = {Neuroimage},
			year = {2016},
			volume = {142},
			month = {Nov.},
			pages = {79--98}
			}

			@article{Shi2000,
			title={Normalized cuts and image segmentation},
			author={Shi, Jianbo and Malik, Jitendra},
			journal={IEEE Trans. Pattern Anal. Mach. Intell.},
			volume={22},
			number={8},
			pages={888--905},
			month = {Aug.},
			year={2000},
			publisher={Ieee}
			}

			@article{Newman2013,
			title={Spectral methods for community detection and graph partitioning},
			author={Newman, Mark EJ},
			journal={Phys. Rev. E},
			volume={88},
			number={4},
			pages={042822},
			month = {Oct.},
			year={2013},
			publisher={APS}
			}

			@article{Liegeois2016,
			title={Cerebral functional connectivity periodically (de) synchronizes with anatomical constraints},
			author={Li{\'e}geois, Rapha{\"e}l and Ziegler, Erik and Phillips, Christophe and Geurts, Pierre and G{\'o}mez, Francisco and Bahri, Mohamed Ali and Yeo, BT Thomas and Soddu, Andrea and Vanhaudenhuyse, Audrey and Laureys, Steven and others},
			journal={Brain Struct. Funct.},
			volume={221},
			number={6},
			pages={2985--2997},
			month = {Jul.},
			year={2016},
			publisher={Springer}
			}

			@article{Atasoy2016,
			author = {Atasoy, S. and Donnelly, I. and Pearson, J.},
			title = {Human brain networks function in connectome-specific harmonic waves},
			journal = {Nat. Commun.},
			pages = {10340},
			month = {Jan.},
			year = {2016},
			volume = {7}
			}

			@article{Raj2012,
			author = {Raj, A. and Kuceyeski, A. and Weiner, M.},
			title = {A network diffusion model of disease progression in dementia},
			journal = {Neuron},
			year = {2012},
			month = {Mar.},
			number = {6},
			pages = {1204--1215},
			volume = {73}
			}

			@article{Abdelnour2014,
			author = {Abdelnour, F. and Voss, H. U. and Raj, A.},
			title = {Network diffusion accurately models the relationship between structural and functional brain connectivity networks},
			journal = {Neuroimage},
			year = {2014},
			month = {Apr.},
			volume = {90},
			pages = {335--347}
			}

			@article{Bressler2010,
			author = {Bressler, Steven L and Menon, Vinod},
			title = {Large-scale brain networks in cognition: emerging methods and principles},
			journal = {Trends in cognitive sciences},
			year = {2010},
			volume = {14},
			number = {6},
			pages = {277--290},
			month = jun
			}

			@article{Smith2000,
			title={{BET}: brain extraction tool},
			author={Smith, Stephen M},
			journal={FMRIB TR00SMS2b, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain), Department of Clinical Neurology, Oxford University, John Radcliffe Hospital, Headington, UK},
			year={2000}
			}

			@article{Greve2009,
			Author = {Greve, Douglas N and Fischl, Bruce},
			Date-Added = {2017-07-05 00:47:45 +0000},
			Date-Modified = {2017-07-05 00:47:58 +0000},
			Journal = {Neuroimage},
			Month = {Oct.},
			Number = {1},
			Pages = {63--72},
			Title = {Accurate and robust brain image alignment using boundary-based registration},
			Volume = {48},
			Year = {2009}}

			@article{Bassett2017,
			author = {Bassett, Danielle S and Mattar, Marcelo G},
			title = {A Network Neuroscience of Human Learning: Potential to Inform Quantitative Theories of Brain and Behavior},
			journal = {Trends Cogn. Sci. (Regul. Ed.)},
			year = {2017},
			volume = {21},
			number = {4},
			pages = {250--264},
			month = {Apr.}
			}

			@article{Puckett2016,
			author = {Puckett, Alexander M and Aquino, Kevin M and Robinson, P A and Breakspear, Michael and Schira, Mark M},
			title = {The spatiotemporal hemodynamic response function for depth-dependent functional imaging of human cortex},
			journal = {Neuroimage},
			year = {2016},
			volume = {139},
			pages = {240--248},
			month = oct
			}

			@article{Pang2017,
			author = {Pang, J C and Robinson, P A and Aquino, K M and Vasan, N},
			title = {Effects of astrocytic dynamics on spatiotemporal hemodynamics: Modeling and enhanced data analysis},
			journal = {Neuroimage},
			year = {2017},
			volume = {147},
			pages = {994--1005},
			month = feb
			}

			@article{Nichols2017,
			author = {Nichols, Thomas E and Das, Samir and Eickhoff, Simon B and Evans, Alan C and Glatard, Tristan and Hanke, Michael and Kriegeskorte, Nikolaus and Milham, Michael P and Poldrack, Russell A and Poline, Jean-Baptiste and Proal, Erika and Thirion, Bertrand and Van Essen, David C and White, Tonya and Yeo, B T Thomas},
			title = {Best practices in data analysis and sharing in neuroimaging using {MRI}},
			journal = {Nature Neuroscience},
			volume = {20},
			number = {3},
			pages = {299--303},
			year={2017}
			}

			@article{Choi2013,
			author = {Choi, H. and Fermin, D. and Nesvizhskii, A. I. and Ghosh, D. and Qin, Z. S.},
			title = {Sparsely correlated hidden {M}arkov models with application to genomewide location studies},
			journal = {Bioinformatics},
			volume = {29},
			number = {5},
			pages = {533--541},
			year = {2013}
			}

			@article{Cocchi2013,
			author = {Cocchi, Luca and Zalesky, Andrew and Fornito, Alex and Mattingley, Jason B},
			title = {Dynamic cooperation and competition between brain systems during cognitive control},
			journal = {Trends in Cognitive Sciences},
			year = {2013},
			volume = {17},
			number = {10},
			pages = {493--501},
			month = oct
			}

			@article{Betzel2016b,
			author = {Betzel, Richard F and Avena-Koenigsberger, Andrea and Go{\~n}i, Joaqu{\'\i}n and He, Ye and de Reus, Marcel A and Griffa, Alessandra and V{\'e}rtes, Petra E and Mi{\v s}i{\'c}, Bratislav and Thiran, Jean-Philippe and Hagmann, Patric and van den Heuvel, Martijn and Zuo, Xi-Nian and Bullmore, Edward T and Sporns, Olaf},
			title = {Generative models of the human connectome},
			journal = {NeuroImage},
			year = {2016},
			volume = {124},
			number = {Part A},
			pages = {1054--1064},
			month = jan
			}

			@article{Bassett2017b,
			author = {Bassett, Danielle S and Sporns, Olaf},
			title = {Network neuroscience},
			journal = {Nat. Neurosci.},
			year = {2017},
			volume = {20},
			number = {3},
			pages = {353--364},
			month = {Feb.}
			}

			@article{Ryali2016,
			author = {Ryali, S. and Supekar, K. and Chen, T. and Kochalka, J. and Cai, W. and Nicholas, J. and Padmanabhan, A. and Menon, V.},
			title = {Temporal dynamics and developmental maturation of salience, default and central-executive network interactions revealed by variational {B}ayes hidden {M}arkov modeling},
			journal = {PLOS Computational Biology},
			volume = {12},
			number = {12},
			pages = {e1005138},
			year = {2016}
			}

			@article {Chen2017,
			author = {Chen, Jingyuan E. and Glover, Gary H. and Greicius, Michael D. and Chang, Catie},
			title = {Dissociated patterns of anti-correlations with dorsal and ventral default-mode networks at rest},
			journal = {Human Brain Mapping},
			volume = {38},
			number = {5},
			issn = {1097-0193},
			url = {http://dx.doi.org/10.1002/hbm.23532},
			doi = {10.1002/hbm.23532},
			pages = {2454--2465},
			keywords = {DMN, anti-correlation, seed-dependence, resting state},
			year = {2017},
			}

			@article{deLacy2017,
			author = {de Lacy, N and Doherty, D and King, B H and Rachakonda, S and Calhoun, V D},
			title = {Disruption to control network function correlates with altered dynamic connectivity in the wider autism spectrum},
			journal = {NeuroImage: Clinical},
			year = {2017},
			volume = {15},
			pages = {513--524}
			}

			@article{Chen2016d,
			author = {Chen, S. and Langely, J. and Chen, X. and Hu, X.},
			title = {Spatiotemporal modeling of brain dynamics using resting-state functional magnetic resonance imaging with {G}aussian hidden {M}arkov model},
			journal = {Brain Topography},
			volume = {6},
			number ={4},
			year = {2016}
			}

			@article{Koshino2014,
			author={ Koshino, Hideya and Minamoto, Takehiro and Yaoi, Ken and Osaka, Mariko and Osaka, Naoyuki},
			title={Coactivation of the Default Mode Network regions and Working Memory Network regions during task preparation},
			journal={Nature Scientific Reports},
			year={2014},
			volume={4},
			number={5954},
			pages={1-8}
			}

			@article{Menon1999,
			author = {Menon, R and Kim, S},
			title = {Spatial and temporal limits in cognitive neuroimaging with {fMRI}},
			journal = {Trends in cognitive sciences},
			year = {1999},
			volume = {3},
			number = {6},
			pages = {207--216},
			month = jun
			}

			@article{Formisano2003,
			author = {Formisano, Elia and Goebel, Rainer},
			title = {Tracking cognitive processes with functional {MRI} mental chronometry},
			journal = {Current opinion in neurobiology},
			year = {2003},
			volume = {13},
			number = {2},
			pages = {174--181},
			month = apr
			}

			@article{VanDenheuvel2012,
			title={High-cost, high-capacity backbone for global brain communication},
			author={van den Heuvel, Martijn P and Kahn, Ren{\'e} S and Go{\~n}i, Joaqu{\'\i}n and Sporns, Olaf},
			journal={Proceedings of the National Academy of Sciences},
			volume={109},
			number={28},
			pages={11372--11377},
			year={2012},
			publisher={National Acad Sciences}
			}

			@article{Fornito2015,
			title={The connectomics of brain disorders},
			author={Fornito, Alex and Zalesky, Andrew and Breakspear, Michael},
			journal={Nature Reviews Neuroscience},
			volume={16},
			number={3},
			pages={159},
			year={2015},
			publisher={Nature Publishing Group}
			}

			@article{Crossley2014,
			title={The hubs of the human connectome are generally implicated in the anatomy of brain disorders},
			author={Crossley, Nicolas A and Mechelli, Andrea and Scott, Jessica and Carletti, Francesco and Fox, Peter T and McGuire, Philip and Bullmore, Edward T},
			journal={Brain},
			volume={137},
			number={8},
			pages={2382--2395},
			year={2014},
			publisher={Oxford University Press}
			}

			@article{DeLange2019,
			title={Shared vulnerability for connectome alterations across psychiatric and neurological brain disorders},
			author={de Lange, Siemon C and Scholtens, Lianne H and van den Berg, Leonard H and Boks, Marco P and Bozzali, Marco and Cahn, Wiepke and Dannlowski, Udo and Durston, Sarah and Geuze, Elbert and van Haren, Neeltje EM and others},
			journal={Nature human behaviour},
			pages={1--11},
			year={2019},
			publisher={Nature Publishing Group}
			}

			@article{SoleCasal2019,
			title={Structural brain network of gifted children has a more integrated and versatile topology},
			author={Sol{\'e}-Casals, Jordi and Serra-Grabulosa, Josep M and Romero-Garcia, Rafael and Vilaseca, Gemma and Adan, Ana and Vilar{\'o}, N{\'u}ria and Bargall{\'o}, N{\'u}ria and Bullmore, Edward T},
			journal={Brain Structure and Function},
			pages={1--11},
			year={2019},
			publisher={Springer}
			}

			@article{Fornito2018,
			title={Bridging the gap between connectome and transcriptome},
			author={Fornito, Alex and Arnatkevi{\v{c}}i{\=u}t{\.e}, Aurina and Fulcher, Ben D},
			journal={Trends in cognitive sciences},
			year={2018},
			publisher={Elsevier}
			}

			@article{Fulcher2016,
			title={A transcriptional signature of hub connectivity in the mouse connectome},
			author={Fulcher, Ben D and Fornito, Alex},
			journal={Proceedings of the National Academy of Sciences},
			volume={113},
			number={5},
			pages={1435--1440},
			year={2016},
			publisher={National Acad Sciences}
			}

			@article{VanDenHeuvel2009b,
			title={Efficiency of functional brain networks and intellectual performance},
			author={van den Heuvel, Martijn P and Stam, Cornelis J and Kahn, Ren{\'e} S and Pol, Hilleke E Hulshoff},
			journal={J. Neurosci.},
			volume={29},
			number={23},
			pages={7619--7624},
			month = {Jun.},
			year={2009},
			publisher={Soc Neuroscience}
			}

			@book{Haken2013,
			Author = {Haken, Hermann},
			Date-Added = {2017-09-16 17:09:43 +0000},
			Date-Modified = {2017-09-16 17:10:01 +0000},
			Publisher = {Springer Science \& Business Media},
			Title = {Principles of Brain Functioning: A synergetic Approach To Brain activity, Behavior and Cognition},
			Volume = {67},
			Year = {2013}}


			@article{He2010,
			title = "The Temporal Structures and Functional Significance of Scale-free Brain Activity ",
			journal = "Neuron ",
			volume = "66",
			number = "3",
			pages = "353-369",
			year = "2010",
			note = "",
			issn = "0896-6273",
			doi = "http://dx.doi.org/10.1016/j.neuron.2010.04.020",
			url = "http://www.sciencedirect.com/science/article/pii/S0896627310002916",
			author = "Biyu J. He and John M. Zempel and Abraham Z. Snyder and Marcus E. Raichle",
			keywords = "\{SYSNEURO\}",
			keywords = "\{SYSBIO\}",
			keywords = "\{SIGNALING\} "
			}

			@Article{Britz2010,
			Title = {{BOLD} Correlates of {EEG} Topography Reveal Rapid Resting-State Network Dynamics},
			Author = {Britz, J. and Van De Ville, D. and Michel, C. M.},
			Journal = {NeuroImage},
			Year = {2010},
			Number = {4},
			Pages = {1162--1170},
			Volume = {52},
			File = {:britz1001.pdf:PDF},
			Owner = {dvdevill},
			Timestamp = {2010.02.18}
			}

			@Article{VanDeVille2010,
			Title = {{EEG} Microstate Sequences in Healthy Humans at Rest Reveal Scale-Free Dynamics},
			Author = {Van De Ville, D. and Britz, J. and Michel, C. M.},
			Journal = {Proceedings of the National Academy of Sciences of the USA},
			Year = {2010},
			Month = {October},
			Number = {42},
			Pages = {18179--18184},
			Volume = {107},
			File = {:vandeville1003.php:PDF},
			Keywords = {CIBM-SPC},
			Owner = {dvdevill},
			Timestamp = {2010.02.18}
			}

			@article{Buckner2013,
			author={Buckner, Randy L and Krienen, Fenna M and Yeo, B T Thomas},
			title={Opportunities and limitations of intrinsic functional connectivity MRI},
			journal = {Nat Neurosci},
			volume={16},
			issue={7},
			pages={ 832--837}
			}

			@article{Uhlhaas2012,
			title = "Neuronal Dynamics and Neuropsychiatric Disorders: Toward a Translational Paradigm for Dysfunctional Large-Scale Networks ",
			journal = "Neuron ",
			volume = "75",
			number = "6",
			pages = "963-980",
			year = "2012",
			note = "",
			issn = "0896-6273",
			doi = "http://dx.doi.org/10.1016/j.neuron.2012.09.004",
			url = "http://www.sciencedirect.com/science/article/pii/S0896627312008112",
			author = "PeterÂ J. Uhlhaas and Wolf Singer"
			}

			@article{Engel2013,
			title = "Intrinsic Coupling Modes: Multiscale Interactions in Ongoing Brain Activity ",
			journal = "Neuron",
			volume = "80",
			number = "4",
			pages = "867-886",
			year = "2013",
			note = "",
			issn = "0896-6273",
			doi = "http://dx.doi.org/10.1016/j.neuron.2013.09.038",
			url = "http://www.sciencedirect.com/science/article/pii/S0896627313008696",
			author = "Andreas K. Engel and Christian Gerloff and Claus C. Hilgetag and Guido Nolte"
			}

			@article{DePasquale2012,
			title = "A Cortical Core for Dynamic Integration of Functional Networks in the Resting Human Brain ",
			author = "Francesco de Pasquale and Stefania Della Penna and Abraham Z. Snyder and Laura Marzetti and Vittorio Pizzella and Gian Luca Romani and Maurizio Corbetta",
			journal = "Neuron ",
			volume = "74",
			number = "4",
			pages = "753-764",
			year = "2012",
			issn = "0896-6273",
			doi = "http://dx.doi.org/10.1016/j.neuron.2012.03.031",
			url = "http://www.sciencedirect.com/science/article/pii/S089662731200342X"
			}

			@Article{McKeown1998,
			Title = {Analysis of {fMRI} data by blind separation into independent spatial components},
			Author = {McKeown, M. and Makeig, S. and Brown, G. and Jung, T. and Kindermann, S. and Bell, A. and Sejnowski, T.},
			Journal = {Human Brain Mapping},
			Year = {1998},
			Pages = {160--188},
			Volume = {6},

			Owner = {dvdevill},
			Timestamp = {2007.11.08}
			}

			@article{Horovitz2009,
			author = {Horovitz, Silvina G. and Braun, Allen R. and Carr, Walter S. and Picchioni, Dante and Balkin, Thomas J. and Fukunaga, Masaki and Duyn, Jeff H.},
			title = {Decoupling of the brain's default mode network during deep sleep},
			volume = {106},
			number = {27},
			pages = {11376-11381},
			year = {2009},
			doi = {10.1073/pnas.0901435106},
			abstract ={The recent discovery of a circuit of brain regions that is highly active in the absence of overt behavior has led to a quest for revealing the possible function of this so-called default-mode network (DMN). A very recent study, finding similarities in awake humans and anesthetized primates, has suggested that DMN activity might not simply reflect ongoing conscious mentation but rather a more general form of network dynamics typical of complex systems. Here, by performing functional MRI in humans, it is shown that a natural, sleep-induced reduction of consciousness is reflected in altered correlation between DMN network components, most notably a reduced involvement of frontal cortex. This suggests that DMN may play an important role in the sustenance of conscious awareness.},
			URL = {http://www.pnas.org/content/106/27/11376.abstract},
			eprint = {http://www.pnas.org/content/106/27/11376.full.pdf+html},
			journal = {Proceedings of the National Academy of Sciences}
			}

			@article{Liu2011,
			author = {Liu, Xiao and Zhu, Xiao-Hong and Zhang, Yi and Chen, Wei},
			title = {Neural Origin of Spontaneous Hemodynamic Fluctuations in Rats under Burst--Suppression Anesthesia Condition},
			volume = {21},
			number = {2},
			pages = {374-384},
			year = {2011},
			doi = {10.1093/cercor/bhq105},
			abstract ={Spontaneous hemodynamic signals fluctuate coherently within many resting-brain functional networks not only in awake humans and lightly anesthetized primates but also in animals under deep anesthesia characterized by burstÃÂsuppression electroencephalogram (EEG) activity and unconsciousness. To understand the neural origin of spontaneous hemodynamic fluctuations under such a deep anesthesia state, epidural EEG and cerebral blood flow (CBF) were simultaneously recorded from the bilateral somatosensory cortical regions of rats with isoflurane-induced burstÃÂsuppression EEG activity. Strong neurovascular coupling was observed between spontaneous EEG ÃÂburstsÃÂ and CBF ÃÂbumps,ÃÂ both of which were also highly synchronized across the 2 hemispheres. Functional magnetic resonance imaging (fMRI) was used to image spontaneous blood oxygen levelÃÂdependent (BOLD) signals under the same anesthesia conditions and showed similar BOLD ÃÂbumpsÃÂ and dependence on anesthesia depth as the CBF signals. The spatiotemporal BOLD correlations indicate a strong but less-specific coherent network covering a wide range of cortical regions. The overall findings reveal that the spontaneous CBF/BOLD fluctuations under unconscious burstÃÂsuppression anesthesia conditions originate mainly from underlying neural activity. They provide insights into the neurophysiological basis for the use of BOLD- and CBF-based fMRI signals for noninvasively imaging spontaneous and synchronous brain activity under various brain states.},
			URL = {http://cercor.oxfordjournals.org/content/21/2/374.abstract},
			eprint = {http://cercor.oxfordjournals.org/content/21/2/374.full.pdf+html},
			journal = {Cerebral Cortex}
			}

			@article{Greicius2004,
			author = {Greicius, Michael D. and Srivastava, Gaurav and Reiss, Allan L. and Menon, Vinod},
			title = {Default-mode network activity distinguishes {Alzheimer's} disease from healthy aging: Evidence from functional {MRI}},
			volume = {101},
			number = {13},
			pages = {4637-4642},
			year = {2004},
			doi = {10.1073/pnas.0308627101},
			abstract ={Recent functional imaging studies have revealed coactivation in a distributed network of cortical regions that characterizes the resting state, or default mode, of the human brain. Among the brain regions implicated in this network, several, including the posterior cingulate cortex and inferior parietal lobes, have also shown decreased metabolism early in the course of Alzheimer's disease (AD). We reasoned that default-mode network activity might therefore be abnormal in AD. To test this hypothesis, we used independent component analysis to isolate the network in a group of 13 subjects with mild AD and in a group of 13 age-matched elderly controls as they performed a simple sensory-motor processing task. Three important findings are reported. Prominent coactivation of the hippocampus, detected in all groups, suggests that the default-mode network is closely involved with episodic memory processing. The AD group showed decreased resting-state activity in the posterior cingulate and hippocampus, suggesting that disrupted connectivity between these two regions accounts for the posterior cingulate hypometabolism commonly detected in positron emission tomography studies of early AD. Finally, a goodness-of-fit analysis applied at the individual subject level suggests that activity in the default-mode network may ultimately prove a sensitive and specific biomarker for incipient AD.},
			URL = {http://www.pnas.org/content/101/13/4637.abstract},
			eprint = {http://www.pnas.org/content/101/13/4637.full.pdf+html},
			journal = {Proceedings of the National Academy of Sciences of the United States of America}
			}

			@article{Khambhati2017,
			title = "Modelling and Interpreting Mesoscale Network Dynamics ",
			journal = "NeuroImage ",
			year = "in press",
			issn = "1053-8119",
			doi = "https://doi.org/10.1016/j.neuroimage.2017.06.029",
			url = "http://www.sciencedirect.com/science/article/pii/S1053811917305001",
			author = "Khambhati, Ankit N. and Sizemore, Ann E. and Betzel, Richard F. and Bassett, Danielle S."
			}

			@article {Boly2009,
			author = {Boly, M. and Tshibanda, L. and Vanhaudenhuyse, A. and Noirhomme, Q. and Schnakers, C. and Ledoux, D. and Boveroux, P. and Garweg, C. and Lambermont, B. and Phillips, C. and Luxen, A. and Moonen, G. and Bassetti, C. and Maquet, P. and Laureys, S.},
			title = {Functional connectivity in the default network during resting state is preserved in a vegetative but not in a brain dead patient},
			journal = {Human Brain Mapping},
			volume = {30},
			number = {8},
			publisher = {Wiley Subscription Services, Inc., A Wiley Company},
			issn = {1097-0193},
			url = {http://dx.doi.org/10.1002/hbm.20672},
			doi = {10.1002/hbm.20672},
			pages = {2393--2400},
			keywords = {functional MRI, consciousness, resting state, vegetative state, default network},
			year = {2009},
			}

			@article{Schirner2015,
			title = {An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data},
			journal = {Neuroimage},
			volume = {117},
			pages = {343--357},
			month = {Aug.},
			year = {2015},
			author = {Schirner, Michael and Rothmeier, Simon and Jirsa, Viktor K. and McIntosh, Anthony Randal and Ritter, Petra},
			}

			@article{Woo2017,
			author = {Woo, Choong-Wan and Chang, Luke J and Lindquist, Martin A and Wager, Tor D},
			title = {Building better biomarkers: brain models in translational neuroimaging},
			journal = {Nature neuroscience},
			year = {2017},
			volume = {20},
			number = {3},
			pages = {365--377},
			month = feb
			}

			@article{Lindquist2007,
			author = {Lindquist, Martin A and Waugh, Christian and Wager, Tor D},
			title = {Modeling state-related {fMRI} activity using change-point theory},
			journal = {NeuroImage},
			year = {2007},
			volume = {35},
			number = {3},
			pages = {1125--1141},
			month = apr
			}

			@article{Liu2014,
			author = {Liu, Wei and Awate, Suyash P and Anderson, Jeffrey S and Fletcher, P Thomas},
			title = {A functional network estimation method of resting-state {fMRI} using a hierarchical {M}arkov random field},
			journal = {NeuroImage},
			year = {2014},
			volume = {100},
			number = {C},
			pages = {520--534},
			month = oct
			}

			@article{Richiardi2015,
			author = {Richiardi, J and Altmann, A and Milazzo, A C and Chang, C and Chakravarty, M M and Barker, G J and Bokde, A L W and Bromberg, U and Conrod, P and Fauth-Buhler, M and Flor, H and Frouin, V and Garavan, H and Gowland, P and Lemaitre, H and Mann, K F and Martinot, J L and Nees, F and Rietschel, M and Robbins, T W and Smolka, M N and Strohle, A and Schumann, G and Hawrylycz, M and Greicius, M D and {IMAGEN consortium}},
			title = {{Correlated gene expression supports synchronous activity in brain networks}},
			journal = {Science},
			year = {2015},
			volume = {348},
			number = {6240},
			pages = {1241--1244},
			month = jun
			}

			@article{Poldrack2014,
			author = {Poldrack, Russell A and Gorgolewski, Krzysztof J},
			title = {Making big data open: data sharing in neuroimaging},
			journal = {Nature Neuroscience},
			year = {2014},
			volume = {17},
			number = {11},
			pages = {1510--1517},
			month = oct
			}

			@Article{Behjat2016,
			author = {Behjat, Hamid and Richter, Ulrike and Van De Ville, Dimitri and Sornmo, Leif},
			title = {Signal-Adapted Tight Frames on Graphs},
			journal = {IEEE Trans. Signal Process.},
			year = {2016},
			month = {Nov.},
			volume = {64},
			number = {22},
			pages = {6017--6029},
			doi = {10.1109/TSP.2016.2591513},
			url = {/index.php/software/wsgt},
			file = {:behjat1601.pdf:PDF},
			owner = {dvdevill},
			timestamp = {2016.06.08},
			}

			@Article{Behjat2015,
			Title = {Anatomically-Adapted Graph Wavelets for Improved Group-Level {fMRI} Activation Mapping},
			Author = {Behjat, Hamid and Leonardi, Nora and S\"ornmo, Leif and Van De Ville, Dimitri},
			Journal = {Neuroimage},
			Year = {2015},
			Month = {Dec.},
			Pages = {185--199},
			Volume = {123},
			Doi = {10.1016/j.neuroimage.2015.06.010},
			File = {:behjat1501.pdf:PDF},
			Owner = {dvdevill},
			Timestamp = {2015.06.02}
			}

			@Article{Tremblay2014,
			Title = {Graph Wavelets for Multiscale Community Mining},
			Author = {Tremblay, Nicolas and Borgnat, Pierre},
			Journal = {IEEE Trans. Signal Process.},
			Year = {2014},
			Month = {Oct.},
			Number = {20},
			Pages = {5227--5239},
			Volume = {62}
			}

			@Article{Hammond2011,
			Title = {Wavelets on graphs via spectral graph theory},
			Author = {Hammond, D K and Vandergheynst, P and Gribonval, R},
			Journal = {Appl. Comput. Harmon. Anal.},
			Year = {2011},
			Number = {2},
			Month = {Mar.},
			Pages = {129--150},
			Volume = {30}
			}

			@Article{Talmon2013,
			Title = {Diffusion maps for signal processing: A deeper look at manifold-learning techniques based on kernels and graphs},
			Author = {Talmon, Ronen and Cohen, Israel and Gannot, Sharon and Coifman, Ronald R},
			Journal = {IEEE Signal Process. Mag.},
			Year = {2013},

			Month = {Jun.},
			Number = {4},
			Pages = {75--86},
			Volume = {30}
			}

			@Article{Coifman2006,
			Title = {Diffusion wavelets},
			Author = {Coifman, Ronald R and Maggioni, Mauro},
			Journal = {Appl. Comput. Harmon. Anal.},
			Year = {2006},

			Month = {Jul.},
			Number = {1},
			Pages = {53--94},
			Volume = {21}
			}

			@Article{Narang2012,
			Title = {Perfect Reconstruction Two-Channel Wavelet Filter Banks for Graph Structured Data},
			Author = {Narang, Sunil K and Ortega, Antonio},
			Journal = {IEEE Trans. Signal Process.},
			Year = {2012},

			Month = {May},
			Number = {6},
			Pages = {2786--2799},
			Volume = {60}
			}

			@Article{Jansen2009,
			author = {Jansen, M. and Nason, G. P. and Silverman, B. W.},
			title = {Multiscale methods for data on graphs and irregular multidimensional situations},
			journal = {J. R. Stat. Soc. Ser. B Stat. Methodol.},
			volume = {71},
			number = {1},
			pages = {97--125},
			month = {Sep.},
			year = {2008}
			}

			@InProceedings{Crovella2003,
			Title = {Graph wavelets for spatial traffic analysis},
			Author = {Crovella, M. and Kolaczyk, E.},
			Booktitle = {IEEE INFOCOM},
			Year = {2003},
			Month = {Mar.},
			volume = {3},
			Pages = {1848--1857}
			}

			@Book{Mallat2009,
			author = {Mallat, S.},
			title = {A Wavelet Tour of Signal Processing},
			publisher = {Academic Press},
			year = {2009}
			}

			@Article{Pirondini2016,
			Title = {Spectral Method for Generating Surrogate Graph Signals},
			Author = {Pirondini, Elvira and Vybornova, Anna and Coscia, Martina and Van De Ville, Dimitri},
			Journal = {IEEE Signal Process. Lett.},
			Year = {2016},

			Month = {Sep.},
			Number = {9},
			Pages = {1275--1278},
			Volume = {23},

			Doi = {10.1109/LSP.2016.2594072},
			File = {:pirondini1601.pdf:PDF},
			Owner = {dvdevill},
			Timestamp = {2016.05.31},
			Url = {/index.php/software/graph-surrogates}
			}

			@InProceedings{Bolton2017,
			author = {Bolton, Thomas and Van De Ville, Dimitri},
			title = {Sparse Coupled Hidden {M}arkov Models Shed Light On Resting-State {fMRI} Cross-Network Interactions},
			booktitle = {Proceedings of the 14th {IEEE} International Symposium on Biomedical Imaging: From Nano to Macro ({ISBI'17})},
			year = {2017},
			publisher = {IEEE},
			pages = {358--361},
			doi = {10.1109/ISBI.2017.7950537},
			file = {:bolton1701.pdf:PDF},
			owner = {dvdevill},
			timestamp = {2017.06.26}
			}

			@article{Kang2019,
			title={Graph-theoretical analysis for energy landscape reveals the organization of state transitions in the resting-state human cerebral cortex},
			author={Kang, Jiyoung and Pae, Chongwon and Park, Hae-Jeong},
			journal={PloS one},
			volume={14},
			number={9},
			pages={e0222161},
			year={2019},
			publisher={Public Library of Science San Francisco, CA USA}
			}

			@article{Bolton2017b,
			title={Interactions between large-scale functional brain networks are captured by sparse coupled HMMs},
			author={Bolton, Thomas AW and Tarun, Anjali and Sterpenich, Virginie and Schwartz, Sophie and Van De Ville, Dimitri},
			journal={IEEE transactions on medical imaging},
			volume={37},
			number={1},
			pages={230--240},
			year={2017},
			publisher={IEEE}
			}

			@article{WhitfieldGabrieli2009,
			author = {Whitfield-Gabrieli, Susan and Thermenos, Heidi W. and Milanovic, Snezana and Tsuang, Ming T. and Faraone, Stephen V. and McCarley, Robert W. and Shenton, Martha E. and Green, Alan I. and Nieto-Castanon, Alfonso and LaViolette, Peter and Wojcik, Joanne and Gabrieli, John D. E. and Seidman, Larry J. },
			title = {Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia},
			year = {2009},
			doi = {10.1073/pnas.0809141106},
			abstract ={We examined the status of the neural network mediating the default mode of brain function, which typically exhibits greater activation during rest than during task, in patients in the early phase of schizophrenia and in young first-degree relatives of persons with schizophrenia. During functional MRI, patients, relatives, and controls alternated between rest and performance of working memory (WM) tasks. As expected, controls exhibited task-related suppression of activation in the default network, including medial prefrontal cortex (MPFC) and posterior cingulate cortex/precuneus. Patients and relatives exhibited significantly reduced task-related suppression in MPFC, and these reductions remained after controlling for performance. Increased task-related MPFC suppression correlated with better WM performance in patients and relatives and with less psychopathology in all 3 groups. For WM task performance, patients and relatives had greater activation in right dorsolateral prefrontal cortex (DLPFC) than controls. During rest and task, patients and relatives exhibited abnormally high functional connectivity within the default network. The magnitudes of default network connectivity during rest and task correlated with psychopathology in the patients. Further, during both rest and task, patients exhibited reduced anticorrelations between MPFC and DLPFC, a region that was hyperactivated by patients and relatives during WM performance. Among patients, the magnitude of MPFC task suppression negatively correlated with default connectivity, suggesting an association between the hyperactivation and hyperconnectivity in schizophrenia. Hyperactivation (reduced task-related suppression) of default regions and hyperconnectivity of the default network may contribute to disturbances of thought in schizophrenia and risk for the illness.},
			URL = {http://www.pnas.org/content/early/2009/01/21/0809141106.abstract},
			eprint = {http://www.pnas.org/content/early/2009/01/21/0809141106.full.pdf+html},
			journal = {Proceedings of the National Academy of Sciences}
			}

			@article{Tomasi2010,
			author = {Tomasi, Dardo and Volkow, Nora D.},
			title = {Functional connectivity density mapping},
			year = {2010},
			doi = {10.1073/pnas.1001414107},
			abstract ={Brain networks with energy-efficient hubs might support the high cognitive performance of humans and a better understanding of their organization is likely of relevance for studying not only brain development and plasticity but also neuropsychiatric disorders. However, the distribution of hubs in the human brain is largely unknown due to the high computational demands of comprehensive analytical methods. Here we propose a 103 times faster method to map the distribution of the local functional connectivity density (lFCD) in the human brain. The robustness of this method was tested in 979 subjects from a large repository of MRI time series collected in resting conditions. Consistently across research sites, a region located in the posterior cingulate/ventral precuneus (BA 23/31) was the area with the highest lFCD, which suggest that this is the most prominent functional hub in the brain. In addition, regions located in the inferior parietal cortex (BA 18) and cuneus (BA 18) had high lFCD. The variability of this pattern across subjects was <36% and within subjects was 12%. The power scaling of the lFCD was consistent across research centers, suggesting that that brain networks have a ÃÂscale-freeÃÂ organization.},
			URL = {http://www.pnas.org/content/early/2010/05/06/1001414107.abstract},
			eprint = {http://www.pnas.org/content/early/2010/05/06/1001414107.full.pdf+html},
			journal = {Proceedings of the National Academy of Sciences}
			}

			@article{Power2012,
			title = "Spurious but systematic correlations in functional connectivity {MRI} networks arise from subject motion ",
			journal = "NeuroImage ",
			volume = "59",
			number = "3",
			pages = "2142-2154",
			year = "2012",
			note = "",
			issn = "1053-8119",
			doi = "http://dx.doi.org/10.1016/j.neuroimage.2011.10.018",
			url = "http://www.sciencedirect.com/science/article/pii/S1053811911011815",
			author = "Jonathan D. Power and Kelly A. Barnes and Abraham Z. Snyder and Bradley L. Schlaggar and Steven E. Petersen",
			}

			@article {Mitra2014,
			author = {Mitra, Anish and Snyder, Abraham Z and Hacker, Carl D and Raichle, Marcus E.},
			title = {Lag structure in resting state {fMRI}},
			year = {2014},
			doi = {10.1152/jn.00804.2013},
			publisher = {American Physiological Society},
			isbn = {1522-1598},
			issn = {0022-3077},
			journal = {Journal of Neurophysiology}
			}

			@Article{Grandjean2017,
			author = {Grandjean, Joanes and Preti, Maria Giulia and Bolton, Thomas A. and Buerge, Michaela and Seifritz, Erich and Pryce, Christopher R. and Van De Ville, Dimitri and Rudin, Markus},
			title = {Dynamic Reorganization of Intrinsic Functional Networks in the Mouse Brain},
			journal = {NeuroImage},
			year = {2017},
			volume = {152},
			pages = {497-508},
			doi = {10.1016/j.neuroimage.2017.03.026},
			file = {:grandjean1701.pdf:PDF},
			owner = {dvdevill},
			timestamp = {2017.03.14},
			}

			@article{VanDenHeuvel2011,
			title={Rich-club organization of the human connectome},
			author={Van Den Heuvel, Martijn P and Sporns, Olaf},
			journal={J. Neurosci.},
			volume={31},
			number={44},
			pages={15775--15786},
			month = {Nov.},
			year={2011},
			publisher={Soc Neuroscience}
			}

			@Article{Richiardi2013,
			Title = {Machine Learning with Brain Graphs},
			Author = {Richiardi, Jonas and Achard, Sophie and Bunke, Horst and Van De Ville, Dimitri},
			Journal = {IEEE Signal Process. Mag.},
			Year = {2013},

			Month = {May},
			Number = {3},
			Pages = {58--70},
			Volume = {30},

			Doi = {10.1109/MSP.2012.2233865},
			File = {:richiardi1301.pdf:PDF},
			Owner = {dvdevill},
			Timestamp = {2012.11.29},
			Url = {/software/wFC}
			}

			@article{Daubechies2009,
			author = {Daubechies, I. and Roussos, E. and Takerkart, S. and Benharrosh, M. and Golden, C. and D'Ardenne, K. and Richter, W. and Cohen, J. D. and Haxby, J. },
			title = {Independent component analysis for brain {fMRI} does not select for independence},
			year = {2009},
			doi = {10.1073/pnas.0903525106},
			abstract ={InfoMax and FastICA are the independent component analysis algorithms most used and apparently most effective for brain fMRI. We show that this is linked to their ability to handle effectively sparse components rather than independent components as such. The mathematical design of better analysis tools for brain fMRI should thus emphasize other mathematical characteristics than independence.},
			URL = {http://www.pnas.org/content/early/2009/06/24/0903525106.abstract},
			eprint = {http://www.pnas.org/content/early/2009/06/24/0903525106.full.pdf+html},
			journal = {Proceedings of the National Academy of Sciences}
			}

			@article{Leech2012,
			author = {Leech, Robert and Braga, Rodrigo and Sharp, David J.},
			title = {Echoes of the Brain within the Posterior Cingulate Cortex},
			volume = {32},
			number = {1},
			pages = {215-222},
			year = {2012},
			doi = {10.1523/JNEUROSCI.3689-11.2012},
			abstract ={There is considerable uncertainty about the function of the posterior cingulate cortex (PCC). The PCC is a major node within the default mode network (DMN) and has high metabolic activity and dense structural connectivity to widespread brain regions, which suggests it has a role as a cortical hub. The region appears to be involved in internally directed thought, for example, memory recollection. However, recent nonhuman primate work provides evidence for a more active role in the control of cognition, through signaling an environmental change and the need to alter behavior. For an organism to flexibly react to a changing environment, information processed in functionally distinct brain networks needs to be integrated by such a cortical hub. If the PCC is involved in this process, its brain activity should show a complex and dynamic pattern that partially reflects activity in other brain networks. Using fMRI in humans and a multivariate analysis, we demonstrate that the PCC shows this type of complex functional architecture, where echoes of multiple other brain networks are seen in separable yet overlapping subregions. For example, a predominantly ventral region shows strong functional connectivity to the rest of the DMN, whereas two subregions within the dorsal PCC show high connectivity to frontoparietal networks involved in cognitive control. PCC subregions showed distinct patterns of activity modulation during the performance of an attentionally demanding task, suggesting that parts of the dorsal PCC interact with frontoparietal networks to regulate the balance between internally and externally directed cognition.},
			URL = {http://www.jneurosci.org/content/32/1/215.abstract},
			eprint = {http://www.jneurosci.org/content/32/1/215.full.pdf+html},
			journal = {The Journal of Neuroscience}
			}

			@article{Leech2011,
			author = {Leech, Robert and Kamourieh, Salwa and Beckmann, Christian F. and Sharp, David J.},
			title = {Fractionating the Default Mode Network: Distinct Contributions of the Ventral and Dorsal Posterior Cingulate Cortex to Cognitive Control},
			volume = {31},
			number = {9},
			pages = {3217-3224},
			year = {2011},
			doi = {10.1523/JNEUROSCI.5626-10.2011},
			abstract ={The posterior cingulate cortex (PCC) is a central part of the default mode network (DMN) and part of the structural core of the brain. Although the PCC often shows consistent deactivation when attention is focused on external events, anatomical studies show that the region is not homogeneous, and electrophysiological recordings in nonhuman primates suggest that it is directly involved in some forms of attention. We report a functional magnetic resonance imaging study of an attentionally demanding task (either a zero- or two-back working memory task). Standard subtraction analysis within the PCC shows a relative deactivation as task difficulty increases. In contrast, a dual-regression functional connectivity analysis reveals a clear dissociation between ventral and dorsal parts of the PCC. As task difficulty increases, the ventral PCC shows reduced integration within the DMN and less anticorrelation with the cognitive control network (CCN) activated by the task. The dorsal PCC shows an opposite pattern, with increased DMN integration and more anticorrelation. At rest, the dorsal PCC also shows functional connectivity with both the DMN and attentional networks. As expected, these results provide evidence that the PCC is involved in supporting internally directed thought, as the region is more highly integrated with the DMN at low task demands. In contrast, the task-dependent increases in connectivity between the dorsal PCC and the CCN are consistent with a role for this region in modulating the dynamic interaction between these two networks controlling the efficient allocation of attention.},
			URL = {http://www.jneurosci.org/content/31/9/3217.abstract},
			eprint = {http://www.jneurosci.org/content/31/9/3217.full.pdf+html},
			journal = {The Journal of Neuroscience}
			}

			@article{Corbetta2008,
			title = "The Reorienting System of the Human Brain: From Environment to Theory of Mind ",
			journal = "Neuron ",
			volume = "58",
			number = "3",
			pages = "306-324",
			year = "2008",
			note = "",
			issn = "0896-6273",
			doi = "http://dx.doi.org/10.1016/j.neuron.2008.04.017",
			url = "http://www.sciencedirect.com/science/article/pii/S0896627308003693",
			author = "Maurizio Corbetta and Gaurav Patel and Gordon L. Shulman",
			abstract = "Survival can depend on the ability to change a current course of action to respond to potentially advantageous or threatening stimuli. This ÂreorientingÂ response involves the coordinated action of a right hemisphere dominant ventral frontoparietal network that interrupts and resets ongoing activity and a dorsal frontoparietal network specialized for selecting and linking stimuli and responses. At rest, each network is distinct and internally correlated, but when attention is focused, the ventral network is suppressed to prevent reorienting to distracting events. These different patterns of recruitment may reflect inputs to the ventral attention network from the locus coeruleus/norepinephrine system. While originally conceptualized as a system for redirecting attention from one object to another, recent evidence suggests a more general role in switching between networks, which may explain recent evidence of its involvement in functions such as social cognition. "
			}

			@article{Lewis2016,
			author = {Lewis, Laura D and Setsompop, Kawin and Rosen, Bruce R and Polimeni, Jonathan R},
			title = {Fast fMRI can detect oscillatory neural activity in humans},
			journal = {Proceedings of the National Academy of Sciences},
			year = {2016},
			volume = {113},
			number = {43},
			pages = {E6679--E6685},
			month = oct
			}

			@article{DePasquale2010,
			author = {de Pasquale, Francesco and Della Penna, Stefania and Snyder, Abraham Z. and Lewis, Christopher and Mantini, Dante and Marzetti, Laura and Belardinelli, Paolo and Ciancetta, Luca and Pizzella, Vittorio and Romani, Gian Luca and Corbetta, Maurizio},
			title = {Temporal dynamics of spontaneous {MEG} activity in brain networks},
			year = {2010},
			doi = {10.1073/pnas.0913863107},
			abstract ={Functional MRI (fMRI) studies have shown that low-frequency (<0.1 Hz) spontaneous fluctuations of the blood oxygenation level dependent (BOLD) signal during restful wakefulness are coherent within distributed large-scale cortical and subcortical networks (resting state networks, RSNs). The neuronal mechanisms underlying RSNs remain poorly understood. Here, we describe magnetoencephalographic correspondents of two well-characterized RSNs: the dorsal attention and the default mode networks. Seed-based correlation mapping was performed using time-dependent MEG power reconstructed at each voxel within the brain. The topography of RSNs computed on the basis of extended (5 min) epochs was similar to that observed with fMRI but confined to the same hemisphere as the seed region. Analyses taking into account the nonstationarity of MEG activity showed transient formation of more complete RSNs, including nodes in the contralateral hemisphere. Spectral analysis indicated that RSNs manifest in MEG as synchronous modulation of band-limited power primarily within the theta, alpha, and beta bandsÃÂthat is, in frequencies slower than those associated with the local electrophysiological correlates of event-related BOLD responses.},
			URL = {http://www.pnas.org/content/early/2010/03/15/0913863107.abstract},
			eprint = {http://www.pnas.org/content/early/2010/03/15/0913863107.full.pdf+html},
			journal = {Proceedings of the National Academy of Sciences}
			}

			@article{He2008,
			author = {He, Biyu J. and Snyder, Abraham Z. and Zempel, John M. and Smyth, Matthew D. and Raichle, Marcus E.},
			title = {Electrophysiological correlates of the brain's intrinsic large-scale functional architecture},
			volume = {105},
			number = {41},
			pages = {16039-16044},
			year = {2008},
			doi = {10.1073/pnas.0807010105},
			abstract ={Spontaneous fluctuations in the blood-oxygen-level-dependent (BOLD) signals demonstrate consistent temporal correlations within large-scale brain networks associated with different functions. The neurophysiological correlates of this phenomenon remain elusive. Here, we show in humans that the slow cortical potentials recorded by electrocorticography demonstrate a correlation structure similar to that of spontaneous BOLD fluctuations across wakefulness, slow-wave sleep, and rapid-eye-movement sleep. Gamma frequency power also showed a similar correlation structure but only during wakefulness and rapid-eye-movement sleep. Our results provide an important bridge between the large-scale brain networks readily revealed by spontaneous BOLD signals and their underlying neurophysiology.},
			URL = {http://www.pnas.org/content/105/41/16039.abstract},
			eprint = {http://www.pnas.org/content/105/41/16039.full.pdf+html},
			journal = {Proceedings of the National Academy of Sciences}
			}

			@article{Mantini2007,
			author = {Mantini, D. and Perrucci, M. G. and Del Gratta, C. and Romani, G. L. and Corbetta, M.},
			title = {Electrophysiological signatures of resting state networks in the human brain},
			volume = {104},
			number = {32},
			pages = {13170-13175},
			year = {2007},
			doi = {10.1073/pnas.0700668104},
			abstract ={Functional neuroimaging and electrophysiological studies have documented a dynamic baseline of intrinsic (not stimulus- or task-evoked) brain activity during resting wakefulness. This baseline is characterized by slow (<0.1 Hz) fluctuations of functional imaging signals that are topographically organized in discrete brain networks, and by much faster (1ÃÂ80 Hz) electrical oscillations. To investigate the relationship between hemodynamic and electrical oscillations, we have adopted a completely data-driven approach that combines information from simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Using independent component analysis on the fMRI data, we identified six widely distributed resting state networks. The blood oxygenation level-dependent signal fluctuations associated with each network were correlated with the EEG power variations of delta, theta, alpha, beta, and gamma rhythms. Each functional network was characterized by a specific electrophysiological signature that involved the combination of different brain rhythms. Moreover, the joint EEG/fMRI analysis afforded a finer physiological fractionation of brain networks in the resting human brain. This result supports for the first time in humans the coalescence of several brain rhythms within large-scale brain networks as suggested by biophysical studies.},
			URL = {http://www.pnas.org/content/104/32/13170.abstract},
			eprint = {http://www.pnas.org/content/104/32/13170.full.pdf+html},
			journal = {Proceedings of the National Academy of Sciences}
			}

			@article{Smith2009,
			author = {Smith, Stephen M. and Fox, Peter T. and Miller, Karla L. and Glahn, David C. and Fox, P. Mickle and Mackay, Clare E. and Filippini, Nicola and Watkins, Kate E. and Toro, Roberto and Laird, Angela R. and Beckmann, Christian F.},
			title = {Correspondence of the brain's functional architecture during activation and rest},
			volume = {106},
			number = {31},
			pages = {13040-13045},
			year = {2009},
			doi = {10.1073/pnas.0905267106},
			abstract ={Neural connections, providing the substrate for functional networks, exist whether or not they are functionally active at any given moment. However, it is not known to what extent brain regions are continuously interacting when the brain is ÃÂat rest.ÃÂ In this work, we identify the major explicit activation networks by carrying out an image-based activation network analysis of thousands of separate activation maps derived from the BrainMap database of functional imaging studies, involving nearly 30,000 human subjects. Independently, we extract the major covarying networks in the resting brain, as imaged with functional magnetic resonance imaging in 36 subjects at rest. The sets of major brain networks, and their decompositions into subnetworks, show close correspondence between the independent analyses of resting and activation brain dynamics. We conclude that the full repertoire of functional networks utilized by the brain in action is continuously and dynamically ÃÂactiveÃÂ even when at ÃÂrest.ÃÂ},
			URL = {http://www.pnas.org/content/106/31/13040.abstract},
			eprint = {http://www.pnas.org/content/106/31/13040.full.pdf+html},
			journal = {Proceedings of the National Academy of Sciences}
			}

			@article{Sonkusare2019,
			title={Naturalistic Stimuli in Neuroscience: Critically Acclaimed},
			author={Sonkusare, Saurabh and Breakspear, Michael and Guo, Christine},
			journal={Trends in cognitive sciences},
			year={2019},
			publisher={Elsevier}
			}

			@article{Vetterli2002,
			author={Vetterli, M. and Marziliano, P. and Blu, T.},
			journal={Signal Processing, IEEE Transactions on},
			title={Sampling signals with finite rate of innovation},
			year={2002},
			volume={50},
			number={6},
			pages={1417-1428},
			keywords={bandlimited signals;error correction codes;filtering theory;polynomials;signal reconstruction;signal sampling;spectral analysis;splines (mathematics);Dirac streams;Poisson process;annihilating filter;bandlimited kernel;bandlimited signal;biological systems;communications systems;error-correction coding;finite innovation rate;finite-length streams;local reconstruction;local sampling;locator filter;nonuniform splines;periodic streams;piecewise polynomials;sampling theorems;signal processing;signal reconstruction;signal sampling;sinc kernel;spectral analysis;spline kernels;Biology computing;Biomedical signal processing;Filters;Kernel;Polynomials;Sampling methods;Signal processing;Signal sampling;Spectral analysis;Technological innovation},
			doi={10.1109/TSP.2002.1003065},
			ISSN={1053-587X},}

			@article{McIntosh1999,
			author = {McIntosh, A. R. and Rajah, M. N. and Lobaugh, N. J.},
			title = {Interactions of Prefrontal Cortex in Relation to Awareness in Sensory Learning},
			volume = {284},
			number = {5419},
			pages = {1531-1533},
			year = {1999},
			doi = {10.1126/science.284.5419.1531},
			abstract ={In an associative learning paradigm, human subjects could be divided based on whether they were aware that one tone predicted a visual event and another did not. Only aware subjects acquired a differential behavioral response to the tones. Regional cerebral blood flow in left prefrontal cortex showed learning-related changes only in aware subjects. Left prefrontal cortex also showed changes in functional connectivity with contralateral prefrontal cortex, sensory association cortices, and cerebellum. Several of the interacting areas correlated with aware subjects' behavior. These results suggest cerebral processes underlying awareness are mediated through interactions of large-scale neurocognitive systems.},
			URL = {http://www.sciencemag.org/content/284/5419/1531.abstract},
			eprint = {http://www.sciencemag.org/content/284/5419/1531.full.pdf},
			journal = {Science}
			}

			@article{Krishnan2011,
			title = "Partial Least Squares {(PLS)} methods for neuroimaging: A tutorial and review ",
			journal = "NeuroImage ",
			volume = "56",
			number = "2",
			pages = "455-475",
			year = "2011",
			note = "Multivariate Decoding and Brain Reading",
			issn = "1053-8119",
			doi = "10.1016/j.neuroimage.2010.07.034",
			url = "http://www.sciencedirect.com/science/article/pii/S1053811910010074",
			author = "Krishnan, A. and Williams, L. J. and McIntosh, A. R. and Abdi, H.",
			}

			@article{McIntosh1996,
			title = "Spatial Pattern Analysis of Functional Brain Images Using Partial Least Squares ",
			journal = "NeuroImage ",
			volume = "3",
			number = "3",
			pages = "143-157",
			year = "1996",
			note = "",
			issn = "1053-8119",
			doi = "10.1006/nimg.1996.0016",
			url = "http://www.sciencedirect.com/science/article/pii/S1053811996900166",
			author = "McIntosh, A.R. and Bookstein, F.L. and Haxby, J.V. and Grady, C.L. "
			}

			@article{Thornton2010,
			author = {Thornton, R. and Laufs, H. and Rodionov, R. and Cannadathu, S. and Carmichael, D. W. and Vulliemoz, S. and {Salek-Haddadi}, A. and McEvoy, A. W. and Smith, S. M. and Lhatoo, S. and Elwes, R. D. C. and Guye, Maxime and Walker, M. C. and Lemieux, L. and Duncan, J. S.},
			title = {{EEG} correlated functional {MRI} and postoperative outcome in focal epilepsy},
			volume = {81},
			number = {8},
			pages = {922-927},
			year = {2010},
			doi = {10.1136/jnnp.2009.196253},
			URL = {http://jnnp.bmj.com/content/81/8/922.abstract},
			journal = {Journal of Neurology, Neurosurgery and Psychiatry}
			}

			@article{Zijlmans2007,
			author = {Zijlmans, M. and Huiskamp, G. and Hersevoort, M. and Seppenwoolde, J.-H. and van Huffelen, A. C. and Leijten, F. S. S.},
			title = {{EEG-fMRI} in the preoperative work-up for epilepsy surgery},
			volume = {130},
			number = {9},
			pages = {2343-2353},
			year = {2007},
			doi = {10.1093/brain/awm141},
			journal = {Brain}
			}

			@article {Nichols2002,
			author = {Nichols, T. E. and Holmes, A. P.},
			title = {Nonparametric permutation tests for functional neuroimaging: A primer with examples},
			journal = {Human Brain Mapping},
			volume = {15},
			number = {1},
			publisher = {John Wiley & Sons, Inc.},
			issn = {1097-0193},
			url = {http://dx.doi.org/10.1002/hbm.1058},
			doi = {10.1002/hbm.1058},
			pages = {1--25},
			keywords = {hypothesis test, multiple comparisons, statistic image, nonparametric, permutation test, randomization test, SPM, general linear model},
			year = {2002},
			}

			@article{Allen2000,
			title = "A Method for Removing Imaging Artifact from Continuous {EEG} Recorded during Functional {MRI} ",
			journal = "NeuroImage ",
			volume = "12",
			number = "2",
			pages = "230-239",
			year = "2000",
			note = "",
			issn = "1053-8119",
			doi = "10.1006/nimg.2000.0599",
			url = "http://www.sciencedirect.com/science/article/pii/S1053811900905998",
			author = "Allen, P. J. and Josephs, O. and Turner, R."
			}

			@article{Smith2010,
			author = {S. M. Smith and K. L. Miller and G. {Salimi-Khorshidi} and M. Webster and C. F. Beckmann and T. E. Nichols and J. D. Ramsey and M. W. Woolrich},
			journal = {NeuroImage},
			title = {Network Modelling Methods for {fMRI}},
			abstract = {There is great interest in estimating brain "networks" from {fMRI} data. This is often attempted by identifying a set of functional "nodes" (e.g., spatial ROIs or {ICA} maps) and then conducting a connectivity analysis between the nodes, based on the {fMRI} timeseries associated with the nodes. Analysis methods range from very simple measures that consider just two nodes at a time (e.g., correlation between two nodes' timeseries) to sophisticated approaches that consider all nodes simultaneously and estimate one global network model (e.g., Bayes net models). Many different methods are being used in the literature, but almost none has been carefully validated or compared for use on {fMRI} timeseries data. In this work we generate rich, realistic simulated {fMRI} data for a wide range of underlying networks, experimental protocols and problematic confounds in the data, in order to compare different connectivity estimation approaches. Our results show that in general correlation-based approaches can be quite successful, methods based on higher-order statistics are less sensitive, and lag-based approaches perform very poorly. More specifically: there are several methods that can give high sensitivity to network connection detection on good quality {fMRI} data, in particular, partial correlation, regularised inverse covariance estimation and several Bayes net methods; however, accurate estimation of connection directionality is more difficult to achieve, though Patel's tau can be reasonably successful. With respect to the various confounds added to the data, the most striking result was that the use of functionally inaccurate ROIs (when defining the network nodes and extracting their associated time series) is extremely damaging to network estimation; hence, results derived from inappropriate ROI definition (such as via structural atlases) should be regarded with great caution.},
			affiliation = {FMRIB (Oxford University Centre for Functional MRI of the Brain), Dept. Clinical Neurology, University of Oxford.},
			pages = {},
			year = {2010},
			month = {Sep},
			language = {ENG},
			date-added = {2010-09-14 15:34:12 +0200},
			date-modified = {2011-04-11 16:27:40 +0200},
			doi = {10.1016/j.neuroimage.2010.08.063},
			pii = {S1053-8119(10)01160-2},
			pmid = {20817103},
			URL = {http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WNP-50XS6GF-5&_user=164550&_coverDate=09%252F03%252F2010&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_acct=C000013218&_version=1&_urlVersion=0&_userid=164550&md5=7a6abb46aa7e74962858b4e92cf6d4f3&searchtype=a},
			local-url = {file://localhost/Users/isik/Documents/Papers/Smith/2010/Neuroimage%202010%20Smith-Network%20Modelling%20Methods%20for%20FMRI.pdf},
			uri = {papers://0627FC08-E3A4-4A9D-A7CB-FFE412EEB21B/Paper/p2616},
			read = {Yes},
			rating = {0}
			}

			@article{Kucyi2017,
			title = "Just a thought: How mind-wandering is represented in dynamic brain connectivity",
			journal = "NeuroImage ",
			year = "in press",
			issn = "1053-8119",
			doi = "https://doi.org/10.1016/j.neuroimage.2017.07.001",
			url = "http://www.sciencedirect.com/science/article/pii/S1053811917305694",
			author = "Aaron Kucyi",
			keywords = "Resting state",
			keywords = "Daydreaming",
			keywords = "Spontaneous cognition",
			keywords = "Consciousness",
			keywords = "Experience sampling",
			keywords = "fMRI "
			}

			@article{Breakspear2017,
			author = {Breakspear, Michael},
			title = {Dynamic models of large-scale brain activity},
			journal = {Nature neuroscience},
			year = {2017},
			volume = {20},
			number = {3},
			pages = {340--352},
			month = feb
			}

			@article{Ashourvan2017,
			author = {Ashourvan, Arian and Gu, Shi and Mattar, Marcelo G and Vettel, Jean M and Bassett, Danielle S},
			title = {The energy landscape underpinning module dynamics in the human brain connectome},
			journal = {NeuroImage},
			year = {2017},
			volume = {157},
			pages = {364--380},
			month = aug
			}


			@article {Miller2017,
			author = {Miller, Robyn L. and Adali, Tulay and Levin-Schwartz, Yuri and Calhoun, Vince D.},
			title = {Resting-State fMRI Dynamics and Null Models: Perspectives, Sampling Variability, and Simulations},
			year = {2017},
			doi = {10.1101/153411},
			publisher = {Cold Spring Harbor Labs Journals},
			URL = {http://www.biorxiv.org/content/early/2017/06/22/153411},
			eprint = {http://www.biorxiv.org/content/early/2017/06/22/153411.full.pdf},
			journal = {bioRxiv}
			}

			@article{Vidaurre2017b,
			author = {Vidaurre, Diego and Abeysuriya, Romesh and Becker, Robert and Quinn, Andrew J and Alfaro-Almagro, Fidel and Smith, Stephen M and Woolrich, Mark W},
			title = {Discovering dynamic brain networks from big data in rest and task},
			journal = {NeuroImage},
			year = {in press},
			doi = {https://doi.org/10.1016/j.neuroimage.2017.06.077},
			issn = "1053-8119",
			url = "http://www.sciencedirect.com/science/article/pii/S1053811917305487",
			month = jun
			}

			@article{Bellec2010,
			title = "{Multi-level bootstrap analysis of stable clusters in resting-state {fMRI}}",
			journal = "NeuroImage",
			volume = "51",
			number = "3",
			pages = "1126-1139",
			year = "2010",
			note = "",
			issn = "1053-8119",
			doi = "10.1016/j.neuroimage.2010.02.082",
			url = "http://www.sciencedirect.com/science/article/pii/S1053811910002697",
			author = "P. Bellec and P. Rosa-Neto and O. C. Lyttelton and H. Benali and A. C. Evans",
			keywords = "Bootstrap",
			keywords = "Clustering",
			keywords = "Functional MRI",
			keywords = "Hierarchical clustering",
			keywords = "<span style='font-style: italic'>k</span>-Means",
			keywords = "Multi-level analysis",
			keywords = "Resting-state networks",
			keywords = "Stability analysis"
			}

			@article {Buckner2008,
			author = {Buckner, R. L. and Andrews-Hanna, J. R. and Schacter, D. L.},
			title = {The Brain's Default Network},
			journal = {Annals of the New York Academy of Sciences},
			volume = {1124},
			number = {1},
			publisher = {Blackwell Publishing Inc},
			issn = {1749-6632},
			url = {http://dx.doi.org/10.1196/annals.1440.011},
			doi = {10.1196/annals.1440.011},
			pages = {1--38},
			keywords = {default mode, default system, default network, {fMRI}, PET, hippocampus, memory, schizophrenia, Alzheimer},
			year = {2008},
			}

			@article{Yeo2014,
			title = "Estimates of segregation and overlap of functional connectivity networks in the human cerebral cortex",
			journal = "NeuroImage ",
			volume = "88",
			number = "0",
			pages = "212-227",
			year = "2014",
			note = "",
			issn = "1053-8119",
			doi = "http://dx.doi.org/10.1016/j.neuroimage.2013.10.046",
			url = "http://www.sciencedirect.com/science/article/pii/S1053811913010690",
			author = "B.T. Thomas Yeo and Fenna M. Krienen and Michael W.L. Chee and Randy L. Buckner"
			}

			@article{Dosenbach2010,
			author = {Dosenbach, Nico U F and Nardos, Binyam and Cohen, Alexander L and Fair, Damien A and Power, Jonathan D and Church, Jessica A and Nelson, Steven M and Wig, Gagan S and Vogel, Alecia C and Lessov-Schlaggar, Christina N and Barnes, Kelly Anne and Dubis, Joseph W and Feczko, Eric and Coalson, Rebecca S and Pruett, John R and Barch, Deanna M and Petersen, Steven E and Schlaggar, Bradley L},
			title = {Prediction of individual brain maturity using f{MRI}},
			journal = {Science},
			year = {2010},
			volume = {329},
			number = {5997},
			pages = {1358--1361},
			month = {Sep.}
			}

			@article{Sizemore2017,
			author = {Sizemore, Ann E and Bassett, Danielle S},
			title = {Dynamic graph metrics: Tutorial, toolbox, and tale},
			journal = {arXiv preprint arXiv:1703.10643},
			year = {2017}
			}

			@article{Greicius2003,
			author = {Greicius, M. D. and Krasnow, B. and Reiss, A. L. and Menon, V.},
			journal = {Proceedings of the National Academy of Sciences of the United States of America},
			title = {Functional connectivity in the resting brain: a network analysis of the default mode hypothesis},
			number = {1},
			pages = {253--8},
			volume = {100},
			year = {2003},
			month = {Jan},
			doi = {10.1073/pnas.0135058100},
			pii = {0135058100},
			pmid = {12506194},
			URL = {http://www.pnas.org/content/100/1/253.long},
			uri = {papers://0627FC08-E3A4-4A9D-A7CB-FFE412EEB21B/Paper/p15570},
			rating = {0}
			}

			@article{Churchill2012,
			author = {Churchill, N. W. and Oder, A. and Abdi, H. and Tam, F. and Lee, W. and Thomas, C. and Ween, J. E. and Graham, S. J. and Strother, S. C.},
			journal = {Human Brain Mapping},
			title = {Optimizing preprocessing and analysis pipelines for single-subject {fMRI. I}. Standard temporal motion and physiological noise correction methods},
			number = {3},
			pages = {609--27},
			volume = {33},
			year = {2012},
			month = {Mar},
			doi = {10.1002/hbm.21238},
			URL = {http://onlinelibrary.wiley.com/doi/10.1002/hbm.21238/abstract;jsessionid=730D6F6C14FCD0A76593BD12C5CE080E.d03t02},
			uri = {papers://0627FC08-E3A4-4A9D-A7CB-FFE412EEB21B/Paper/p14053},
			read = {Yes},
			rating = {0}
			}

			@article{McIntosh2004,
			title = "Partial least squares analysis of neuroimaging data: applications and advances",
			journal = "NeuroImage",
			volume = "23, Supplement 1",
			pages = "S250-S263",
			year = "2004",
			note = "Mathematics in Brain Imaging",
			issn = "1053-8119",
			doi = "10.1016/j.neuroimage.2004.07.020",
			url = "http://www.sciencedirect.com/science/article/pii/S1053811904003866",
			author = "A. R. McIntosh and N. J. Lobaugh",
			}

			@article{Zou2005,
			title={Regularization and variable selection via the elastic net},
			author={Zou, Hui and Hastie, Trevor},
			journal={Journal of the royal statistical society: series B (statistical methodology)},
			volume={67},
			number={2},
			pages={301--320},
			year={2005},
			publisher={Wiley Online Library}
			}

			@article{Pedregosa2011,
			title={Scikit-learn: Machine learning in Python},
			author={Pedregosa, Fabian and Varoquaux, Ga{\"e}l and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and others},
			journal={Journal of machine learning research},
			volume={12},
			number={Oct},
			pages={2825--2830},
			year={2011}
			}

			@article{Seeley2007,
			author = {Seeley, W. W. and Menon, V. and Schatzberg, A. F. and Keller, J. and Glover, G. H. and Kenna, H. and Reiss, A. L. and Greicius, M. D. },
			title = {Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control},
			volume = {27},
			number = {9},
			pages = {2349-2356},
			year = {2007},
			doi = {10.1523/JNEUROSCI.5587-06.2007},
			journal = {The Journal of Neuroscience}
			}

			@article{Raichle2001,
			author = {Raichle, M. E. and MacLeod, A. M. and Snyder, A. Z. and Powers, W. J. and Gusnard, D. A. and Shulman, G. L.},
			citeulike-article-id = {770132},
			citeulike-linkout-0 = {http://dx.doi.org/10.1073/pnas.98.2.676},
			citeulike-linkout-1 = {http://www.pnas.org/content/98/2/676.abstract},
			citeulike-linkout-2 = {http://www.pnas.org/content/98/2/676.full.pdf},
			citeulike-linkout-3 = {http://www.pnas.org/cgi/content/abstract/98/2/676},
			citeulike-linkout-4 = {http://view.ncbi.nlm.nih.gov/pubmed/11209064},
			citeulike-linkout-5 = {http://www.hubmed.org/display.cgi?uids=11209064},
			day = {16},
			doi = {10.1073/pnas.98.2.676},
			issn = {0027-8424},
			journal = {Proceedings of the National Academy of Sciences of the United States of America},
			keywords = {default, mode},
			month = {January},
			number = {2},
			pages = {676--682},
			posted-at = {2007-09-17 03:31:40},
			priority = {3},
			title = {A default mode of brain function},
			url = {http://dx.doi.org/10.1073/pnas.98.2.676},
			volume = {98},
			year = {2001}
			}

			@article{Fox2007,
			title = "Spontaneous fluctuations in brain activity observed with {fMRI}",
			journal = "Nature Review Neuroscience",
			volume = "8",
			number = "9",
			pages = "700-711",
			year = "2007",
			issn = "1471-003X",
			doi = "10.1038/nrn2201",
			url = "http://dx.doi.org/10.1038/nrn2201",
			author = "M. D. Fox and M. E. Raichle"
			}

			@article{DeLuca2006,
			title = "{fMRI} resting state networks define distinct modes of long-distance interactions in the human brain",
			journal = "NeuroImage",
			volume = "29",
			number = "4",
			pages = "1359-1367",
			year = "2006",
			note = "",
			issn = "1053-8119",
			doi = "10.1016/j.neuroimage.2005.08.035",
			url = "http://www.sciencedirect.com/science/article/pii/S1053811905006257",
			author = "M. De Luca and C.F. Beckmann and N. De Stefano and P.M. Matthews and S.M. Smith",
			keywords = "Functional MRI",
			keywords = "Brain activation",
			keywords = "Resting state",
			keywords = "Independent component analysis",
			keywords = "Functional connectivity",
			keywords = "Resting state networks",
			keywords = "PICA",
			keywords = "Perfusion {fMRI}"
			}

			@article{Fox2006,
			author = {M. D. Fox and M. Corbetta and A. Z. Snyder and J. L. Vincent and M. E. Raichle},
			title = {Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems},
			volume = {103},
			number = {26},
			pages = {10046-10051},
			year = {2006},
			doi = {10.1073/pnas.0604187103},
			journal = {Proceedings of the National Academy of Sciences}
			}

			@article{Corbetta2002,
			author = {M. Corbetta and G. L. Shulman},
			title = {Control of goal-directed and stimulus-driven attention in the brain},
			journal = {Nature Reviews Neuroscience},
			volume = {3},
			issue={3},
			pages = {201-215},
			year = {2002},
			doi = {10.1038/nrn755},
			URL = {http://dx.doi.org/10.1038/nrn755},
			}

			@article {Friston1996,
			author = {Friston, K. J. and Williams, S. and Howard, R. and Frackowiak, R. S. J. and Turner, R.},
			title = {Movement-Related effects in {fMRI} time-series},
			journal = {Magnetic Resonance in Medicine},
			volume = {35},
			number = {3},
			publisher = {Wiley Subscription Services, Inc., A Wiley Company},
			issn = {1522-2594},
			url = {http://dx.doi.org/10.1002/mrm.1910350312},
			doi = {10.1002/mrm.1910350312},
			pages = {346--355},
			keywords = {realignment, {fMRI}, movement artifacts, autoregression-moving average models},
			year = {1996},
			}

			@article{Keilholz2017,
			author = {Keilholz, S. D. and Caballero-Gaudes, C. and Bandettini, P. and Deco, G. and Calhoun, V. D.},
			title = {Time-resolved resting state f{MRI} analysis: current status, challenges, and new directions},
			journal = {Brain Connect.},
			volume = {7},
			number = {8},
			month = {Oct.},
			pages = {465--481},
			year = {2017}
			}

			@ARTICLE{Friston2000,
			author = {K. J. Friston and A. Mechelli and R. Turner and C. J. Price},
			title = {Nonlinear Responses in {fMRI}: The Balloon Model, {Volterra} Kernels,
			and Other Hemodynamics},
			journal = {NeuroImage},
			year = {2000},
			volume = {12},
			pages = {466-477},
			number = {4},
			doi = {10.1006/nimg.2000.0630},
			issn = {1053-8119},
			keywords = {nonlinear system identification},
			url = {http://www.sciencedirect.com/science/article/pii/S105381190090630X}
			}

			@article{Heeger2002,
			author={ Heeger, D. J. and Ress, D.},
			title={ What does {fMRI} tell us about neuronal activity?},
			journal = {Nature Reviews Neuroscience},
			year={2002},
			volume = {3},
			issue={2},
			pages = {142-151}
			}

			@article {Glover2000,
			author = {Glover, G. H. and Li, T.-Q. and Ress, D.},
			title = {Image-based method for retrospective correction of physiological motion effects in {fMRI: RETROICOR}},
			journal = {Magnetic Resonance in Medicine},
			volume = {44},
			number = {1},
			publisher = {John Wiley & Sons, Inc.},
			issn = {1522-2594},
			url = {http://dx.doi.org/10.1002/1522-2594(200007)44:1<162::AID-MRM23>3.0.CO;2-E},
			doi = {10.1002/1522-2594(200007)44:1<162::AID-MRM23>3.0.CO;2-E},
			pages = {162--167},
			keywords = {functional magnetic resonance imaging, physiological motion, retrospective motion correction},
			year = {2000},
			}

			@article {Gaudes2011,
			author = {Caballero-Gaudes, C. and Petridou, N. and Francis, S. T. and Dryden, V.
			and Gowland, P. A.},
			title = {Paradigm free mapping with sparse regression automatically detects
			single-trial {fMRI} {BOLD} responses},
			journal = {Human Brain Mapping},
			volume = {34},
			number = {3},
			publisher = {Wiley Subscription Services, Inc., A Wiley Company},
			issn = {1097-0193},
			url = {http://dx.doi.org/10.1002/hbm.21452},
			doi = {10.1002/hbm.21452},
			pages = {501--518},
			keywords = {single-trial analysis, paradigm free mapping, sparse regression, fMRI, brain mapping},
			year = {2013},
			}

			@article{Kriegeskorte2009,
			author = " Kriegeskorte, N and Simmons, W. K. and Bellgowan, P. S. F. and Baker, C. I.",
			title = "Circular analysis in systems neuroscience: the dangers of double dipping",
			journal = "Nature Neuroscience",
			year= "2009",
			volume= "12",
			issue = "5",
			pages = "535-540",
			publisher= "Nature Publishing Group",
			issn= "1097-6256",
			url = "http://dx.doi.org/10.1038/nn.2303"
			}

			@article{Sporns2016,
			author = {Sporns, O. and Betzel, R. F.},
			title = {Modular Brain Networks},
			journal = {Annu. Rev. Psychol.},
			volume = {67},
			pages = {613-640},
			month = {Jan.},
			year = {2016}
			}

			@ARTICLE{TzourioMazoyer2002,
			author = {N. Tzourio-Mazoyer and B. Landeau and D. Papathanassiou and F. Crivello
			and O. Etard and N. Delcroix and B. Mazoyer and M. Loliot},
			title = {Automated anatomical labeling of activations in {SPM} using a macroscopic
			anatomical parcellation of the {MNI} {MRI} single-subject brain},
			journal = {NeuroImage},
			year = {2002},
			volume = {15},
			pages = {273-289}
			}

			@article{Friedman2010,
			title={Regularization paths for generalized linear models via coordinate descent},
			author={Friedman, Jerome and Hastie, Trevor and Tibshirani, Rob},
			journal={Journal of statistical software},
			volume={33},
			number={1},
			pages={1},
			year={2010},
			publisher={NIH Public Access}
			}

			@article{Tibshirani1996,
			author = {R. Tibshirani},
			title = {Regression Shrinkage and Selection Via the {LASSO}},
			journal = {Journal of the Royal Statistical Society, Series B},
			year = {1994},
			volume = {58},
			pages = {267--288}
			}

			@article{AndrewsHanna2010,
			author = {Andrews-Hanna, Jessica R and Reidler, Jay S and Sepulcre, Jorge and Poulin, Renee and Buckner, Randy L},
			doi = {10.1016/j.neuron.2010.02.005},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/5c5f69361f322e83888dbfa2b92b7f5892ada9e0.pdf:pdf},
			issn = {0896-6273},
			journal = {Neuron},
			number = {4},
			pages = {550--562},
			publisher = {Elsevier Ltd},
			title = {{Functional-Anatomic Fractionation of the Brain's Default Network}},
			url = {http://dx.doi.org/10.1016/j.neuron.2010.02.005 https://ac.els-cdn.com/S0896627310000966/1-s2.0-S0896627310000966-main.pdf?{\_}tid=ad5948c1-b9f7-473f-b5a3-5306802423a6{\&}acdnat=1547202687{\_}86b654fa20b4b460cde911449429f31a},
			volume = {65},
			year = {2010}
			}

			@article{Friedman2007,
			title={Pathwise coordinate optimization},
			author={Friedman, Jerome and Hastie, Trevor and H{\"o}fling, Holger and Tibshirani, Robert and others},
			journal={The annals of applied statistics},
			volume={1},
			number={2},
			pages={302--332},
			year={2007},
			publisher={Institute of Mathematical Statistics}
			}

			@article{Friedman2008,
			title={Sparse inverse covariance estimation with the graphical lasso},
			author={Friedman, Jerome and Hastie, Trevor and Tibshirani, Robert},
			journal={Biostatistics},
			volume={9},
			number={3},
			pages={432--441},
			year={2008},
			publisher={Oxford University Press}
			}

			@article{Schwarz1978,
			title={Estimating the dimension of a model},
			author={Schwarz, Gideon and others},
			journal={The annals of statistics},
			volume={6},
			number={2},
			pages={461--464},
			year={1978},
			publisher={Institute of Mathematical Statistics}
			}

			@article{Eklund2016,
			title={Cluster failure: why f{MRI} inferences for spatial extent have inflated false-positive rates},
			author={Eklund, Anders and Nichols, Thomas E and Knutsson, Hans},
			journal={Proceedings of the National Academy of Sciences},
			pages={201602413},
			year={2016},
			publisher={National Acad Sciences}
			}

			@article{Ogawa1990,
			author = {Ogawa, S and Lee, T M and Kay, A R},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/017ec0fb815db8d589d95239ce416456ba8460e0.pdf:pdf},
			journal = {Proceedings of the National Academy of Sciences},
			number = {December},
			pages = {9868--9872},
			title = {{Brain magnetic resonance imaging with contrast dependent on blood oxygenation}},
			url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC55275/pdf/pnas01049-0370.pdf},
			volume = {87},
			year = {1990}
			}

			@article{Hasson2010,
			title={Reliability of cortical activity during natural stimulation},
			author={Hasson, Uri and Malach, Rafael and Heeger, David J},
			journal={Trends in cognitive sciences},
			volume={14},
			number={1},
			pages={40--48},
			year={2010},
			publisher={Elsevier}
			}

			@article{Dumont2013,
			abstract = {Feedback control is ubiquitous in nature and engineering and has revolutionized safety in fields from space travel to the automobile. In anesthesia, automated feedback control holds the promise of limiting the effects on performance of individual patient variability, optimizing the workload of the anesthesiologist, increasing the time spent in a more desirable clinical state, and ultimately improving the safety and quality of anesthesia care. The benefits of control systems will not be realized without widespread support from the health care team in close collaboration with industrial partners. In this review, we provide an introduction to the established field of control systems research for the everyday anesthesiologist. We introduce important concepts such as feedback and modeling specific to control problems and provide insight into design requirements for guaranteeing the safety and performance of feedback control systems. We focus our discussion on the optimization of anesthetic drug administration.},
			author = {Dumont, Guy a. and Ansermino, J. Mark},
			doi = {10.1213/ANE.0b013e3182973687},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Dumont, Ansermino - 2013 - Closed-loop control of anesthesia A primer for anesthesiologists.pdf:pdf},
			isbn = {1526-7598 (Electronic)$\backslash$r0003-2999 (Linking)},
			issn = {00032999},
			journal = {Anesthesia and Analgesia},
			number = {5},
			pages = {1130--1138},
			pmid = {23835456},
			title = {{Closed-loop control of anesthesia: A primer for anesthesiologists}},
			volume = {117},
			year = {2013}
			}

			@article{Norman2006,
			abstract = {A key challenge for cognitive neuroscience is determining how mental representations map onto patterns of neural activity. Recently, researchers have started to address this question by applying sophisticated pattern-classification algorithms to distributed (multi-voxel) patterns of functional MRI data, with the goal of decoding the information that is represented in the subject's brain at a particular point in time. This multi-voxel pattern analysis (MVPA) approach has led to several impressive feats of mind reading. More importantly, MVPA methods constitute a useful new tool for advancing our understanding of neural information processing. We review how researchers are using MVPA methods to characterize neural coding and information processing in domains ranging from visual perception to memory search. ?? 2006 Elsevier Ltd. All rights reserved.},
			archivePrefix = {arXiv},
			arxivId = {NIHMS150003},
			author = {Norman, Kenneth A. and Polyn, Sean M. and Detre, Greg J. and Haxby, James V.},
			doi = {10.1016/j.tics.2006.07.005},
			eprint = {NIHMS150003},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/235c7758e553e3c31339aac67c2169ea138874fc.pdf:pdf},
			isbn = {1364-6613},
			issn = {13646613},
			journal = {Trends in Cognitive Sciences},
			number = {9},
			pages = {424--430},
			pmid = {16899397},
			title = {{Beyond mind-reading: multi-voxel pattern analysis of fMRI data}},
			url = {http://compmemweb.princeton.edu/wp/wp-content/uploads/2016/11/beyond-mind-reading.pdf},
			volume = {10},
			year = {2006}
			}

			@article{Vidaurre2017,
			abstract = {The brain recruits neuronal populations in a temporally coordinated manner in task and at rest. However, the extent to which large-scale networks exhibit their own organized temporal dynamics is unclear. We use an approach designed to find repeating network patterns in whole-brain resting fMRI data, where networks are defined as graphs of interacting brain areas. We find that the transitions between networks are nonrandom, with certain networks more likely to occur after others. Further, this nonrandom sequencing is itself hierarchically organized, revealing two distinct sets of networks, or metastates, that the brain has a tendency to cycle within. One metastate is associated with sensory and motor regions, and the other involves areas related to higher order cognition. Moreover, we find that the proportion of time that a subject spends in each brain network and metastate is a consistent subject-specific measure, is heritable, and shows a significant relationship with cognitive traits.},
			archivePrefix = {arXiv},
			arxivId = {arXiv:1408.1149},
			author = {Vidaurre, Diego and Smith, Stephen M. and Woolrich, Mark W.},
			doi = {10.1073/pnas.1705120114},
			eprint = {arXiv:1408.1149},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/706098c4e22574ff2ac4fef1e21021ba563d8db4.pdf:pdf},
			isbn = {0711232105},
			issn = {0027-8424},
			journal = {Proceedings of the National Academy of Sciences},
			number = {48},
			pages = {201705120},
			pmid = {29087305},
			title = {{Brain network dynamics are hierarchically organized in time}},
			url = {http://www.pnas.org/lookup/doi/10.1073/pnas.1705120114},
			volume = {114},
			year = {2017}
			}

			@article{OConnell2009,
			abstract = {The extent to which changes in brain activity can foreshadow human error is uncertain yet has important theoretical and practical implications. The present study examined the temporal dynamics of electrocortical signals preceding a lapse of sustained attention. Twenty-one participants performed a continuous temporal expectancy task, which involved continuously monitoring a stream of regularly alternating patterned stimuli to detect a rarely occurring target stimulus whose duration was 40{\%} longer. The stimulus stream flickered at a rate of 25 Hz to elicit a steady-state visual-evoked potential (SSVEP), which served as a continuous measure of basic visual processing. Increasing activity in the alpha band (8-14 Hz) was found beginning approximately 20 s before a missed target. This was followed by decreases in the amplitude of two event-related components over a short pretarget time frame: the frontal P3 (3-4 s) and contingent-negative variation (during the target interval). In contrast, SSVEP amplitude before hits and misses was closely matched, suggesting that the efficacy of ongoing basic visual processing was unaffected. Our results show that the specific neural signatures of attentional lapses are registered in the EEG up to 20 s before an error.},
			author = {O'Connell, Redmond G and Dockree, Paul M and Robertson, Ian H and Bellgrove, Mark a and Foxe, John J and Kelly, Simon P},
			doi = {10.1523/JNEUROSCI.5967-08.2009},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/O'Connell et al. - 2009 - Uncovering the neural signature of lapsing attention electrophysiological signals predict errors up to 20 s be.pdf:pdf},
			issn = {1529-2401},
			journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
			keywords = {Adult,Attention,Attention: physiology,Brain Mapping,Cerebral Cortex,Cerebral Cortex: physiology,Contingent Negative Variation,Contingent Negative Variation: physiology,Electroencephalography,Electroencephalography: methods,Evoked Potentials, Visual,Evoked Potentials, Visual: physiology,Female,Flicker Fusion,Flicker Fusion: physiology,Humans,Male,Pattern Recognition, Visual,Photic Stimulation,Photic Stimulation: methods,Psychophysics,Reaction Time,Reaction Time: physiology,Signal Detection, Psychological,Spectrum Analysis,Time Factors,Young Adult},
			month = {jul},
			number = {26},
			pages = {8604--11},
			pmid = {19571151},
			title = {{Uncovering the neural signature of lapsing attention: electrophysiological signals predict errors up to 20 s before they occur.}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/19571151},
			volume = {29},
			year = {2009}
			}
			@article{Friston2005,
			abstract = {Inferences about brain function, using neuroimaging data, rest on models of how the data were caused. These models can be quite diverse, ranging from conceptual models of functional anatomy to nonlinear mathematical models of hemodynamics. However, they all have to be internally consistent because they model the same thing. This consistency encompasses many levels of description and places constraints on the statistical models, adopted for data analysis, and the experimental designs they embody. The aim of this review is to introduce the key models used in imaging neuroscience and how they relate to each other. We start with anatomical models of functional brain architectures, which motivate some of the fundaments of neuroimaging. We then turn to basic statistical models (e.g., the general linear model) used for making classical and Bayesian inferences about where neuronal responses are expressed. By incorporating biophysical constraints, these basic models can be finessed and, in a dynamic setting, rendered causal. This allows us to infer how interactions among brain regions are mediated.},
			author = {Friston, Karl J.},
			doi = {10.1146/annurev.psych.56.091103.070311},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/48da11bda25c0372ebf3c5728494897e24b3c9a5.pdf:pdf},
			isbn = {0066-4308 (Print)$\backslash$n0066-4308 (Linking)},
			issn = {0066-4308},
			journal = {Annual Review of Psychology},
			keywords = {bayesian,causal,dynamic,fmri,hemodynamics,inference},
			number = {1},
			pages = {57--87},
			pmid = {15709929},
			title = {{Models of Brain Function in Neuroimaging}},
			url = {http://www.annualreviews.org/doi/10.1146/annurev.psych.56.091103.070311},
			volume = {56},
			year = {2005}
			}

			@article{Horrell2010,
			abstract = {INTRODUCTION: Preoccupation with drug and drug-related items is a typical characteristic of cocaine addicted individuals. It has been shown in multiple accounts that prolonged drug use has a profound effect on the EEG recordings of drug addicts when compared to controls during cue reactivity tests. Cue reactivity refers to a phenomenon in which individuals with a history of drug abuse exhibit excessive psychophysiological responses to cues associated with their drug of choice. One of the aims of this pilot study was to determine the presence of an attentional bias to preferentially process drug-related cues using evoked and induced gamma reactivity measures in cocaine addicts before and after biobehavioral treatment based on neurofeedback. Another aim was to show that central SMR amplitude increase and frontal theta control is possible in an experimental outpatient drug users group over 12 neurofeedback sessions. METHOD: Ten current cocaine abusers participated in this pilot research study using neurofeedback combined with Motivational Interviewing sessions. Eight of them completed all planned pre- and post -neurofeedback cue reactivity tests with event-related EEG recording and clinical evaluations. Cue reactivity test represented a visual oddball task with images from the International Affective Picture System and drug-related pictures. Evoked and induced gamma responses to target and non-target drug cues were analyzed using wavelet analysis. RESULTS: Outpatient subjects with cocaine addiction completed the biobehavioral intervention and successfully increased SMR while keeping theta practically unchanged in 12 sessions of neurofeedback training. The addition of Motivational Interviewing helped retain patients in the study. Clinical evaluations immediately after completion of the treatment showed decreased self-reports on depression and stress scores, and urine tests collaborated reports of decreased use of cocaine and marijuana. Effects of neurofeedback resulted in a lower EEG gamma reactivity to drug-related images in a post-neurofeedback cue reactivity test. In particular, evoked gamma showed decreases in power to non-target and to a lesser extent target drug-related cues at all topographies (left, right, frontal, parietal, medial, inferior); while induced gamma power decreased globally to both target and non-target drug cues. Our findings supported our hypothesis that gamma band cue reactivity measures are sufficiently sensitive functional outcomes of neurofeedback treatment. Both evoked and induced gamma measures were found capable to detect changes in responsiveness to both target and non-target drug cues. CONCLUSION: Our study emphasizes the utility of cognitive neuroscience methods based on EEG gamma band measures for the assessment of the functional outcomes of neurofeedback-based biobehavioral interventions for cocaine use disorders. This approach may have significant potential for identifying both physiological and clinical markers of treatment progress. The results confirmed our prediction that EEG changes achieved with neurofeedback training will be accompanied by positive EEG outcomes in a cue reactivity and clinical improvements.},
			author = {Horrell, Timothy and El-Baz, Ayman and Baruth, Joshua and Tasman, Allan and Sokhadze, Guela and Stewart, Christopher and Sokhadze, Estate},
			doi = {10.1080/10874208.2010.501498},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Horrell et al. - 2010 - Neurofeedback Effects on Evoked and Induced EEG Gamma Band Reactivity to Drug-related Cues in Cocaine Addiction.pdf:pdf},
			issn = {1087-4208},
			journal = {Journal of neurotherapy},
			month = {jul},
			number = {3},
			pages = {195--216},
			pmid = {20976131},
			title = {{Neurofeedback Effects on Evoked and Induced EEG Gamma Band Reactivity to Drug-related Cues in Cocaine Addiction.}},
			url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2957125{\&}tool=pmcentrez{\&}rendertype=abstract},
			volume = {14},
			year = {2010}
			}

			@article{Clayson2013,
			abstract = {There is considerable variability in the quantification of event-related potential (ERP) amplitudes and latencies. We examined susceptibility of ERP quantification measures to incremental increases in background noise through published ERP data and simulations. Measures included mean amplitude, adaptive mean, peak amplitude, peak latency, and centroid latency. Results indicated mean amplitude was the most robust against increases in background noise. The adaptive mean measure was more biased, but represented an efficient estimator of the true ERP signal particularly for individual-subject latency variability. Strong evidence is provided against using peak amplitude. For latency measures, the peak latency measure was less biased and less efficient than the centroid latency measurement. Results emphasize the prudence in reporting the number of trials retained for averaging as well as noise estimates for groups and conditions when comparing ERPs.},
			author = {Clayson, Peter E and Baldwin, Scott a and Larson, Michael J},
			doi = {10.1111/psyp.12001},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Clayson, Baldwin, Larson - 2013 - How does noise affect amplitude and latency measurement of event-related potentials (ERPs) A methodolo.pdf:pdf},
			issn = {1540-5958},
			journal = {Psychophysiology},
			keywords = {Acoustic Stimulation,Computer Simulation,Data Interpretation, Statistical,Electroencephalography,Evoked Potentials,Evoked Potentials: physiology,Humans,Noise},
			month = {feb},
			number = {2},
			pages = {174--86},
			pmid = {23216521},
			title = {{How does noise affect amplitude and latency measurement of event-related potentials (ERPs)? A methodological critique and simulation study.}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/23216521},
			volume = {50},
			year = {2013}
			}

			@article{Mackay2004,
			author = {Mackay, J and Mensah, G},
			journal = {World Health Organization (WHO)},
			title = {{The atlas of heart disease and stroke. Global burden of stroke.}},
			year = {2004}
			}

			@article{Randal2004,
			author = {Randal, Anthony and Lobaugh, Nancy J},
			doi = {10.1016/j.neuroimage.2004.07.020},
			file = {:Users/lorenafreitas/Library/Containers/com.apple.mail/Data/Library/Mail Downloads/3DF8EF77-794B-4AF7-8CEC-2844413C9A0C/McIntosh, Lobaugh{\_}2004{\_}Partial least squares analysis of neuroimaging data Applications and advances.pdf:pdf},
			journal = {NeuroImage},
			keywords = {event-related potentials,functional mri,magnetoencephalography,multivariate statistics,nonpara-,positron emission tomography},
			pages = {250--263},
			title = {{Partial least squares analysis of neuroimaging data : applications and advances}},
			volume = {23},
			year = {2004}
			}

			@article{Ploner2009,
			abstract = {Pain is a complex experience subserved by an extended network of brain areas. However, the functional integration among these brain areas, i.e., how they interact and communicate to generate a coherent pain percept and an adequate behavioral response is largely unknown. Here, we used magnetoencephalography to investigate functional integration among pain-related cortical activations in terms of Granger causality and compared it with tactile-related activations. The results show causal influences of primary somatosensory cortex on secondary somatosensory cortex for tactile-related but not for pain-related activations, which supports the proposition of a partially parallel organization of pain processing in the human brain. Furthermore, during a simple reaction time task, the strength of causal influences between somatosensory areas but not the latencies between activations correlated significantly with the speed of reaction times. These findings show how the analysis of functional integration complements traditional analyses of electrophysiological data and provides novel and behaviorally relevant information about the organization of the human pain system.},
			author = {Ploner, Markus and Schoffelen, Jan Mathijs and Schnitzler, Alfons and Gross, Joachim},
			doi = {10.1002/hbm.20826},
			file = {:Users/lorenafreitas/Downloads/Ploner{\_}et{\_}al-2009-Human{\_}Brain{\_}Mapping.pdf:pdf},
			isbn = {1097-0193 (Electronic)$\backslash$r1065-9471 (Linking)},
			issn = {10659471},
			journal = {Human Brain Mapping},
			keywords = {Connectivity,Cutaneous laser stimulation,Granger causality,Magnetoencephalography,Pain,Somatosensory cortices,Tactile},
			number = {12},
			pages = {4025--4032},
			pmid = {19479728},
			title = {{Functional integration within the human pain system as revealed by Granger causality}},
			volume = {30},
			year = {2009}
			}

			@article{LeGuen2013,
			author = {{Le Guen}, Morgan and Liu, Ngai and Bourgeois, Eric and Chazot, Thierry and Sessler, Daniel I. and Rouby, Jean-Jacques and Fischler, Marc},
			doi = {10.1007/s00134-012-2762-2},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Le Guen et al. - 2013 - Automated sedation outperforms manual administration of propofol and remifentanil in critically ill patients wit.pdf:pdf},
			issn = {0342-4642},
			journal = {Intensive Care Medicine},
			number = {3},
			pages = {454--462},
			title = {{Automated sedation outperforms manual administration of propofol and remifentanil in critically ill patients with deep sedation: a randomized phase II trial}},
			url = {http://link.springer.com/10.1007/s00134-012-2762-2},
			volume = {39},
			year = {2013}
			}

			@article{Nelson2000,
			author = {Nelson, Charles A and Monk, Christopher S and Lin, Joseph and Carver, Leslie J and Thomas, Kathleen M and Truwit, Charles L},
			file = {:Users/lorenafreitas/Downloads/nelson2000.pdf:pdf},
			journal = {Developmental Psychology},
			number = {1},
			pages = {109--116},
			title = {{Functional Neuroanatomy of Spatial Working Memory in Children}},
			volume = {36},
			year = {2000}
			}

			@book{Wood2015,
			author = {Wood, Matt A.},
			doi = {10.1088/978-1-6270-5620-5},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Wood - 2015 - Python and Matplotlib Essentials for Scientists and Engineers.pdf:pdf},
			isbn = {978-1-6270-5620-5},
			publisher = {Morgan {\&} Claypool Publishers},
			title = {{Python and Matplotlib Essentials for Scientists and Engineers}},
			url = {http://iopscience.iop.org/book/978-1-6270-5620-5},
			year = {2015}
			}

			@article{Akcakaya2014,
			author = {Akcakaya, Murat and Peters, Betts and Moghadamfalahi, Mohammad and Member, Student and Mooney, Aimee R and Orhan, Umut and Oken, Barry and Erdogmus, Deniz and Member, Senior and Fried-oken, Melanie},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Akcakaya et al. - 2014 - Noninvasive Brain â Computer Interfaces for Augmentative and Alternative Communication.pdf:pdf},
			pages = {31--49},
			title = {{Noninvasive Brain â Computer Interfaces for Augmentative and Alternative Communication}},
			volume = {7},
			year = {2014}
			}

			@article{GonzalezCastillo2018,
			abstract = {The temporal evolution of functional connectivity (FC) within the confines of individual scans is nowadays often explored with functional neuroimaging. This is particularly true for resting-state; yet, FC-dynamics have also been investigated as subjects engage on numerous tasks. It is these research efforts that constitute the core of this survey. First, empirical observations on how FC differs between task and restâindependent of temporal scaleâare reviewed, as they underscore how, despite overall preservation of network topography, the brain's FC does reconfigure in systematic ways to accommodate task demands. Next, reports on the relationships between instantaneous FC and perception/performance in subsequent trials are discussed. Similarly, research where different aspects of task-concurrent FC-dynamics are explored or utilized to predict ongoing mental states are also examined. The manuscript finishes with an incomplete list of challenges that hopefully fuels future work in this vibrant area of neuroscientific research. Overall, this review concludes that task-concurrent FC-dynamics, when properly characterized, are relevant to behavior, and that their translational value holds considerable promise.},
			author = {Gonzalez-Castillo, Javier and Bandettini, Peter A.},
			doi = {10.1016/j.neuroimage.2017.08.006},
			file = {:Users/lorenafreitas/Downloads/1-s2.0-S1053811917306535-main.pdf:pdf},
			issn = {10959572},
			journal = {NeuroImage},
			keywords = {Connectivity dynamics,Dynamic functional connectivity,Functional connectivity states,Task vs. rest,Task-concurrent functional connectivity},
			number = {May 2017},
			pages = {526--533},
			pmid = {28780401},
			publisher = {Elsevier Ltd},
			title = {{Task-based dynamic functional connectivity: Recent findings and open questions}},
			url = {https://doi.org/10.1016/j.neuroimage.2017.08.006},
			volume = {180},
			year = {2018}
			}

			@article{Hu2013,
			abstract = {UNLABELLED: Nociceptive stimuli can induce a transient suppression of electroencephalographic oscillations in the alpha frequency band (ie, alpha event-related desynchronization, $\alpha$-ERD). Here we investigated whether $\alpha$-ERD could be functionally distinguished in 2 temporally and spatially segregated subcomponents as suggested by previous studies. In addition, we tested whether the degree of dependence of nociceptive-induced $\alpha$-ERD magnitude on the prestimulus $\alpha$-power would have been larger than the degree of dependence on the poststimulus $\alpha$-power. Our findings confirmed the dissociation between a sensory-related $\alpha$-ERD maximally distributed over contralateral central electrodes, and a task-related $\alpha$-ERD (possibly affected by motor-related activity), maximally distributed at posterior parietal and occipital electrodes. The cortical sources of these activities were estimated to be located at the level of sensorimotor and bilateral occipital cortices, respectively. Importantly, the time course of the $\alpha$-ERD revealed that functional segregation emerged only at late latencies (400 to 750 ms) whereas topographic similarity was observed at earlier latencies (250 to 350 ms). Furthermore, the nociceptive-induced $\alpha$-ERD magnitude was significantly more dependent on prestimulus than poststimulus $\alpha$-power. Altogether these findings provide direct evidence that the nociceptive-induced $\alpha$-ERD reflects the summation of sensory-related and task-related cortical processes, and that prestimulus fluctuations can remarkably influence the non-phase-locked nociceptive $\alpha$-ERD.$\backslash$n$\backslash$nPERSPECTIVE: Present results extend the functional understanding of $\alpha$-oscillation suppression during pain perception and demonstrate the influence of prestimulus variability on this cortical phenomenon. This work has the potential to guide pain clinicians in a more accurate interpretation on physiological and psychological modulations of $\alpha$-oscillations.},
			author = {Hu, Li and Peng, Weiwei and Valentini, Elia and Zhang, Zhiguo and Hu, Yong},
			doi = {10.1016/j.jpain.2012.10.008},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Hu et al. - 2013 - Functional features of nociceptive-induced suppression of alpha band electroencephalographic oscillations.pdf:pdf},
			issn = {1528-8447},
			journal = {The journal of pain : official journal of the American Pain Society},
			keywords = {Adult,Alpha Rhythm,Alpha Rhythm: physiology,Data Interpretation, Statistical,Electroencephalography,Electroencephalography Phase Synchronization,Female,Humans,Male,Nociception,Nociception: physiology,Occipital Lobe,Occipital Lobe: physiology,Parietal Lobe,Parietal Lobe: physiology,Reaction Time,Reaction Time: physiology,Young Adult},
			month = {jan},
			number = {1},
			pages = {89--99},
			pmid = {23273836},
			title = {{Functional features of nociceptive-induced suppression of alpha band electroencephalographic oscillations.}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/23273836},
			volume = {14},
			year = {2013}
			}

			@article{Basar2012,
			abstract = {AIM OF THE REVIEW: Questions related to the genesis and functional correlates of the brain's alpha oscillations around 10Hz (Alpha) are one of the fundamental research areas in neuroscience. In recent decades, analysis of this activity has been not only the focus of interest for description of sensory-cognitive processes, but has also led to trials for establishing new hypotheses. The present review and the companion review aim to constitute an ensemble of "reasonings and suggestions" to understand alpha oscillations based on a wide range of accumulated findings rather than a trial to launch a new "alpha theory".$\backslash$n$\backslash$nSURVEYED DESCRIPTIONS RELATED TO PHYSIOLOGY AND BRAIN FUNCTION: The review starts with descriptions of earlier extracellular recordings, field potentials and also considers earlier alpha hypotheses. Analytical descriptions of evoked and event-related responses, event-related desynchronization, the relationship between spontaneous activity and evoked potentials, aging brain, pathology and alpha response in cognitive impairment are in the content of this review. In essence, the gamut of the survey includes a multiplicity of evidence on functional correlates in sensory processing, cognition, memory and vegetative system, including the spinal cord and heart.},
			author = {BaÅar, Erol},
			doi = {10.1016/j.ijpsycho.2012.07.002},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/BaÅar - 2012 - A review of alpha activity in integrative brain function Fundamental physiology, sensory coding, cognition and pathology.pdf:pdf},
			issn = {1872-7697},
			journal = {International Journal of Psychophysiology},
			keywords = {Alpha Rhythm,Alpha Rhythm: physiology,Animals,Behavior,Behavior: physiology,Brain,Brain: cytology,Brain: growth {\&} development,Brain: physiology,Cerebellum,Cerebellum: cytology,Cerebellum: physiology,Cognition,Cognition: physiology,Electroencephalography,Evoked Potentials, Visual,Evoked Potentials, Visual: physiology,Heart,Heart: innervation,Humans,Mental Disorders,Mental Disorders: physiopathology,Mental Disorders: psychology,Mental Processes,Mental Processes: physiology,Neurons,Neurons: physiology,Spinal Cord,Spinal Cord: cytology,Spinal Cord: physiology,Sympathetic Nervous System,Sympathetic Nervous System: physiology},
			month = {oct},
			number = {1},
			pages = {1--24},
			pmid = {22820267},
			title = {{A review of alpha activity in integrative brain function: Fundamental physiology, sensory coding, cognition and pathology}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/22820267},
			volume = {86},
			year = {2012}
			}

			@article{Rinehart2015,
			abstract = {Research into closed-loop systems (CLS) in health care has been increasing steadily over the past 2 decades, with a significant expansion over the past 5 years. With the improvement in technology and realization by health care providers and companies of the potential for patient care, the applications of CLS have broadened to include medication delivery, ventilation control, temperature control, hemody- namic management, and fluid administration to name a few. Anesthesiologists have an opportunity to capitalize on CLS to improve their collective practice. They work in dynamic environments where they manage physiological parameters, interact with devices and patient monitors, and are constantly managing information from multiple sources, which necessitates an ability to multitask.1 Moreover, anesthesiologists among all physician specialties spend the largest portion of their time directly managing pharmacologic interventions, including vasoactive, narcotic, and sedative-hypnotic drugs, which may have serious consequences with incorrect dosing. Regardless of vigilance or intent, multiple studies have shown that humans have a limited capacity for multitasking and attention over prolonged periods. CLS have the ability to reduce the need for multitasking and the variation both within and between individual providers' management strategies by performing repetitive tasks with some degree of autonomy and allowing the anesthesiologist to focus on high-level decision-making. Several review articles in recent years have discussed CLS in anesthesiology and critical care and have focused on specific aspects of care: anesthetic agents, hemodynamics, insulin therapy, and fluid management. In this article, we focus on the specific benefits and limitations of closed-loop pharmacologic applications in anesthesiology and critical care.},
			author = {Rinehart, Joseph and Canales, Cecilia},
			doi = {10.1097/AIA.0000000000000051},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Rinehart, Canales - 2015 - Closed-loop pharmacology in anesthesia and critical care benefits and limitations.pdf:pdf},
			issn = {1537-1913},
			journal = {International anesthesiology clinics},
			number = {2},
			pages = {91--101},
			pmid = {25807021},
			title = {{Closed-loop pharmacology in anesthesia and critical care: benefits and limitations.}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/25807021},
			volume = {53},
			year = {2015}
			}

			@article{Jo2013,
			author = {Jo, Hang Joon and Gotts, Stephen J and Reynolds, Richard C and Bandettini, Peter A and Martin, Alex and Cox, Robert W and Saad, Ziad S},
			file = {:Users/lorenafreitas/Downloads/935154.pdf:pdf},
			journal = {Journal of Applied Mathematics},
			title = {{Effective Preprocessing Procedures Virtually Eliminate Distance-Dependent Motion Artifacts in Resting State FMRI}},
			volume = {2013},
			year = {2013}
			}

			@article{Kozberg2013,
			author = {Kozberg, Mariel G and Chen, Brenda R and Deleo, Sarah E and Bouchard, Matthew B and Hillman, Elizabeth M C},
			doi = {10.1073/pnas.1212785110/-/DCSupplemental.www.pnas.org/cgi/doi/10.1073/pnas.1212785110},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/f438f510388a8c13b8a0e1c1fd68df81e4a80cf7.pdf:pdf},
			journal = {Proceedings of the National Academy of Sciences},
			number = {11},
			pages = {4380--4385},
			title = {{Resolving the transition from negative to positive blood oxygen level-dependent responses in the developing brain}},
			url = {http://www.pnas.org/content/110/11/4380.full.pdf},
			volume = {110},
			year = {2013}
			}

			@article{Fox2007b,
			author = {Fox, Michael D and Snyder, Abraham Z and Vincent, Justin L and Raichle, Marcus E},
			doi = {10.1016/j.neuron.2007.08.023},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/8d90dd3949a6518d4fec7bc7ef4c35ae3f9665fe.pdf:pdf},
			pages = {171--184},
			title = {{Intrinsic Fluctuations within Cortical Systems Account for Intertrial Variability in Human Behavior}},
			url = {http://ac.els-cdn.com/S0896627307006666/1-s2.0-S0896627307006666-main.pdf?{\_}tid=664d9406-370a-11e7-a365-00000aacb35f{\&}acdnat=1494590526{\_}7d280daec83e10e3e0acf3b9e8d3fcb9},
			year = {2007}
			}

			@article{Howseman1999,
			abstract = {Functional magnetic resonance imaging (fMRI) is a widely used technique for generating images or maps of human brain activity. The applications of the technique are widespread in cognitive neuroscience and it is hoped they will eventually extend into clinical practice. The activation signal measured with fMRI is predicated on indirectly measuring changes in the concentration of deoxyhaemoglobin which arise from an increase in blood oxygenation in the vicinity of neuronal firing. The exact mechanisms of this blood oxygenation level dependent (BOLD) contrast are highly complex. The signal measured is dependent on both the underlying physiological events and the imaging physics. BOLD contrast, although sensitive, is not a quantifiable measure of neuronal activity. A number of different imaging techniques and parameters can be used for fMRI, the choice of which depends on the particular requirements of each functional imaging experiment. The high-speed MRI technique, echo-planar imaging provides the basis for most fMRI experiments. The problems inherent to this method and the ways in which these may be overcome are particularly important in the move towards performing functional studies on higher field MRI systems. Future developments in techniques and hardware are also likely to enhance the measurement of brain activity using MRI.},
			author = {Howseman, a M and Bowtell, R W},
			doi = {10.1098/rstb.1999.0473},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/9b4e63488d3ae48fd449560d73898f3b42a2dd6e.pdf:pdf},
			issn = {0962-8436},
			journal = {Philosophical transactions of the Royal Society of London. Series B, Biological sciences},
			keywords = {and practical di,bold contrast,both papers in this,brain mapping,culties,echo-planar imaging,et al,extent to which the,fmri,issue,mcguire,psychiatric disorders,see fu,see petersson,statistical,the,theoretical,this issue},
			number = {1387},
			pages = {1179--1194},
			pmid = {10466145},
			title = {{Functional magnetic resonance imaging: imaging techniques and contrast mechanisms.}},
			url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1692627/pdf/10466145.pdf},
			volume = {354},
			year = {1999}
			}

			@article{schmeling2016,
			author = {Schmeling, Andreas and Dettmeyer, Reinhard and Rudolf, Ernst and Vieth, Volker and Geserick, Gunther},
			doi = {10.3238/arztebl.2016.0044},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/537795213a32745904be92ec8c5a55f2991c52a1.pdf:pdf},
			journal = {Deutsches {\"{A}}rzteblatt International},
			pages = {44--50},
			title = {{Forensic Age Estimation}},
			url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4760148/pdf/Dtsch{\_}Arztebl{\_}Int-113-0044.pdf},
			volume = {113},
			year = {2016}
			}

			@article{Biswas2013,
			abstract = {OBJECTIVE: The objective of this study was to compare the feasibility of closed-loop anesthesia delivery with manual control of propofol in pediatric patients during cardiac surgery. METHODS: Forty ASA II-III children, undergoing elective cardiac surgery under cardiopulmonary bypass (CPB) in a tertiary care hospital, were randomized to receive propofol either through a closed-loop anesthesia delivery system (CL group) or through traditional manual control (manual group) to achieve a target BIS of 50. Patients were induced and subsequently maintained with a propofol infusion. The propofol usage and the efficacy of closed-loop system in controlling BIS within Â±10 of the target were compared with that of manual control. RESULTS: The maintenance of BIS within Â±10 of target and intraoperative hemodynamic stability were similar between the two groups. However, induction dose of propofol was less in the CL group (2.06 Â± 0.79 mg{\textperiodcentered}kg(-1) ) than the manual group (2.95 Â± 1.03 mg{\textperiodcentered}kg(-1) ) (P = 0.006) with less overshoot of BIS during induction in the closed-loop group (P = 0.007). Total propofol used in the off-CPB period was less in the CL group (6.29 Â± 2.48 mg{\textperiodcentered}kg(-1) h(-1) vs 7.82 Â± 2.1 mg{\textperiodcentered}kg(-1) h(-1) ) (P = 0.037). Phenylephrine use in the pre-CPB period was more in the manual group (16.92 Â± 10.92 $\mu$g{\textperiodcentered}kg(-1) vs 5.79 Â± 5.98 $\mu$g{\textperiodcentered}kg(-1) ) (P = 0.014). Manual group required a median of 18 (range 8-29) dose adjustments per hour, while the CL group required none. CONCLUSION: This study demonstrated the feasibility of closed-loop controlled propofol anesthesia in children, even in challenging procedures such as cardiac surgery. Closed-loop system needs further and larger evaluation to establish its safety and efficacy.},
			author = {Biswas, Indranil and Mathew, Preethy J. and Singh, Rana S. and Puri, Goverdhan D.},
			doi = {10.1111/pan.12265},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Biswas et al. - 2013 - Evaluation of closed-loop anesthesia delivery for propofol anesthesia in pediatric cardiac surgery.pdf:pdf},
			issn = {11555645},
			journal = {Paediatric Anaesthesia},
			keywords = {BIS,cardiac surgery,closed-loop anesthesia,propofol; pediatric},
			number = {12},
			pages = {1145--1152},
			pmid = {24118468},
			title = {{Evaluation of closed-loop anesthesia delivery for propofol anesthesia in pediatric cardiac surgery}},
			volume = {23},
			year = {2013}
			}

			@article{Dyson2008,
			abstract = {The motivation for this study was to obtain candidate electrode sites for use in online self-paced brain-computer interfaces and preliminary classification results for comparison to online tests. Six mental tasks were tested for classification against an idle state. Data representing the idle state was collected in association with active mental task data during each recording session. Features were extracted in two representations, band power and reflection coefficients. A sequential forward floating search algorithm was used to obtain prevailing electrode-feature pairs for each subject-task combination under two conditions: maximising classification accuracy and maximising mean trial accuracy. Methods used to select electrode-feature combinations are found to lead to differing electrode sites in a number of task-feature combinations. An across task prevalence towards electrodes positioned in the left frontal hemisphere is observed when maximising classification accuracy.},
			author = {Dyson, Matthew and Sepulveda, Francisco and Gan, John Q},
			doi = {10.1109/IEMBS.2008.4650206},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Dyson, Sepulveda, Gan - 2008 - Mental task classification against the idle state a preliminary investigation.pdf:pdf},
			isbn = {9781424418152},
			issn = {1557-170X},
			journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference},
			keywords = {Adult,Algorithms,Attention,Brain,Brain: physiology,Electrodes,Electroencephalography,Electroencephalography: methods,Equipment Design,Humans,Imagery (Psychotherapy),Male,Models, Theoretical,Psychomotor Performance,Psychomotor Performance: physiology,Reproducibility of Results,Software,User-Computer Interface},
			month = {jan},
			pages = {4473--7},
			pmid = {19163709},
			title = {{Mental task classification against the idle state: a preliminary investigation.}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/19163709},
			volume = {2008},
			year = {2008}
			}

			@article{Roy2013,
			abstract = {Current mental state monitoring systems, a.k.a. passive brain-computer interfaces (pBCI), allow one to perform a real-time assessment of an operator's cognitive state. In EEG-based systems, typical measurements for workload level assessment are band power estimates in several frequency bands. Mental fatigue, arising from growing time-on-task (TOT), can significantly affect the distribution of these band power features. However, the impact of mental fatigue on workload (WKL) assessment has not yet been evaluated. With this paper we intend to help fill in this lack of knowledge by analyzing the influence of WKL and TOT on EEG band power features, as well as their interaction and its impact on classification performance. Twenty participants underwent an experiment that modulated both their WKL (low/high) and time spent on the task (short/long). Statistical analyses were performed on the EEG signals, behavioral and subjective data. They revealed opposite changes in alpha power distribution between WKL and TOT conditions, as well as a decrease in WKL level discriminability with increasing TOT in both number of statistical differences in band power and classification performance. Implications for pBCI systems and experimental protocol design are discussed.},
			author = {Roy, Raphaelle N and Bonnet, Stephane and Charbonnier, Sylvie and Campagne, Aurelie},
			doi = {10.1109/EMBC.2013.6611070},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Roy et al. - 2013 - Mental fatigue and working memory load estimation interaction and implications for EEG-based passive BCI.pdf:pdf},
			isbn = {9781457702167},
			issn = {1557-170X},
			journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference},
			keywords = {Brain-computer/machine interface,Human performance - Attention and vigilance,Human performance - Cognition,analysis,statistical,statistics},
			mendeley-tags = {analysis,statistical,statistics},
			month = {jan},
			pages = {6607--10},
			pmid = {24111257},
			title = {{Mental fatigue and working memory load estimation: interaction and implications for EEG-based passive BCI.}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/24111257},
			volume = {2013},
			year = {2013}
			}

			@article{Shi2014,
			abstract = {Atlas construction generally includes first an image registration step to normalize all images into a common space and then an atlas building step to fuse the information from all the aligned images. Although numerous atlas construction studies have been performed to improve the accuracy of the image registration step, unweighted or simply weighted average is often used in the atlas building step. In this article, we propose a novel patch-based sparse representation method for atlas construction after all images have been registered into the common space. By taking advantage of local sparse representation, more anatomical details can be recovered in the built atlas. To make the anatomical structures spatially smooth in the atlas, the anatomical feature constraints on group structure of representations and also the overlapping of neighboring patches are imposed to ensure the anatomical consistency between neighboring patches. The proposed method has been applied to 73 neonatal MR images with poor spatial resolution and low tissue contrast, for constructing a neonatal brain atlas with sharp anatomical details. Experimental results demonstrate that the proposed method can significantly enhance the quality of the constructed atlas by discovering more anatomical details especially in the highly convoluted cortical regions. The resulting atlas demonstrates superior performance of our atlas when applied to spatially normalizing three different neonatal datasets, compared with other start-of-the-art neonatal brain atlases.},
			author = {Shi, Feng and Wang, Li and Wu, Guorong and Li, Gang and Gilmore, John H. and Lin, Weili and Shen, Dinggang},
			doi = {10.1002/hbm.22502},
			file = {:Users/lorenafreitas/Downloads/Shi{\_}et{\_}al-2014-Human{\_}Brain{\_}Mapping.pdf:pdf},
			isbn = {1097-0193},
			issn = {10970193},
			journal = {Human Brain Mapping},
			keywords = {Brain atlases,Brain development,MRI template,Neonatal brain,Spatial normalization,Super resolution},
			number = {9},
			pages = {4663--4677},
			pmid = {24638883},
			title = {{Neonatal atlas construction using sparse representation}},
			volume = {35},
			year = {2014}
			}

			@article{Perani2011,
			author = {Perani, Daniela and Saccuman, Maria C and Scifo, Paola and Anwander, Alfred and Spada, Danilo and Baldoli, Cristina},
			doi = {10.1073/pnas.1102991108/-/DCSupplemental.www.pnas.org/cgi/doi/10.1073/pnas.1102991108},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/d7c5bcfe57949ae59f0d6a37e3d39ef8ed9976ca.pdf:pdf},
			journal = {Proceedings of the National Academy of Sciences},
			number = {38},
			pages = {16056--16061},
			title = {{Neural language networks at birth}},
			url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3179044/pdf/pnas.201102991.pdf},
			volume = {108},
			year = {2011}
			}

			@article{Kazemi2007,
			abstract = {Commonly used brain templates are based on adults' or children's brains. In this study, we create a neonatal brain template. This becomes necessary because of the pronounced differences not only in size but even more importantly in geometrical proportions of the brains of adults and children as compared to the ones of newborns. The template is created based on high resolution T1 magnetic resonance images of 7 individuals with gestational ages between 39 and 42 weeks at the dates of examination. As usual, the created template presents two characteristics in a single image: an average intensity and an average shape. The normalization process to map subjects to the same space is done using SPM2 (Statistical Parametric Mapping) and its deformation toolbox. It consists of two steps: an affine and a nonlinear registration for global and local alignments, respectively. The template was evaluated by (i) study of anatomical local deviations and (ii) amount of local deformations of brain tissues in normalized neonatal images. The extracted results were compared with the ones obtained by normalization using adult and pediatric templates. It was shown that the application of our neonatal brain template for alignment of neonatal images results in a pronounced increase in performance of the normalization procedure as indicated by reduction of deviation of anatomical equivalent structures. The neonatal atlas template is freely downloadable from http://www.u-picardie.fr/labo/GRAMFC. {\textcopyright} 2007 Elsevier Inc. All rights reserved.},
			author = {Kazemi, Kamran and Moghaddam, Hamid Abrishami and Grebe, Reinhard and Gondry-Jouet, Catherine and Wallois, Fabrice},
			doi = {10.1016/j.neuroimage.2007.05.004},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/70635e1b68e598602c162d9caa1adac83ce1655b.pdf:pdf},
			isbn = {1053-8119},
			issn = {10538119},
			journal = {NeuroImage},
			keywords = {MRI,Neonate,Spatial normalization,Stereotaxic space,Template},
			number = {2},
			pages = {463--473},
			pmid = {17560795},
			title = {{A neonatal atlas template for spatial normalization of whole-brain magnetic resonance images of newborns: Preliminary results}},
			url = {http://ac.els-cdn.com/S1053811907004284/1-s2.0-S1053811907004284-main.pdf?{\_}tid=586d6814-3fc2-11e7-89f9-00000aacb35f{\&}acdnat=1495549140{\_}72938b27551a1c82d1b9dd53fa869d2f},
			volume = {37},
			year = {2007}
			}

			@article{Yarrow2014,
			author = {Yarrow, Stuart and Razak, Khaleel A and Seitz, Aaron R and Serie, Peggy},
			doi = {10.1371/Citation},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Yarrow et al. - 2014 - Detecting and Quantifying Topography in Neural Maps `.pdf:pdf},
			number = {2},
			title = {{Detecting and Quantifying Topography in Neural Maps `}},
			volume = {9},
			year = {2014}
			}

			@article{Desmond2002,
			abstract = {Estimation of statistical power in functional MRI (fMRI) requires knowledge of the expected percent signal change between two conditions as well as estimates of the variability in percent signal change. Variability can be divided into intra-subject variability, reflecting noise within the time series, and inter-subject variability, reflecting subject-to-subject differences in activation. The purpose of this study was to obtain estimates of percent signal change and the two sources of variability from fMRI data, and then use these parameter estimates in simulation experiments in order to generate power curves. Of interest from these simulations were conclusions concerning how many subjects are needed and how many time points within a scan are optimal in an fMRI study of cognitive function. Intra-subject variability was estimated from resting conditions, and inter-subject variability and percent signal change were estimated from verbal working memory data. Simulations derived from these parameters illustrate how percent signal change, intra- and inter-subject variability, and number of time points affect power. An empirical test experiment, using fMRI data acquired during somatosensory stimulation, showed good correspondence between the simulation-based power predictions and the power observed within somatosensory regions of interest. Our analyses suggested that for a liberal threshold of 0.05, about 12 subjects were required to achieve 80{\%} power at the single voxel level for typical activations. At more realistic thresholds, that approach those used after correcting for multiple comparisons, the number of subjects doubled to maintain this level of power. ?? 2002 Elsevier Science B.V. All rights reserved.},
			author = {Desmond, John E. and Glover, Gary H.},
			doi = {10.1016/S0165-0270(02)00121-8},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/db4c959e5a791e88a4cad56f9c0f305ce41948b6.pdf:pdf},
			isbn = {0165-0270 (Print)$\backslash$n0165-0270 (Linking)},
			issn = {01650270},
			journal = {Journal of Neuroscience Methods},
			keywords = {Neuroimaging,Power,Sample size,Statistics,fMRI},
			number = {2},
			pages = {115--128},
			pmid = {12204303},
			title = {{Estimating sample size in functional MRI (fMRI) neuroimaging studies: Statistical power analyses}},
			url = {http://ac.els-cdn.com/S0165027002001218/1-s2.0-S0165027002001218-main.pdf?{\_}tid=77e49016-58df-11e6-a6fa-00000aab0f02{\&}acdnat=1470162929{\_}89ddae1f7ef5dd0f8ac4fe04a5525b05},
			volume = {118},
			year = {2002}
			}

			@article{Smallwood2007b,
			abstract = {In a recent review, we suggested that an important aspect of mind-wandering is whether participants are aware that they are off task (Smallwood {\&} Schooler, 2006). We tested this hypothesis by examining the information-processing correlates of mind wandering with and without awareness in a task requiring participants to encode words and detect targets with either a high or a low probability. Target detection was measured via response inhibition. Mind wandering in the absence of awareness was associated with a failure to supervise task performance, as indicated by short RTs, and was predictive of failures in response inhibition. Under conditions of low target probability, mind wandering was associated with a relative absence of the influence of recollection at retrieval. The results are consistent with the notion that mind wandering involves a state of decoupled attention and emphasizes the importance of meta-awareness of off-task episodes in determining the consequences of these mental states.},
			author = {Smallwood, Jonathan and McSpadden, Merrill and Schooler, Jonathan W},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Smallwood, McSpadden, Schooler - 2007 - The lights are on but no one's home meta-awareness and the decoupling of attention when the mind.pdf:pdf},
			issn = {1069-9384},
			journal = {Psychonomic bulletin {\&} review},
			keywords = {Adolescent,Adult,Attention,Awareness,Humans,Probability,Reaction Time,Reproducibility of Results,Speech,Thinking,Thinking: physiology},
			month = {jun},
			number = {3},
			pages = {527--33},
			pmid = {17874601},
			title = {{The lights are on but no one's home: meta-awareness and the decoupling of attention when the mind wanders.}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/17874601},
			volume = {14},
			year = {2007}
			}

			@article{Monti2010,
			abstract = {BACKGROUND: The differential diagnosis of disorders of consciousness is challenging. The rate of misdiagnosis is approximately 40{\%}, and new methods are required to complement bedside testing, particularly if the patient's capacity to show behavioral signs of awareness is diminished. METHODS: At two major referral centers in Cambridge, United Kingdom, and Liege, Belgium, we performed a study involving 54 patients with disorders of consciousness. We used functional magnetic resonance imaging (MRI) to assess each patient's ability to generate willful, neuroanatomically specific, blood-oxygenation-level-dependent responses during two established mental-imagery tasks. A technique was then developed to determine whether such tasks could be used to communicate yes-or-no answers to simple questions. RESULTS: Of the 54 patients enrolled in the study, 5 were able to willfully modulate their brain activity. In three of these patients, additional bedside testing revealed some sign of awareness, but in the other two patients, no voluntary behavior could be detected by means of clinical assessment. One patient was able to use our technique to answer yes or no to questions during functional MRI; however, it remained impossible to establish any form of communication at the bedside. CONCLUSIONS: These results show that a small proportion of patients in a vegetative or minimally conscious state have brain activation reflecting some awareness and cognition. Careful clinical examination will result in reclassification of the state of consciousness in some of these patients. This technique may be useful in establishing basic communication with patients who appear to be unresponsive.},
			author = {Monti, Martin M and Vanhaudenhuyse, Audrey and Coleman, Martin R and Boly, Melanie and Pickard, John D and Tshibanda, Luaba and Owen, Adrian M and Laureys, Steven},
			doi = {10.1056/NEJMoa0905370},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Monti et al. - 2010 - Willful modulation of brain activity in disorders of consciousness.pdf:pdf},
			issn = {1533-4406},
			journal = {The New England journal of medicine},
			keywords = {80 and over,Adult,Aged,Awareness,Brain,Brain: metabolism,Communication,Female,Humans,Imagination,Magnetic Resonance Imaging,Male,Mental Processes,Middle Aged,Persistent Vegetative State,Persistent Vegetative State: diagnosis,Persistent Vegetative State: metabolism,Persistent Vegetative State: psychology,Young Adult},
			month = {feb},
			number = {7},
			pages = {579--89},
			pmid = {20130250},
			title = {{Willful modulation of brain activity in disorders of consciousness.}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/20130250},
			volume = {362},
			year = {2010}
			}

			@article{Cincotti2008,
			abstract = {High-resolution electroencephalographic (HREEG) techniques allow estimation of cortical activity based on non-invasive scalp potential measurements, using appropriate models of volume conduction and of neuroelectrical sources. In this study we propose an application of this body of technologies, originally developed to obtain functional images of the brain's electrical activity, in the context of brain-computer interfaces (BCI). Our working hypothesis predicted that, since HREEG pre-processing removes spatial correlation introduced by current conduction in the head structures, by providing the BCI with waveforms that are mostly due to the unmixed activity of a small cortical region, a more reliable classification would be obtained, at least when the activity to detect has a limited generator, which is the case in motor related tasks. HREEG techniques employed in this study rely on (i) individual head models derived from anatomical magnetic resonance images, (ii) distributed source model, composed of a layer of current dipoles, geometrically constrained to the cortical mantle, (iii) depth-weighted minimum L(2)-norm constraint and Tikhonov regularization for linear inverse problem solution and (iv) estimation of electrical activity in cortical regions of interest corresponding to relevant Brodmann areas. Six subjects were trained to learn self modulation of sensorimotor EEG rhythms, related to the imagination of limb movements. Off-line EEG data was used to estimate waveforms of cortical activity (cortical current density, CCD) on selected regions of interest. CCD waveforms were fed into the BCI computational pipeline as an alternative to raw EEG signals; spectral features are evaluated through statistical tests (r(2) analysis), to quantify their reliability for BCI control. These results are compared, within subjects, to analogous results obtained without HREEG techniques. The processing procedure was designed in such a way that computations could be split into a setup phase (which includes most of the computational burden) and the actual EEG processing phase, which was limited to a single matrix multiplication. This separation allowed to make the procedure suitable for on-line utilization, and a pilot experiment was performed. Results show that lateralization of electrical activity, which is expected to be contralateral to the imagined movement, is more evident on the estimated CCDs than in the scalp potentials. CCDs produce a pattern of relevant spectral features that is more spatially focused, and has a higher statistical significance (EEG: 0.20+/-0.114 S.D.; CCD: 0.55+/-0.16 S.D.; p=10(-5)). A pilot experiment showed that a trained subject could utilize voluntary modulation of estimated CCDs for accurate (eight targets) on-line control of a cursor. This study showed that it is practically feasible to utilize HREEG techniques for on-line operation of a BCI system; off-line analysis suggests that accuracy of BCI control is enhanced by the proposed method.},
			author = {Cincotti, Febo and Mattia, Donatella and Aloise, Fabio and Bufalari, Simona and Astolfi, Laura and {De Vico Fallani}, Fabrizio and Tocci, Andrea and Bianchi, Luigi and Marciani, Maria Grazia and Gao, Shangkai and Millan, Jose and Babiloni, Fabio},
			doi = {10.1016/j.jneumeth.2007.06.031},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Cincotti et al. - 2008 - High-resolution EEG techniques for brain-computer interface applications.pdf:pdf},
			institution = {IRCCS Fondazione Santa Lucia, Rome, Italy.},
			issn = {0165-0270},
			journal = {Journal of neuroscience methods},
			keywords = {Adult,Biofeedback, Psychology,Brain,Brain Mapping,Brain: physiology,Communication Aids for Disabled,Electrodes,Electroencephalography,Evoked Potentials, Motor,Evoked Potentials, Motor: physiology,Evoked Potentials, Somatosensory,Female,Humans,Male,Online Systems,Signal Processing, Computer-Assisted,User-Computer Interface},
			month = {jan},
			number = {1},
			pages = {31--42},
			pmid = {17706292},
			title = {{High-resolution EEG techniques for brain-computer interface applications.}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/17706292},
			volume = {167},
			year = {2008}
			}

			@article{Peters2013,
			author = {Peters, Lieke and Maathuis, Carel and Hadders-Algra, Mijna},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/a097d69800c5f4767127ac52f96e6efe9eb1496d.html:html},
			journal = {Developmental Medicine {\&} Child Neurology},
			number = {4},
			pages = {59--64},
			title = {{Neural correlates of developmental coordination disorder}},
			url = {http://onlinelibrary.wiley.com/doi/10.1111/dmcn.12309/epdf},
			volume = {55},
			year = {2013}
			}

			@article{Rubinov2010,
			abstract = {Brain connectivity datasets comprise networks of brain regions connected by anatomical tracts or by functional associations. Complex network analysis-a new multidisciplinary approach to the study of complex systems-aims to characterize these brain networks with a small number of neurobiologically meaningful and easily computable measures. In this article, we discuss construction of brain networks from connectivity data and describe the most commonly used network measures of structural and functional connectivity. We describe measures that variously detect functional integration and segregation, quantify centrality of individual brain regions or pathways, characterize patterns of local anatomical circuitry, and test resilience of networks to insult. We discuss the issues surrounding comparison of structural and functional network connectivity, as well as comparison of networks across subjects. Finally, we describe a Matlab toolbox (http://www.brain-connectivity-toolbox.net) accompanying this article and containing a collection of complex network measures and large-scale neuroanatomical connectivity datasets. ?? 2009 Elsevier Inc.},
			author = {Rubinov, Mikail and Sporns, Olaf},
			doi = {10.1016/j.neuroimage.2009.10.003},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Rubinov, Sporns - 2010 - Complex network measures of brain connectivity Uses and interpretations.pdf:pdf},
			isbn = {1095-9572 (Electronic)$\backslash$r1053-8119 (Linking)},
			issn = {10538119},
			journal = {NeuroImage},
			number = {3},
			pages = {1059--1069},
			pmid = {19819337},
			publisher = {Elsevier Inc.},
			title = {{Complex network measures of brain connectivity: Uses and interpretations}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2009.10.003 http://ac.els-cdn.com/S105381190901074X/1-s2.0-S105381190901074X-main.pdf?{\_}tid=f945c8e8-2bce-11e6-a237-00000aab0f01{\&}acdnat=1465208042{\_}e0a5e6bb1ce72ad6fe3a75c40d0054e5},
			volume = {52},
			year = {2010}
			}

			@article{Mooneyham2013,
			abstract = {Substantial evidence suggests that mind-wandering typically occurs at a significant cost to performance. Mind-wandering-related deficits in performance have been observed in many contexts, most notably reading, tests of sustained attention, and tests of aptitude. Mind-wandering has been shown to negatively impact reading comprehension and model building, impair the ability to withhold automatized responses, and disrupt performance on tests of working memory and intelligence. These empirically identified costs of mind-wandering have led to the suggestion that mind-wandering may represent a pure failure of cognitive control and thus pose little benefit. However, emerging evidence suggests that the role of mind-wandering is not entirely pernicious. Recent studies have shown that mind-wandering may play a crucial role in both autobiographical planning and creative problem solving, thus providing at least two possible adaptive functions of the phenomenon. This article reviews these observed costs and possible functions of mind-wandering and identifies important avenues of future inquiry.},
			author = {Mooneyham, Benjamin W and Schooler, Jonathan W},
			doi = {10.1037/a0031569},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Mooneyham, Schooler - 2013 - The costs and benefits of mind-wandering a review.pdf:pdf},
			issn = {1878-7290},
			journal = {Canadian journal of experimental psychology = Revue canadienne de psychologie exp{\'{e}}rimentale},
			keywords = {Affect,Attention,Attention: physiology,Cost-Benefit Analysis,Humans,Memory, Short-Term,Memory, Short-Term: physiology,Reading,Thinking,Thinking: physiology},
			month = {mar},
			number = {1},
			pages = {11--8},
			pmid = {23458547},
			title = {{The costs and benefits of mind-wandering: a review.}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/23458547},
			volume = {67},
			year = {2013}
			}

			@article{Karahanoglu2017,
			abstract = {Ongoing fluctuations of brain activity measured by functional magnetic resonance imaging (fMRI) provide a novel window onto the organizational principles of brain function. Advances in data analysis have focussed on extracting the constituting elements of temporal dynamics in terms of activity or connectivity patterns. Subsequently, brain states can be defined and then be analyzed using temporal features and computational models as to capture subtle interactions between functional networks. These new methodological advances allow to deconstruct the rich spatiotemporal structure of functional components that dynamically assemble into resting-state networks long been observed using conventional measures of functional connectivity. Applications of these emerging methods demonstrate that changes in functional connectivity are indeed driven by complex reorganization of network interactions, and thus provide valuable observations to build better models of brain function and dysfunction. Here, we give an overview of the recent developments in this exciting field, together with main findings and perspectives on future research.},
			author = {KarahanoÄlu, Fikret IÅÄ±k and {Van De Ville}, Dimitri},
			doi = {10.1016/j.cobme.2017.09.008},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/5ca4e581eff56a6653ef27ab0b251ae629f871c5.pdf:pdf},
			issn = {24684511},
			journal = {Current Opinion in Biomedical Engineering},
			pages = {28--36},
			title = {{Dynamics of Large-Scale fMRI Networks: Deconstruct Brain Activity to Build Better Models of Brain Function}},
			url = {http://linkinghub.elsevier.com/retrieve/pii/S2468451117300417},
			year = {2017}
			}

			@article{GonzalezCastillo2017,
			abstract = {The temporal evolution of functional connectivity (FC) within the confines of individual scans is nowadays often explored with functional neuroimaging. This is particularly true for resting-state; yet, FC-dynamics have also been investigated as subjects engage on numerous tasks. It is these research efforts that constitute the core of this survey. First, empirical observations on how FC differs between task and rest-independent of temporal scale-are reviewed, as they underscore how, despite overall preservation of network topography, the brain's FC does reconfigure in systematic ways to accommodate task demands. Next, reports on the relationships between instantaneous FC and perception/performance in subsequent trials are discussed. Similarly, research where different aspects of task-concurrent FC-dynamics are explored or utilized to predict ongoing mental states are also examined. The manuscript finishes with an incomplete list of challenges that hopefully fuels future work in this vibrant area of neuroscientific research. Overall, this review concludes that task-concurrent FC-dynamics, when properly characterized, are relevant to behavior, and that their translational value holds considerable promise.},
			author = {Gonzalez-Castillo, Javier and Bandettini, Peter A.},
			doi = {10.1016/j.neuroimage.2017.08.006},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/be19b2bb061ae5627aab33f3818ac682bd37878d.pdf:pdf},
			issn = {10959572},
			journal = {NeuroImage},
			keywords = {Connectivity dynamics,Dynamic functional connectivity,Functional connectivity states,Task vs. rest,Task-concurrent functional connectivity},
			number = {August},
			pages = {1--8},
			publisher = {Elsevier Ltd},
			title = {{Task-based dynamic functional connectivity: Recent findings and open questions}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2017.08.006 https://ac.els-cdn.com/S1053811917306535/1-s2.0-S1053811917306535-main.pdf?{\_}tid=a4ea9c96-1340-11e8-9a6e-00000aacb362{\&}acdnat=1518803082{\_}878295dd475b244771a58e789f2151bf},
			year = {2017}
			}

			@article{Preuschoff2010,
			author = {Preuschoff, Kerstin},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/df6945dd70c53a7c0380a1f0f820f223199dace0.pdf:pdf},
			journal = {Simultaneous EEG and fMRI: {\ldots}},
			keywords = {BOLD contrast,BOLD signal,Local Field Potentials (LFP),MRI,Neurophysiological basis,measurement,stimulus,synapse},
			pages = {1--38},
			title = {{Physiological Basis of the BOLD Signal}},
			url = {http://books.google.com/books?hl=en{\&}lr={\&}id=HtkAfChTLToC{\&}oi=fnd{\&}pg=PA21{\&}dq=Physiological+Basis+of+the+BOLD+Signal{\&}ots=umx8CrAlXw{\&}sig=tjME3qsMWQ3p7lGjgE-{\_}K75TA9I},
			year = {2010}
			}

			@article{stone1974,
			author = {Stone, M.},
			journal = {J. Royal Stat. Soc.},
			pages = {111--147},
			title = {{Cross-validatory choice and assessment of statistical predictions}},
			volume = {36(2)},
			year = {1974}
			}

			@article{Abrams2016,
			author = {Abrams, Daniel A. and Chen, Tianwen and Odriozola, Paola and Cheng, Katherine M. and Baker, Amanda E. and Padmanabhan, Aarthi and Ryali, Srikanth and Kochalka, John and Feinstein, Carl and Menon, Vinod},
			doi = {10.1073/pnas.1602948113},
			file = {:Users/lorenafreitas/Downloads/PNAS-2016-Abrams-6295-300.pdf:pdf},
			isbn = {1091-6490 (Electronic)0027-8424 (Linking)},
			issn = {0027-8424},
			journal = {Proceedings of the National Academy of Sciences},
			number = {22},
			pages = {201602948},
			pmid = {27185915},
			title = {{Neural circuits underlying mother's voice perception predict social communication abilities in children}},
			url = {http://www.pnas.org/lookup/doi/10.1073/pnas.1602948113},
			volume = {113},
			year = {2016}
			}

			@article{Worsley1995,
			author = {Worsley, KJ and Friston, Karl},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/6318c7697daed8330d40165b56776d5d2a77c0f7.pdf:pdf},
			journal = {NeuroImage},
			pages = {172--181},
			title = {{Analysis of fMRI Time-Series RevisitedâAgain}},
			url = {http://www.fil.ion.ucl.ac.uk/spm/doc/papers/kjw{\_}revisited{\_}again.pdf},
			volume = {2},
			year = {1995}
			}

			@article{Wen2013,
			abstract = {Multivariate neural data provide the basis for assessing interactions in brain networks. Among myriad connectivity measures, Granger causality (GC) has proven to be statistically intuitive, easy to implement, and generate meaningful results. Although its application to functional MRI (fMRI) data is increasing, several factors have been identified that appear to hinder its neural interpretability: (a) latency differences in hemodynamic response function (HRF) across different brain regions, (b) low-sampling rates, and (c) noise. Recognizing that in basic and clinical neuroscience, it is often the change of a dependent variable (e.g., GC) between experimental conditions and between normal and pathology that is of interest, we address the question of whether there exist systematic relationships between GC at the fMRI level and that at the neural level. Simulated neural signals were convolved with a canonical HRF, down-sampled, and noise-added to generate simulated fMRI data. As the coupling parameters in the model were varied, fMRI GC and neural GC were calculated, and their relationship examined. Three main results were found: (1) GC following HRF convolution is a monotonically increasing function of neural GC; (2) this monotonicity can be reliably detected as a positive correlation when realistic fMRI temporal resolution and noise level were used; and (3) although the detectability of monotonicity declined due to the presence of HRF latency differences, substantial recovery of detectability occurred after correcting for latency differences. These results suggest that Granger causality is a viable technique for analyzing fMRI data when the questions are appropriately formulated.},
			author = {Wen, Xiaotong and Rangarajan, Govindan and Ding, Mingzhou},
			doi = {10.1371/journal.pone.0067428},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/1b12d6a6d366c767e2588488a63e1c331aec44c9.pdf:pdf},
			isbn = {1932-6203},
			issn = {19326203},
			journal = {PLoS ONE},
			number = {7},
			pmid = {23861763},
			title = {{Is Granger Causality a Viable Technique for Analyzing fMRI Data?}},
			url = {http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0067428{\&}type=printable},
			volume = {8},
			year = {2013}
			}

			@article{Power2014,
			archivePrefix = {arXiv},
			arxivId = {NIHMS150003},
			author = {Power, Jonathan D. and Mitra, Anish and Laumann, Timothy O. and Snyder, Abraham Z. and Schlaggar, Bradley L. and Petersen, Steven E.},
			doi = {10.1016/j.neuroimage.2013.08.048},
			eprint = {NIHMS150003},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Power et al. - 2014 - Methods to detect, characterize, and remove motion artifact in resting state fMRI.pdf:pdf},
			isbn = {0000000000000},
			issn = {10538119},
			journal = {NeuroImage},
			keywords = {artifact,functional connectivity,motion,movement,mri,resting state},
			number = {2},
			pages = {320--341},
			pmid = {19121661},
			title = {{Methods to detect, characterize, and remove motion artifact in resting state fMRI}},
			url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811913009117},
			volume = {84},
			year = {2014}
			}

			@article{Fransson2007,
			abstract = {In the absence of any overt task performance, it has been shown that spontaneous, intrinsic brain activity is expressed as systemwide, resting-state networks in the adult brain. However, the route to adult patterns of resting-state activity through neuronal development in the human brain is currently unknown. Therefore, we used functional MRI to map patterns of resting-state activity in infants during sleep. We found five unique resting-states networks in the infant brain that encompassed the primary visual cortex, bilateral sensorimotor areas, bilateral auditory cortex, a network including the precuneus area, lateral parietal cortex, and the cerebellum as well as an anterior network that incorporated the medial and dorsolateral prefrontal cortex. These results suggest that resting-state networks driven by spontaneous signal fluctuations are present already in the infant brain. The potential link between the emergence of behavior and patterns of resting-state activity in the infant brain is discussed.},
			author = {Fransson, Peter and Ski{\"{o}}ld, Beatrice and Horsch, Sandra and Nordell, Anders and Blennow, Mats and Lagercrantz, Hugo and Aden, Ulrika},
			doi = {10.1073/pnas.0704380104},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/dcd22f19642f414fc460f4c953bd9f913fc90ec0.pdf:pdf},
			isbn = {0027-8424},
			issn = {0027-8424},
			journal = {Proceedings of the National Academy of Sciences of the United States of America},
			keywords = {Animals,Behavior,Brain,Brain Mapping,Brain Mapping: methods,Brain: growth {\&} development,Brain: metabolism,Brain: physiology,Computer-Assisted,Female,Humans,Image Processing,Infant,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Male,Models,Neurological,Rest},
			number = {39},
			pages = {15531--6},
			pmid = {17878310},
			title = {{Resting-state networks in the infant brain.}},
			url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2000516{\&}tool=pmcentrez{\&}rendertype=abstract},
			volume = {104},
			year = {2007}
			}

			@article{Zhuang2008,
			abstract = {In this fMRI study, we explore the connectivity among brain regions in a shape-from-motion task using the causal mapping analysis of structural equation modeling (SEM). An important distinction of our approach is that we have adapted SEM from its traditional role in confirmatory analysis to provide utility as an exploratory mapping technique. Our current approaches include (I) detecting brain regions that fit well in a hypothesized neural network model, and (II) identifying the best connectivity model at each brain region. We demonstrate that SEM effectively detects the dorsal and ventral visual pathways from the covariance structure in fMRI data, confirming previous neuroscience results. Further, our SEM mapping methodology found that the two pathways interact through specific cortical areas such as the superior lateral occipital cortex in the perception of shape from motion. {\textcopyright} 2008 Elsevier Inc. All rights reserved.},
			author = {Zhuang, Jiancheng and Peltier, Scott and He, Sheng and LaConte, Stephen and Hu, Xiaoping},
			doi = {10.1016/j.neuroimage.2008.05.036},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/5860ab2b17641d392b811b86c45150dc411aacc0.pdf:pdf},
			isbn = {1095-9572 (Electronic)},
			issn = {10538119},
			journal = {NeuroImage},
			keywords = {Effective connectivity,Functional magnetic resonance imaging,Shape from motion,Structural equation modeling},
			number = {2},
			pages = {799--806},
			pmid = {18599316},
			title = {{Mapping the connectivity with structural equation modeling in an fMRI study of shape-from-motion task}},
			url = {https://ac.els-cdn.com/S1053811908006691/1-s2.0-S1053811908006691-main.pdf?{\_}tid=5d44bc6b-3217-429c-b5db-75fa2122eb66{\&}acdnat=1524144683{\_}dc58718c99e1ec72b3aeaa1092126b49},
			volume = {42},
			year = {2008}
			}

			@article{Gao2010,
			author = {Gao, J. and Zheng, C. and Wang, P.},
			doi = {10.1177/155005941004100111},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Gao, Zheng, Wang - 2010 - Online Removal of Muscle Artifact from Electroencephalogram Signals Based on Canonical Correlation Analysis.pdf:pdf},
			issn = {1550-0594},
			journal = {Clinical EEG and Neuroscience},
			keywords = {canonical correlation analysis,electroencephalography},
			month = {jan},
			number = {1},
			pages = {53--59},
			title = {{Online Removal of Muscle Artifact from Electroencephalogram Signals Based on Canonical Correlation Analysis}},
			url = {http://eeg.sagepub.com/lookup/doi/10.1177/155005941004100111},
			volume = {41},
			year = {2010}
			}

			@article{Casey1995,
			author = {Casey, BJ and Choen, Jonathan and Jezzard, Peter and Turner, Robert and Noll, Douglas and Trainor, Rolf and Giedd, Jay and Kaysen, Debra and Hertz-Pannier, Lucy and Rapoport, Judith},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/1a6811e02e084322d8ca88ec8be8431692805b9b.pdf:pdf},
			journal = {NeuroImage},
			pages = {221--229},
			title = {{Activation of Prefrontal Cortex in Children during a Nonspatial Working Memory Task with Functional MRI}},
			url = {http://ac.els-cdn.com/S1053811985710294/1-s2.0-S1053811985710294-main.pdf?{\_}tid=acb841f8-e3c6-11e6-a4be-00000aacb361{\&}acdnat=1485435492{\_}bea66a1c283ecc28f9935022ca2f7e01},
			year = {1995}
			}
			@article{Scholler2012,
			author = {Scholler, Simon and Bosse, Sebastian and Treder, Matthias Sebastian and Blankertz, Benjamin and Curio, Gabriel and M{\"{u}}ller, Klaus-robert and Wiegand, Thomas},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Scholler et al. - 2012 - Toward a Direct Measure of Video Quality Perception Using EEG.pdf:pdf},
			number = {5},
			pages = {2619--2629},
			title = {{Toward a Direct Measure of Video Quality Perception Using EEG}},
			volume = {21},
			year = {2012}
			}

			@article{Tibshirani2011,
			author = {Tibshirani, Robert},
			file = {:Users/lorenafreitas/Desktop/99c128b9cd7b7fbc817a2843a47ce8a1c35d.pdf:pdf},
			keywords = {l 1 -penalty,penalization,regularization},
			pages = {273--282},
			title = {{Regression shrinkage and selection via the lasso : a retrospective}},
			year = {2011}
			}

			@article{Benjamini1995,
			author = {Benjamini, Y and Hochberg, Y},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Benjamini, Hochberg - 1995 - Controlling the False Discovery Rate A Practical and Powerful Approach to Multiple Testing.pdf:pdf},
			journal = {Journal of the Royal Statistical Society. Series B {\ldots}},
			number = {1},
			pages = {289--300},
			title = {{Controlling the False Discovery Rate : A Practical and Powerful Approach to Multiple Testing}},
			url = {http://www.jstor.org/stable/2346101},
			volume = {57},
			year = {1995}
			}

			@article{Grosse-Wentrup2011,
			author = {Grosse-Wentrup, Moritz},
			doi = {10.1109/NER.2011.5910567},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Grosse-Wentrup - 2011 - Fronto-parietal gamma-oscillations are a cause of performance variation in brain-computer interfacing.pdf:pdf},
			isbn = {978-1-4244-4140-2},
			journal = {2011 5th International IEEE/EMBS Conference on Neural Engineering},
			month = {apr},
			pages = {384--387},
			publisher = {Ieee},
			title = {{Fronto-parietal gamma-oscillations are a cause of performance variation in brain-computer interfacing}},
			url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5910567},
			year = {2011}
			}

			@article{Gottlich2015,
			abstract = {BASCO (BetA Series COrrelation) is a user-friendly MATLAB toolbox with a graphical user interface (GUI) which allows investigating functional connectivity in event-related functional magnetic resonance imaging (fMRI) data. Connectivity analyses extend and compliment univariate activation analyses since the actual interaction between brain regions involved in a task can be explored. BASCO supports seed-based functional connectivity as well as brain network analyses. Although there are a multitude of advanced toolboxes for investigating resting-state functional connectivity, BASCO is the first toolbox for evaluating task-related whole-brain functional connectivity employing a large number of network nodes. Thus, BASCO allows investigating task-specific rather than resting-state networks. Here, we summarize the main features of the toolbox and describe the methods and algorithms.},
			author = {G{\"{o}}ttlich, Martin and Beyer, Frederike and Kr{\"{a}}mer, Ulrike M.},
			doi = {10.3389/fnsys.2015.00126},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/3771b30b466f6ccf60f1b19d1af6e42e1bf7f8e7.pdf:pdf},
			isbn = {1662-5137 (Electronic)$\backslash$r1662-5137 (Linking)},
			issn = {1662-5137},
			journal = {Frontiers in Systems Neuroscience},
			keywords = {beta-series correlation,degree centrality,event-related design,fMRI, functional connectivity, network analysis, b,fmri,functional connectivity,network analysis},
			number = {September},
			pages = {1--10},
			pmid = {26441558},
			title = {{BASCO: a toolbox for task-related functional connectivity}},
			url = {http://journal.frontiersin.org/Article/10.3389/fnsys.2015.00126/abstract},
			volume = {9},
			year = {2015}
			}

			@article{Jochaut2015,
			abstract = {Subjects with autism often show language difficulties, but it is unclear how they relate to neurophysiological anomalies of cortical speech processing. We used combined EEG and fMRI in 13 subjects with autism and 13 control participants and show that in autism, gamma and theta cortical activity do not engage synergistically in response to speech. Theta activity in left auditory cortex fails to track speech modulations, and to down-regulate gamma oscillations in the group with autism. This deficit predicts the severity of both verbal impairment and autism symptoms in the affected sample. Finally, we found that oscillation-based connectivity between auditory and other language cortices is altered in autism. These results suggest that the verbal disorder in autism could be associated with an altered balance of slow and fast auditory oscillations, and that this anomaly could compromise the mapping between sensory input and higher-level cognitive representations.},
			author = {Jochaut, Delphine and Lehongre, Katia and Saitovitch, Ana and Devauchelle, Anne-Dominique and Olasagasti, Itsaso and Chabane, Nadia and Zilbovicius, Monica and Giraud, Anne-Lise},
			doi = {10.3389/fnhum.2015.00171},
			file = {:Users/lorenafreitas/Downloads/fnhum-09-00171.pdf:pdf},
			isbn = {1662-5161},
			issn = {1662-5161},
			journal = {Frontiers in Human Neuroscience},
			keywords = {auditory cortex,autism,cortical oscillations,oscillation coupling,speech processing,speech processing, auditory cortex, cortical oscil},
			number = {March},
			pages = {1--12},
			pmid = {25870556},
			title = {{Atypical coordination of cortical oscillations in response to speech in autism}},
			url = {http://www.frontiersin.org/Human{\_}Neuroscience/10.3389/fnhum.2015.00171/abstract},
			volume = {9},
			year = {2015}
			}

			@article{Cashero2011,
			author = {Cashero, Zachary and Ben-hur, Asa},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Cashero, Ben-hur - 2011 - COMPARISON OF EEG PREPROCESSING METHODS TO IMPROVE THE CLASSIFICATION OF P300 TRIALS.pdf:pdf},
			title = {{COMPARISON OF EEG PREPROCESSING METHODS TO IMPROVE THE CLASSIFICATION OF P300 TRIALS}},
			url = {http://www.cs.colostate.edu/eeg/publications/zachthesis.pdf},
			year = {2011}
			}

			@article{Young2014,
			abstract = {Brain-computer interface (BCI) technology is being incorporated into new stroke rehabilitation devices, but little is known about brain changes associated with its use. We collected anatomical and functional MRI of nine stroke patients with persistent upper extremity motor impairment before, during, and after therapy using a BCI system. Subjects were asked to perform finger tapping of the impaired hand during fMRI. Action Research Arm Test (ARAT), 9-Hole Peg Test (9-HPT), and Stroke Impact Scale (SIS) domains of Hand Function (HF) and Activities of Daily Living (ADL) were also assessed. Group-level analyses examined changes in whole-brain task-based functional connectivity (FC) to seed regions in the motor network observed during and after BCI therapy. Whole-brain FC analyses seeded in each thalamus showed FC increases from baseline at mid-therapy and post-therapy (p {\textless} 0.05). Changes in FC between seeds at both the network and the connection levels were examined for correlations with changes in behavioral measures. Average motor network FC was increased post-therapy, and changes in average network FC correlated (p {\textless} 0.05) with changes in performance on ARAT (R (2) = 0.21), 9-HPT (R (2) = 0.41), SIS HF (R (2) = 0.27), and SIS ADL (R (2) = 0.40). Multiple individual connections within the motor network were found to correlate in change from baseline with changes in behavioral measures. Many of these connections involved the thalamus, with change in each of four behavioral measures significantly correlating with change from baseline FC of at least one thalamic connection. These preliminary results show changes in FC that occur with the administration of rehabilitative therapy using a BCI system. The correlations noted between changes in FC measures and changes in behavioral outcomes indicate that both adaptive and maladaptive changes in FC may develop with this therapy and also suggest a brain-behavior relationship that may be stimulated by the neuromodulatory component of BCI therapy.},
			author = {Young, Brittany Mei and Nigogosyan, Zack and Remsik, Alexander and Walton, L{\'{e}}o M and Song, Jie and Nair, Veena a and Grogan, Scott W and Tyler, Mitchell E and Edwards, Dorothy Farrar and Caldera, Kristin and Sattin, Justin a and Williams, Justin C and Prabhakaran, Vivek},
			doi = {10.3389/fneng.2014.00025},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Young et al. - 2014 - Changes in functional connectivity correlate with behavioral gains in stroke patients after therapy using a brain-.pdf:pdf},
			issn = {1662-6443},
			journal = {Frontiers in neuroengineering},
			keywords = {bci therapy,brain-computer interface,brain-computer interface, stroke rehabilitation, f,functional connectivity,stroke rehabilitation,ue motor recovery},
			month = {jan},
			number = {July},
			pages = {25},
			pmid = {25071547},
			title = {{Changes in functional connectivity correlate with behavioral gains in stroke patients after therapy using a brain-computer interface device.}},
			url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4086321{\&}tool=pmcentrez{\&}rendertype=abstract},
			volume = {7},
			year = {2014}
			}

			@article{Schlaggar2002,
			author = {Schlaggar, Bradley L and Brown, Timothy T and Lugar, Heather M and Visscher, Kristina M and Miezin, Francis M},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/0702894ad2f86d189ccb2398cacb40e564f2c5bb.pdf:pdf},
			journal = {Science},
			pages = {1476--1479},
			title = {{Functional Neuroanatomical Differences Between Adults and School-Age Children in the Processing of Single Words}},
			url = {http://science.sciencemag.org/content/sci/296/5572/1476.full.pdf},
			volume = {296},
			year = {2002}
			}

			@article{Seghier2010,
			author = {Seghier, Mohamed L and H{\"{u}}ppi, Petra S},
			doi = {10.1053/j.semperi.2009.10.008},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/41d8f05e26be07231792b02e1de1f4afa3b15564.pdf:pdf},
			issn = {0146-0005},
			journal = {Seminars in Petinatology},
			keywords = {abnormal function,bold response,brain injury,brain plasticity,functional mri},
			number = {1},
			pages = {79--86},
			publisher = {Elsevier Inc.},
			title = {{The Role of Functional Magnetic Resonance Imaging in the Study of Brain Development , Injury , and Recovery in the Newborn}},
			url = {http://dx.doi.org/10.1053/j.semperi.2009.10.008 http://ac.els-cdn.com/S0146000509000962/1-s2.0-S0146000509000962-main.pdf?{\_}tid=00f8746e-e3da-11e6-a984-00000aacb35e{\&}acdnat=1485443794{\_}e60b5a258db44d2a28c332d959cd2275},
			volume = {34},
			year = {2010}
			}

			@article{Rombouts2009,
			author = {Rombouts, Serge A R B and Damoiseaux, Jessica S and Goekoop, Rutger and Barkhof, Frederik and Scheltens, Philip and Smith, Stephen M and Beckmann, Christian F},
			doi = {10.1002/hbm.20505},
			file = {:Users/lorenafreitas/Downloads/Rombouts{\_}et{\_}al-2009-Human{\_}Brain{\_}Mapping.pdf:pdf},
			keywords = {117,36,916,alzheimer,connectivity,contract grant numbers,contract grant sponsor,correspondence to,default mode network,department of radiology,fmri,netherlands organization for scientific,nwo,research,s disease,serge rombouts},
			number = {March 2007},
			pages = {256--266},
			title = {{Model-Free Group Analysis Shows Altered BOLD FMRI Networks in Dementia}},
			volume = {266},
			year = {2009}
			}

			@article{Haxby2013,
			author = {Haxby, J. V.},
			doi = {10.1016/j.neuroimage.2012.03.016.Multivariate},
			file = {:Users/lorenafreitas/Downloads/nihms363224.pdf:pdf},
			keywords = {decoding,fmri,machine learning,multivariate pattern analysis,mvpa,pattern,vision},
			number = {2},
			pages = {852--855},
			title = {{Multivariate pattern analysis of fMRI : The early beginnings}},
			volume = {62},
			year = {2013}
			}

			@article{Siegel2014,
			abstract = {Subject motion degrades the quality of task functional magnetic resonance imaging (fMRI) data. Here, we test two classes of methods to counteract the effects of motion in task fMRI data: (1) a variety of motion regressions and (2) motion censoring ("motion scrubbing"). In motion regression, various regressors based on realignment estimates were included as nuisance regressors in general linear model (GLM) estimation. In motion censoring, volumes in which head motion exceeded a threshold were withheld from GLM estimation. The effects of each method were explored in several task fMRI data sets and compared using indicators of data quality and signal-to-noise ratio. Motion censoring decreased variance in parameter estimates within- and across-subjects, reduced residual error in GLM estimation, and increased the magnitude of statistical effects. Motion censoring performed better than all forms of motion regression and also performed well across a variety of parameter spaces, in GLMs with assumed or unassumed response shapes. We conclude that motion censoring improves the quality of task fMRI data and can be a valuable processing step in studies involving populations with even mild amounts of head movement.},
			archivePrefix = {arXiv},
			arxivId = {NIHMS150003},
			author = {Siegel, Joshua S. and Power, Jonathan D. and Dubis, Joseph W. and Vogel, Alecia C. and Church, Jessica A. and Schlaggar, Bradley L. and Petersen, Steven E.},
			doi = {10.1002/hbm.22307},
			eprint = {NIHMS150003},
			file = {:Users/lorenafreitas/Downloads/Siegel{\_}et{\_}al-2014-Human{\_}Brain{\_}Mapping.pdf:pdf},
			isbn = {1097-0193 (Electronic)$\backslash$r1065-9471 (Linking)},
			issn = {10970193},
			journal = {Human Brain Mapping},
			keywords = {Data quality,FMRI,GLM,General linear model,Head movement,Motion,Noise,Scrubbing,Task,motion,preprocessing},
			mendeley-tags = {motion,preprocessing},
			number = {5},
			pages = {1981--1996},
			pmid = {23861343},
			title = {{Statistical improvements in functional magnetic resonance imaging analyses produced by censoring high-motion data points}},
			volume = {35},
			year = {2014}
			}

			@article{Josephs1999,
			abstract = {Event-related functional magnetic resonance imaging is a recent and popular technique for detecting haemodynamic responses to brief stimuli or events. However, the design of event-related experiments requires careful consideration of numerous issues of measurement, modelling and inference. Here we review these issues, with particular emphasis on the use of basis functions within a general linear modelling framework to model and make inferences about the haemodynamic response. With these models in mind, we then consider how the properties of functional magnetic resonance imaging data determine the optimal experimental design for a specific hypothesis, in terms of stimulus ordering and interstimulus interval. Finally, we illustrate various event-related models with examples from recent studies.},
			author = {Josephs, O and Henson, R N},
			doi = {10.1098/rstb.1999.0475},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/0b3112ad1329ccfac599882db90ffa19a42ae878.pdf:pdf},
			isbn = {0962-8436 (Print)},
			issn = {0962-8436},
			journal = {Philosophical transactions of the Royal Society of London},
			keywords = {bold contrast,echo-planar mri,event-related fmri,single trial,statistics},
			number = {1387},
			pages = {1215--1228},
			pmid = {10466147},
			title = {{Event-related functional magnetic resonance imaging: modelling, inference and optimization.}},
			url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1692638/pdf/10466147.pdf},
			volume = {354},
			year = {1999}
			}

			@article{Kappenman2010,
			abstract = {To determine whether data quality is meaningfully reduced by high electrode impedance, EEG was recorded simultaneously from low- and high-impedance electrode sites during an oddball task. Low-frequency noise was found to be increased at high-impedance sites relative to low-impedance sites, especially when the recording environment was warm and humid. The increased noise at the high-impedance sites caused an increase in the number of trials needed to obtain statistical significance in analyses of P3 amplitude, but this could be partially mitigated by high-pass filtering and artifact rejection. High electrode impedance did not reduce statistical power for the N1 wave unless the recording environment was warm and humid. Thus, high electrode impedance may increase noise and decrease statistical power under some conditions, but these effects can be reduced by using a cool and dry recording environment and appropriate signal processing methods.},
			author = {Kappenman, Emily S and Luck, Steven J},
			doi = {10.1111/j.1469-8986.2010.01009.x},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Kappenman, Luck - 2010 - The effects of electrode impedance on data quality and statistical significance in ERP recordings.pdf:pdf},
			issn = {1540-5958},
			journal = {Psychophysiology},
			keywords = {Adolescent,Adult,Electric Impedance,Electrodes,Electrodes: standards,Electroencephalography,Electroencephalography: instrumentation,Electroencephalography: standards,Evoked Potentials,Evoked Potentials: physiology,Female,Humans,Male,Monte Carlo Method,Photic Stimulation,Quality Control,Scalp,Scalp: physiology,Skin Physiological Phenomena,Young Adult},
			month = {sep},
			number = {5},
			pages = {888--904},
			pmid = {20374541},
			title = {{The effects of electrode impedance on data quality and statistical significance in ERP recordings.}},
			url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2902592/pdf/nihms175138.pdf http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2902592{\&}tool=pmcentrez{\&}rendertype=abstract},
			volume = {47},
			year = {2010}
			}

			@article{Lemieux2007,
			abstract = {EEG-correlated fMRI can provide localisation information on the generators of epileptiform discharges in patients with focal epilepsy. To increase the technique's clinical potential, it is important to consider ways of optimising the yield of each experiment while minimizing the risk of false-positive activation. Head motion can lead to severe image degradation and result in false-positive activation and is usually worse in patients than in healthy subjects. We performed general linear model fMRI data analysis on simultaneous EEG-fMRI data acquired in 34 cases with focal epilepsy. Signal changes associated with large inter-scan motion events (head jerks) were modelled using modified design matrices that include 'scan nulling' regressors. We evaluated the efficacy of this approach by mapping the proportion of the brain for which F-tests across the additional regressors were significant. In 95{\%} of cases, there was a significant effect of motion in 50{\%} of the brain or greater; for the scan nulling effect, the proportion was 36{\%}; this effect was predominantly in the neocortex. We conclude that careful consideration of the motion-related effects in fMRI studies of patients with epilepsy is essential and that the proposed approach can be effective. {\textcopyright} 2007 Elsevier Inc. All rights reserved.},
			author = {Lemieux, Louis and Salek-Haddadi, Afraim and Lund, Torben E. and Laufs, Helmut and Carmichael, David},
			doi = {10.1016/j.mri.2007.03.009},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/2ccaab00658bc7f587f60a701be7416d60ad7c9c.pdf:pdf},
			isbn = {1065-9471 (Print)$\backslash$r1065-9471 (Linking)},
			issn = {0730725X},
			journal = {Magnetic Resonance Imaging},
			keywords = {Epilepsy,Large motion events,Modelling,fMRI},
			number = {6},
			pages = {894--901},
			pmid = {17490845},
			title = {{Modelling large motion events in fMRI studies of patients with epilepsy}},
			url = {https://ac.els-cdn.com/S0730725X07002214/1-s2.0-S0730725X07002214-main.pdf?{\_}tid=92ab5ebe-10e1-11e8-896b-00000aab0f6b{\&}acdnat=1518542345{\_}95c2427b8c8503be33bf2746e05ba024},
			volume = {25},
			year = {2007}
			}

			@article{Vasung2013,
			abstract = {The development of the human brain, from the fetal period until childhood, happens in a series of intertwined neurogenetical and histogenetical events that are influenced by environment. Neuronal proliferation and migration, cell aggregation, axonal ingrowth and outgrowth, dendritic arborisation, synaptic pruning and myelinisation contribute to the 'plasticity of the developing brain'. These events taken together contribute to the establishment of adult-like neuroarchitecture required for normal brain function. With the advances in technology today, mostly due to the development of non-invasive neuroimaging tools, it is possible to analyze these structural events not only in anatomical space but also longitudinally in time. In this review we have highlighted current 'state of the art' neuroimaging tools. Development of the new MRI acquisition sequences (DTI, CHARMED and phase imaging) provides valuable insight into the changes of the microstructural environment of the cortex and white matter. Development of MRI imaging tools dedicated for analysis of the acquired images (i) TBSS and ROI fiber tractography, (ii) new tissue segmentation techniques and (iii) morphometric analysis of the cortical mantle (cortical thickness and convolutions) allows the researchers to map the longitudinal changes in the macrostructure of the developing brain that go hand-in-hand with the acquisition of cognitive skills during childhood. Finally, the latest and the newest technologies, like connectom analysis and resting state fMRI connectivity analysis, today, for the first time provide the opportunity to study the developing brain through the prism of maturation of the systems and networks beyond individual anatomical areas. Combining these methods in the future and modeling the hierarchical organization of the brain might ultimately help to understand the mechanisms underlying complex brain structure function relationships of normal development and of developmental disorders.},
			author = {Vasung, Lana and Fischi-Gomez, Elda and H{\"{u}}ppi, Petra S.},
			doi = {10.1007/s00247-012-2515-y},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Vasung, Fischi-Gomez, H{\"{u}}ppi - 2013 - Multimodality evaluation of the pediatric brain DTI and its competitors.pdf:pdf},
			isbn = {1432-1998 (Electronic)$\backslash$n0301-0449 (Linking)},
			issn = {03010449},
			journal = {Pediatric Radiology},
			keywords = {Brain development,Connectivity,Cortical folding,Cortical thickness,Diffusion tensor imaging,Neuroimaging biomakers,Premature birth},
			number = {1},
			pages = {60--68},
			pmid = {23288478},
			title = {{Multimodality evaluation of the pediatric brain: DTI and its competitors}},
			volume = {43},
			year = {2013}
			}
			@article{Neuroscience2013,
			author = {Neuroscience, Human and Heuvel, Van Den and Benders, Manon J N L and Kersbergen, Karina J},
			doi = {10.3389/fnhum.2013.00650},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/ec6ffb7c30b101109fe14700ed7d06df2023c01d.pdf:pdf},
			keywords = {brain development,functional connectivity,resting-state functional MRI, functional connectiv,resting-state functional mri},
			number = {October},
			pages = {1--7},
			title = {{On development of functional brain connectivity in the young brain}},
			url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3792361/pdf/fnhum-07-00650.pdf},
			volume = {7},
			year = {2013}
			}

			@article{Yuan2009,
			author = {Yuan, Weihong and Altaye, Mekibib and Ret, Jen and Schmithorst, Vincent and Byars, Anna W and Holland, Scott K},
			doi = {10.1002/hbm.20616.Quantification},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/594fba19be1f4003c8b1e6dcf339dcee4480b6e6.pdf:pdf},
			journal = {Human Brain Mapping},
			keywords = {children,fmri,head motion,language task},
			number = {5},
			pages = {1481--1489},
			title = {{Quantification of Head Motion in Children During Various fMRI Language Tasks}},
			url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2763570/pdf/nihms-114085.pdf},
			volume = {30},
			year = {2009}
			}

			@article{Guger2012,
			abstract = {Most brain-computer interfaces (BCIs) rely on one of three types of signals in the electroencephalogram (EEG): P300s, steady-state visually evoked potentials, and event-related desynchronization. EEG is typically recorded non-invasively with electrodes mounted on the human scalp using conductive electrode gel for optimal impedance and data quality. The use of electrode gel entails serious problems that are especially pronounced in real-world settings when experts are not available. Some recent work has introduced dry electrode systems that do not require gel, but often introduce new problems such as comfort and signal quality. The principal goal of this study was to assess a new dry electrode BCI system in a very common task: spelling with a P300 BCI. A total of 23 subjects used a P300 BCI to spell the word "LUCAS" while receiving real-time, closed-loop feedback. The dry system yielded classification accuracies that were similar to those obtained with gel systems. All subjects completed a questionnaire after data recording, and all subjects stated that the dry system was not uncomfortable. This is the first field validation of a dry electrode P300 BCI system, and paves the way for new research and development with EEG recording systems that are much more practical and convenient in field settings than conventional systems.},
			author = {Guger, Christoph and Krausz, Gunther and Allison, Brendan Z and Edlinger, Guenter},
			doi = {10.3389/fnins.2012.00060},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Guger et al. - 2012 - Comparison of dry and gel based electrodes for p300 brain-computer interfaces.pdf:pdf},
			issn = {1662-453X},
			journal = {Frontiers in neuroscience},
			keywords = {brain,brainâcomputer interface, brainâmachine interface,,computer interface,dry electrodes,eeg,erp,gel electrodes,machine interface},
			month = {jan},
			number = {May},
			pages = {60},
			pmid = {22586362},
			title = {{Comparison of dry and gel based electrodes for p300 brain-computer interfaces.}},
			url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3345570{\&}tool=pmcentrez{\&}rendertype=abstract},
			volume = {6},
			year = {2012}
			}

			@article{Kozberg2016,
			author = {Kozberg, Mariel G and Ma, Ying and Shaik, Mohammed A and Kim, X Sharon H and Hillman, Elizabeth M C},
			doi = {10.1523/JNEUROSCI.2363-15.2016},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/d06314fd866738c1e6dad29194a28bac5777f4af.pdf:pdf},
			keywords = {by development of the,flavoprotein fluorescence,fmri,functional hyperemia,gcamp imaging,mecha-,neuronal connectivity is accompanied,neurovascular coupling,oxygen consumption,postnatal neural development,significance statement,the postnatal development of,this work demonstrates that},
			number = {25},
			pages = {6704--6717},
			title = {{Rapid Postnatal Expansion of Neural Networks Occurs in an Environment of Altered Neurovascular and Neurometabolic Coupling}},
			url = {http://www.jneurosci.org/content/jneuro/36/25/6704.full.pdf},
			volume = {36},
			year = {2016}
			}

			@article{Brown2014,
			author = {Brown, Colin J and Miller, Steven P and Booth, Brian G and Andrews, Shawn and Chau, Vann and Poskitt, Kenneth J and Hamarneh, Ghassan},
			doi = {10.1016/j.neuroimage.2014.07.030},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/35a4a7b05748da7632bb3f29895b50a9097b26fb.pdf:pdf},
			issn = {1053-8119},
			journal = {NeuroImage},
			keywords = {Brain development,Neonates,Network measures,Preterm,Structural connectome,Tractography},
			pages = {667--680},
			publisher = {Elsevier Inc.},
			title = {{Structural network analysis of brain development in young preterm neonates}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2014.07.030 http://ac.els-cdn.com/S1053811914006132/1-s2.0-S1053811914006132-main.pdf?{\_}tid=c2a0a154-3592-11e7-85ee-00000aacb35e{\&}acdnat=1494429190{\_}948fc815fbcfba988f4d4a5da23f8643},
			volume = {101},
			year = {2014}
			}

			@article{Clifford2005,
			abstract = {Spectral estimates of heart rate variability (HRV) often involve the use of techniques such as the fast Fourier transform (FFT), which require an evenly sampled time series. HRV is calculated from the variations in the beat-to-beat (RR) interval timing of the cardiac cycle which are inherently irregularly spaced in time. In order to produce an evenly sampled time series prior to FFT-based spectral estimation, linear or cubic spline resampling is usually employed. In this paper, by using a realistic artificial RR interval generator, interpolation and resampling is shown to result in consistent over-estimations of the power spectral density (PSD) compared with the theoretical solution. The Lomb-Scargle (LS) periodogram, a more appropriate spectral estimation technique for unevenly sampled time series that uses only the original data, is shown to provide a superior PSD estimate. Ectopy removal or replacement is shown to be essential regardless of the spectral estimation technique. Resampling and phantom beat replacement is shown to decrease the accuracy of PSD estimation, even at low levels of ectopy or artefact. A linear relationship between the frequency of ectopy/artefact and the error (mean and variance) of the PSD estimate is demonstrated. Comparisons of PSD estimation techniques performed on real RR interval data during minimally active segments (sleep) demonstrate that the LS periodogram provides a less noisy spectral estimate of HRV.},
			author = {Clifford, Gari D. and Tarassenko, Lionel},
			doi = {10.1109/TBME.2005.844028},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Clifford, Tarassenko - 2005 - Quantifying errors in spectral estimates of HRV due to beat replacement and resampling.pdf:pdf},
			isbn = {0018-9294},
			issn = {00189294},
			journal = {IEEE Transactions on Biomedical Engineering},
			keywords = {Beat replacement,Fast Fourier transform,HRV,Heart rate variability,Interpolation,Irregular sampling,Lomb periodogram,Resampling,Sleep,Uneven sampling,heart rate,resampling},
			mendeley-tags = {HRV,heart rate,resampling},
			number = {4},
			pages = {630--638},
			pmid = {15825865},
			title = {{Quantifying errors in spectral estimates of HRV due to beat replacement and resampling}},
			volume = {52},
			year = {2005}
			}

			@article{Liverani2015,
			author = {Liverani, Maria Chiara and Manuel, Aur{\'{e}}lie and Nahum, Louis and Guardabassi, Veronica and Tomasetto, Carlo and Schnider, Armin},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/300c8d38d1b10fada705483362108d22cf5fbaa7.pdf:pdf},
			journal = {Child Neuropsychology},
			title = {{Children's sense of reality: The development of orbitofrontal reality filtering}},
			url = {http://www.tandfonline.com/doi/pdf/10.1080/09297049.2015.1120861?needAccess=true},
			year = {2015}
			}

			@article{Glover1999,
			abstract = {The temporal characteristics of the BOLD response in sensorimotor and auditory cortices were measured in subjects performing finger tapping while listening to metronome pacing tones. A repeated trial paradigm was used with stimulus durations of 167 ms to 16 s and intertrial times of 30 s. Both cortical systems were found to be nonlinear in that the response to a long stimulus could not be predicted by convolving the 1-s response with a rectangular function. In the short-time regime, the amplitude of the response varied only slowly with stimulus duration. It was found that this character was predicted with a modification to Buxton's balloon model. Wiener deconvolution was used to deblur the response to concatenated short episodes of finger tapping at different temporal separations and at rates from 1 to 4 Hz. While the measured response curves were distorted by overlap between the individual episodes, the deconvolved response at each rate was found to agree well with separate scans at each of the individual rates. Thus, although the impulse response cannot predict the response to fully overlapping stimuli, linear deconvolution is effective when the stimuli are separated by at least 4 s. The deconvolution filter must be measured for each subject using a short-stimulus paradigm. It is concluded that deconvolution may be effective in diminishing the hemodynamically imposed temporal blurring and may have potential applications in quantitating responses in eventrelated fMRI.},
			author = {Glover, Gary H.},
			doi = {10.1006/nimg.1998.0419},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Glover - 1999 - Deconvolution of Impulse Response in Event-Related BOLD fMRI.pdf:pdf},
			isbn = {1053-8119},
			issn = {10538119},
			journal = {NeuroImage},
			number = {4},
			pages = {416--429},
			pmid = {10191170},
			title = {{Deconvolution of Impulse Response in Event-Related BOLD fMRI}},
			url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811998904190},
			volume = {9},
			year = {1999}
			}

			@article{Murphy2013,
			abstract = {The goal of resting-state functional magnetic resonance imaging (fMRI) is to investigate the brain's functional connections by using the temporal similarity between blood oxygenation level dependent (BOLD) signals in different regions of the brain "at rest" as an indicator of synchronous neural activity. Since this measure relies on the temporal correlation of fMRI signal changes between different parts of the brain, any non-neural activity-related process that affects the signals will influence the measure of functional connectivity, yielding spurious results. To understand the sources of these resting-state fMRI confounds, this article describes the origins of the BOLD signal in terms of MR physics and cerebral physiology. Potential confounds arising from motion, cardiac and respiratory cycles, arterial CO2concentration, blood pressure/cerebral autoregulation, and vasomotion are discussed. Two classes of techniques to remove confounds from resting-state BOLD time series are reviewed: 1) those utilising external recordings of physiology and 2) data-based cleanup methods that only use the resting-state fMRI data itself. Further methods that remove noise from functional connectivity measures at a group level are also discussed. For successful interpretation of resting-state fMRI comparisons and results, noise cleanup is an often over-looked but essential step in the analysis pipeline. {\textcopyright} 2013 Elsevier Inc.},
			archivePrefix = {arXiv},
			arxivId = {NIHMS150003},
			author = {Murphy, Kevin and Birn, Rasmus M. and Bandettini, Peter A.},
			doi = {10.1016/j.neuroimage.2013.04.001},
			eprint = {NIHMS150003},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/0314ce74a22ed689bb8a562f47466a40fd8978a2.pdf:pdf},
			isbn = {1095-9572 (Electronic)$\backslash$r1053-8119 (Linking)},
			issn = {10538119},
			journal = {NeuroImage},
			keywords = {Functional connectivity,Functional magnetic resonance imaging (fMRI),Noise correction,Physiological noise,Resting-state,motion,preprocessing},
			mendeley-tags = {motion,preprocessing},
			pages = {349--359},
			pmid = {23571418},
			publisher = {Elsevier Inc.},
			title = {{Resting-state fMRI confounds and cleanup}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2013.04.001 https://ac.els-cdn.com/S1053811913003170/1-s2.0-S1053811913003170-main.pdf?{\_}tid=4154ba54-10bb-11e8-a93e-00000aab0f27{\&}acdnat=1518525888{\_}ae27ac524e9cabdb09e618eac0f937ee},
			volume = {80},
			year = {2013}
			}

			@article{Dijen2013,
			author = {Dijen, Andrea Van},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Dijen - 2013 - Lowering Arousal and Promoting Sleep- Â â Onset with EEG- Â â Biofeedback A P i l o t s t u d y.pdf:pdf},
			keywords = {Report,alpha,arousal,biofeedback,eeg,insomnia,sleep-onset,theta},
			mendeley-tags = {Report},
			number = {July},
			title = {{Lowering Arousal and Promoting Sleep- Â â Onset with EEG- Â â Biofeedback A P i l o t s t u d y}},
			year = {2013}
			}

			@article{Klein2009,
			abstract = {All fields of neuroscience that employ brain imaging need to communicate their results with reference to anatomical regions. In particular, comparative morphometry and group analysis of functional and physiological data require coregistration of brains to establish correspondences across brain structures. It is well established that linear registration of one brain to another is inadequate for aligning brain structures, so numerous algorithms have emerged to nonlinearly register brains to one another. This study is the largest evaluation of nonlinear deformation algorithms applied to brain image registration ever conducted. Fourteen algorithms from laboratories around the world are evaluated using 8 different error measures. More than 45,000 registrations between 80 manually labeled brains were performed by algorithms including: AIR, ANIMAL, ART, Diffeomorphic Demons, FNIRT, IRTK, JRD-fluid, ROMEO, SICLE, SyN, and four different SPM5 algorithms ("SPM2-type" and regular Normalization, Unified Segmentation, and the DARTEL Toolbox). All of these registrations were preceded by linear registration between the same image pairs using FLIRT. One of the most significant findings of this study is that the relative performances of the registration methods under comparison appear to be little affected by the choice of subject population, labeling protocol, and type of overlap measure. This is important because it suggests that the findings are generalizable to new subject populations that are labeled or evaluated using different labeling protocols. Furthermore, we ranked the 14 methods according to three completely independent analyses (permutation tests, one-way ANOVA tests, and indifference-zone ranking) and derived three almost identical top rankings of the methods. ART, SyN, IRTK, and SPM's DARTEL Toolbox gave the best results according to overlap and distance measures, with ART and SyN delivering the most consistently high accuracy across subjects and label sets. Updates will be published on the http://www.mindboggle.info/papers/ website. {\textcopyright} 2009.},
			archivePrefix = {arXiv},
			arxivId = {1505.03540},
			author = {Klein, Arno and Andersson, Jesper and Ardekani, Babak A. and Ashburner, John and Avants, Brian and Chiang, Ming Chang and Christensen, Gary E. and Collins, D. Louis and Gee, James and Hellier, Pierre and Song, Joo Hyun and Jenkinson, Mark and Lepage, Claude and Rueckert, Daniel and Thompson, Paul and Vercauteren, Tom and Woods, Roger P. and Mann, J. John and Parsey, Ramin V.},
			doi = {10.1016/j.neuroimage.2008.12.037},
			eprint = {1505.03540},
			file = {:Users/lorenafreitas/Library/Containers/com.apple.mail/Data/Library/Mail Downloads/7E366C67-D383-4B50-941F-E095BC29C5AB/Klein et al.{\_}2009{\_}Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration.pdf:pdf},
			isbn = {1095-9572 (Electronic) 1053-8119 (Linking)},
			issn = {10538119},
			journal = {NeuroImage},
			keywords = {dartel,normalisation,template},
			mendeley-tags = {dartel,normalisation,template},
			number = {3},
			pages = {786--802},
			pmid = {19195496},
			publisher = {Elsevier B.V.},
			title = {{Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2008.12.037},
			volume = {46},
			year = {2009}
			}

			@article{Senbabaoglu2014,
			author = {Senbabaoglu, Yasin and Michailidis, George and Li, Jun Z},
			doi = {10.1038/srep06207},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/382aa815369ef45417a581c18339ea134fae846d.pdf:pdf},
			journal = {Scientific Reports},
			number = {6207},
			pmid = {25158761},
			title = {{Critical limitations of consensus clustering in class discovery}},
			url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4145288/pdf/srep06207.pdf},
			volume = {4},
			year = {2014}
			}

			@article{Fatourechi2007,
			abstract = {It is widely accepted in the brain computer interface (BCI) research community that neurological phenomena are the only source of control in any BCI system. Artifacts are undesirable signals that can interfere with neurological phenomena. They may change the characteristics of neurological phenomena or even be mistakenly used as the source of control in BCI systems. Electrooculography (EOG) and electromyography (EMG) artifacts are considered among the most important sources of physiological artifacts in BCI systems. Currently, however, there is no comprehensive review of EMG and EOG artifacts in BCI literature. This paper reviews EOG and EMG artifacts associated with BCI systems and the current methods for dealing with them. More than 250 refereed journal and conference papers are reviewed and categorized based on the type of neurological phenomenon used and the methods employed for handling EOG and EMG artifacts. This study reveals weaknesses in BCI studies related to reporting the methods of handling EMG and EOG artifacts. Most BCI papers do not report whether or not they have considered the presence of EMG and EOG artifacts in the brain signals. Only a small percentage of BCI papers report automated methods for rejection or removal of artifacts in their systems. As the lack of dealing with artifacts may result in the deterioration of the performance of a particular BCI system during practical applications, it is necessary to develop automatic methods to handle artifacts or to design BCI systems whose performance is robust to the presence of artifacts.},
			author = {Fatourechi, Mehrdad and Bashashati, Ali and Ward, Rabab K and Birch, Gary E},
			doi = {10.1016/j.clinph.2006.10.019},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Fatourechi et al. - 2007 - EMG and EOG artifacts in brain computer interface systems A survey.pdf:pdf},
			issn = {1388-2457},
			journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
			keywords = {Algorithms,Artifacts,Brain,Brain: physiology,Computer Simulation,Electromyography,Electrooculography,Humans,User-Computer Interface},
			month = {mar},
			number = {3},
			pages = {480--94},
			pmid = {17169606},
			title = {{EMG and EOG artifacts in brain computer interface systems: A survey.}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/17169606},
			volume = {118},
			year = {2007}
			}

			@article{Kucyi2014,
			abstract = {Humans spend much of their time engaged in stimulus-independent thoughts, colloquially known as "daydreaming" or "mind-wandering." A fundamental question concerns how awake, spontaneous brain activity represents the ongoing cognition of daydreaming versus unconscious processes characterized as "intrinsic." Since daydreaming involves brief cognitive events that spontaneously fluctuate, we tested the hypothesis that the dynamics of brain network functional connectivity (FC) are linked with daydreaming. We determined the general tendency to daydream in healthy adults based on a daydreaming frequency scale (DDF). Subjects then underwent both resting state functional magnetic resonance imaging (rs-fMRI) and fMRI during sensory stimulation with intermittent thought probes to determine the occurrences of mind-wandering events. Brain regions within the default mode network (DMN), purported to be involved in daydreaming, were assessed for 1) static FC across the entire fMRI scans, and 2) dynamic FC based on FC variability (FCV) across 30. s progressively sliding windows of 2. s increments within each scan. We found that during both resting and sensory stimulation states, individual differences in DDF were negatively correlated with static FC between the posterior cingulate cortex and a ventral DMN subsystem involved in future-oriented thought. Dynamic FC analysis revealed that DDF was positively correlated with FCV within the same DMN subsystem in the resting state but not during stimulation. However, dynamic but not static FC, in this subsystem, was positively correlated with an individual's degree of self-reported mind-wandering during sensory stimulation. These findings identify temporal aspects of spontaneous DMN activity that reflect conscious and unconscious processes. {\textcopyright} 2014 Elsevier Inc.},
			author = {Kucyi, Aaron and Davis, Karen D.},
			doi = {10.1016/j.neuroimage.2014.06.044},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/a0a75435b4866ee82fb20a7f8d26ce80d1214804.pdf:pdf},
			isbn = {1053-8119},
			issn = {10959572},
			journal = {NeuroImage},
			keywords = {Awareness,Brain dynamics,Consciousness,Spontaneous cognition,Stimulus-independent thought},
			pages = {471--480},
			pmid = {24973603},
			publisher = {Elsevier Inc.},
			title = {{Dynamic functional connectivity of the default mode network tracks daydreaming}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2014.06.044 http://ac.els-cdn.com/S1053811914005217/1-s2.0-S1053811914005217-main.pdf?{\_}tid=2b8e9086-3fac-11e7-89ea-00000aab0f02{\&}acdnat=1495539615{\_}dca95c975c3096306ae218aac7d8e365},
			volume = {100},
			year = {2014}
			}

			@article{Wager2011,
			author = {Wager, Tor D.},
			doi = {10.3389/conf.fninf.2011.08.00058},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/60e60a504ef91a73972305f71e0336f47895a987.html:html},
			issn = {1662-5196},
			journal = {Frontiers in Neuroinformatics},
			title = {{NeuroSynth: a new platform for large-scale automated synthesis of human functional neuroimaging data}},
			url = {https://www.frontiersin.org/articles/10.3389/fpls.2018.01478/abstract http://www.frontiersin.org/10.3389/conf.fninf.2011.08.00058/event{\_}abstract},
			volume = {5},
			year = {2011}
			}

			@article{Friedrich2014,
			author = {Friedrich, Elisabeth V. C. and Suttie, Neil and Sivanathan, Aparajithan and Lim, Theodore and Louchart, Sandy and Pineda, Jaime A},
			doi = {10.3389/fneng.2014.00021},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Friedrich et al. - 2014 - Brain computer interface game applications for combined neurofeedback and biofeedback treatment for children o.pdf:pdf},
			issn = {1662-6443},
			journal = {Frontiers in Neuroengineering},
			keywords = {asd,autism spectrum disorder,autism spectrum disorder (ASD),bci,brain,brainâcomputer interface (BCI),computer interface,con-,disorder,games,heart rate variability,is an increasingly prevalent,mirror neuron system,mu rhythm,neuroetiology of autism spectrum,neurofeedback and biofeedback,neurofeedback and biofeedback training,social engagement system,training},
			month = {jul},
			number = {July},
			pages = {1--7},
			title = {{Brain computer interface game applications for combined neurofeedback and biofeedback treatment for children on the autism spectrum}},
			url = {http://www.frontiersin.org/Neuroengineering/10.3389/fneng.2014.00021/abstract},
			volume = {7},
			year = {2014}
			}

			@article{OReilly2012,
			abstract = {Psychophysiological interactions (PPIs) analysis is a method for investigating task-specific changes in the relationship between activity in different brain areas, using functional magnetic resonance imaging (fMRI) data. Specifically, PPI analyses identify voxels in which activity is more related to activity in a seed region of interest (seed ROI) in a given psychological context, such as during attention or in the presence of emotive stimuli. In this tutorial, we aim to give a simple conceptual explanation of how PPI analysis works, in order to assist readers in planning and interpreting their own PPI experiments.},
			author = {O'Reilly, Jill X. and Woolrich, Mark W. and Behrens, Timothy E J and Smith, Stephen M. and Johansen-Berg, Heidi},
			doi = {10.1093/scan/nss055},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/fe50243b42c83753b7f6f93e30074a3373d821bd.pdf:pdf},
			isbn = {1749-5024 (Electronic)$\backslash$r1749-5016 (Linking)},
			issn = {17495016},
			journal = {Social Cognitive and Affective Neuroscience},
			keywords = {Functional connectivity,PPI,Psychophysiological interactions,Resting state},
			number = {5},
			pages = {604--609},
			pmid = {22569188},
			title = {{Tools of the trade: Psychophysiological interactions and functional connectivity}},
			url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3375893/pdf/nss055.pdf},
			volume = {7},
			year = {2012}
			}

			@article{Dua2010,
			abstract = {This article presents model predictive controllers (MPCs) and multi-parametric model-based controllers for delivery of anaesthetic agents. The MPC can take into account constraints on drug delivery rates and state of the patient but requires solving an optimization problem at regular time intervals. The multi-parametric controller has all the advantages of the MPC and does not require repetitive solution of optimization problem for its implementation. This is achieved by obtaining the optimal drug delivery rates as a set of explicit functions of the state of the patient. The derivation of the controllers relies on using detailed models of the system. A compartmental model for the delivery of three drugs for anaesthesia is developed. The key feature of this model is that mean arterial pressure, cardiac output and unconsciousness of the patient can be simultaneously regulated. This is achieved by using three drugs: dopamine (DP), sodium nitroprusside (SNP) and isoflurane. A number of dynamic simulation experiments are carried out for the validation of the model. The model is then used for the design of model predictive and multi-parametric controllers, and the performance of the controllers is analyzed.},
			author = {Dua, Pinky and Dua, Vivek and Pistikopoulos, Efstratios N.},
			doi = {10.1007/s11517-010-0604-3},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Dua, Dua, Pistikopoulos - 2010 - Modelling and multi-parametric control for delivery of anaesthetic agents.pdf:pdf},
			isbn = {0140-0118},
			issn = {01400118},
			journal = {Medical and Biological Engineering and Computing},
			keywords = {Compartmental model,Delivery of anaesthetic agents,Model predictive control,Multi-parametric control},
			number = {6},
			pages = {543--553},
			pmid = {20405230},
			title = {{Modelling and multi-parametric control for delivery of anaesthetic agents}},
			volume = {48},
			year = {2010}
			}

			@article{Haufe2014,
			abstract = {The increase in spatiotemporal resolution of neuroimaging devices is accompanied by a trend towards more powerful multivariate analysis methods. Often it is desired to interpret the outcome of these methods with respect to the cognitive processes under study. Here we discuss which methods allow for such interpretations, and provide guidelines for choosing an appropriate analysis for a given experimental goal: For a surgeon who needs to decide where to remove brain tissue it is most important to determine the origin of cognitive functions and associated neural processes. In contrast, when communicating with paralyzed or comatose patients via brain-computer interfaces, it is most important to accurately extract the neural processes specific to a certain mental state. These equally important but complementary objectives require different analysis methods. Determining the origin of neural processes in time or space from the parameters of a data-driven model requires what we call a forward model of the data; such a model explains how the measured data was generated from the neural sources. Examples are general linear models (GLMs). Methods for the extraction of neural information from data can be considered as backward models, as they attempt to reverse the data generating process. Examples are multivariate classifiers. Here we demonstrate that the parameters of forward models are neurophysiologically interpretable in the sense that significant nonzero weights are only observed at channels the activity of which is related to the brain process under study. In contrast, the interpretation of backward model parameters can lead to wrong conclusions regarding the spatial or temporal origin of the neural signals of interest, since significant nonzero weights may also be observed at channels the activity of which is statistically independent of the brain process under study. As a remedy for the linear case, we propose a procedure for transforming backward models into forward models. This procedure enables the neurophysiological interpretation of the parameters of linear backward models. We hope that this work raises awareness for an often encountered problem and provides a theoretical basis for conducting better interpretable multivariate neuroimaging analyses.},
			author = {Haufe, Stefan and Meinecke, Frank and G{\"{o}}rgen, Kai and D{\"{a}}hne, Sven and Haynes, John-Dylan and Blankertz, Benjamin and Bie{\ss}mann, Felix},
			doi = {10.1016/j.neuroimage.2013.10.067},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Haufe et al. - 2014 - On the interpretation of weight vectors of linear models in multivariate neuroimaging.pdf:pdf},
			issn = {1095-9572},
			journal = {NeuroImage},
			keywords = {Algorithms,Brain Mapping,Brain Mapping: methods,Humans,Image Processing, Computer-Assisted,Image Processing, Computer-Assisted: methods,Linear Models,Models, Neurological,Models, Theoretical,Neuroimaging,Neuroimaging: methods},
			month = {feb},
			pages = {96--110},
			pmid = {24239590},
			publisher = {The Authors},
			title = {{On the interpretation of weight vectors of linear models in multivariate neuroimaging.}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/24239590},
			volume = {87},
			year = {2014}
			}

			@article{Macdonald2011,
			abstract = {Parieto-occipital electroencephalogram (EEG) alpha power and subjective reports of attentional state are both associated with visual attention and awareness, but little is currently known about the relationship between these two measures. Here, we bring together these two literatures to explore the relationship between alpha activity and participants' introspective judgments of attentional state as each varied from trial-to-trial during performance of a visual detection task. We collected participants' subjective ratings of perceptual decision confidence and attentional state on continuous scales on each trial of a rapid serial visual presentation detection task while recording EEG. We found that confidence and attentional state ratings were largely uncorrelated with each other, but both were strongly associated with task performance and post-stimulus decision-related EEG activity. Crucially, attentional state ratings were also negatively associated with prestimulus EEG alpha power. Attesting to the robustness of this association, we were able to classify attentional state ratings via prestimulus alpha power on a single-trial basis. Moreover, when we repeated these analyses after smoothing the time series of attentional state ratings and alpha power with increasingly large sliding windows, both the correlations and classification performance improved considerably, with the peaks occurring at a sliding window size of approximately 7âmin worth of trials. Our results therefore suggest that slow fluctuations in attentional state in the order of minutes are reflected in spontaneous alpha power. Since these subjective attentional state ratings were associated with objective measures of both behavior and neural activity, we suggest that they provide a simple and effective estimate of task engagement that could prove useful in operational settings that require human operators to maintain a sustained focus of visual attention.},
			author = {Macdonald, James S P and Mathan, Santosh and Yeung, Nick},
			doi = {10.3389/fpsyg.2011.00082},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Macdonald, Mathan, Yeung - 2011 - Trial-by-Trial Variations in Subjective Attentional State are Reflected in Ongoing Prestimulus EEG Alp.pdf:pdf},
			issn = {1664-1078},
			journal = {Frontiers in psychology},
			keywords = {alpha,attention,attention, alpha, mind-wandering, vigilance, detec,confidence,detection,mind-wandering,p300,vigilance},
			month = {jan},
			number = {May},
			pages = {82},
			pmid = {21687452},
			title = {{Trial-by-Trial Variations in Subjective Attentional State are Reflected in Ongoing Prestimulus EEG Alpha Oscillations.}},
			url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3110334{\&}tool=pmcentrez{\&}rendertype=abstract},
			volume = {2},
			year = {2011}
			}

			@article{Heeger2000,
			author = {Heeger, David J and Huk, Alex C and Geisler, Wilson S and Albrecht, Duane G},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/5038deeed68c9e34bf8cfbd4992f13e7624a2c11.pdf:pdf},
			journal = {Nature neuroscience},
			number = {7},
			pages = {631--2},
			title = {{Spikes versus BOLD : what does neuroimaging tell us about neuronal activity ?}},
			url = {http://www.nature.com/neuro/journal/v3/n7/pdf/nn0700{\_}631.pdf},
			volume = {3},
			year = {2000}
			}

			@article{VonBunau2010,
			abstract = {Neurophysiological measurements obtained from e.g. EEG or fMRI are inherently non-stationary because the properties of the underlying brain processes vary over time. For example, in Brain-Computer-Interfacing (BCI), deteriorating performance (bitrate) is a common phenomenon since the parameters determined during the calibration phase can be suboptimal under the application regime, where the brain state is different, e.g. due to increased tiredness or changes in the experimental paradigm. We show that Stationary Subspace Analysis (SSA), a time series analysis method, can be used to identify the underlying stationary and non-stationary brain sources from high-dimensional EEG measurements. Restricting the BCI to the stationary sources found by SSA can significantly increase the performance. Moreover, SSA yields topographic maps corresponding to stationary- and non-stationary brain sources which reveal their spatial characteristics.},
			author = {von Bunau, Paul and Meinecke, Frank C and Scholler, Simon and Muller, Klaus-Robert},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/von Bunau et al. - 2010 - Finding stationary brain sources in EEG data.pdf:pdf},
			institution = {TU Berlin (Berlin Institute of Technology), Dept. Computer Science, Franklinstr. 28/29, 10587, Germany. buenau@cs.tu-berlin.de},
			isbn = {9781424441242},
			journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference},
			pages = {2810--2813},
			title = {{Finding stationary brain sources in EEG data.}},
			volume = {2010},
			year = {2010}
			}

			@article{Yang2017,
			author = {Yang, Y J Daniel and Sukhodolsky, Denis G and Lei, Jiedi and Dayan, Eran and Pelphrey, Kevin A and Ventola, Pamela},
			doi = {10.1186/s11689-017-9183-z},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/3f9de98684008dc5ffcaa27c442af51c94aa1b7e.pdf:pdf},
			issn = {1866-1947},
			journal = {Journal of Neurodevelopmental Disorders},
			keywords = {ADHD,Anxiety disorders,Autism spectrum disorder,Comorbidity,Default mode network,Disruptive behavior,Neuroimaging,Oppositional defiant disorder,Social perception,adhd,anxiety disorders,autism spectrum disorder,comorbidity,correspondence,danielyang,default mode network,disruptive behavior,edu,gwu,neuroimaging,oppositional defiant disorder,social perception},
			number = {1},
			pages = {1--17},
			publisher = {Journal of Neurodevelopmental Disorders},
			title = {{Distinct neural bases of disruptive behavior and autism symptom severity in boys with autism spectrum disorder}},
			url = {http://dx.doi.org/10.1186/s11689-017-9183-z http://download.springer.com/static/pdf/274/art{\%}253A10.1186{\%}252Fs11689-017-9183-z.pdf?originUrl=http{\%}3A{\%}2F{\%}2Fjneurodevdisorders.biomedcentral.com{\%}2Farticle{\%}2F10.1186{\%}2Fs11689-017-9183-z{\&}token2=exp=1485439656{~}acl},
			volume = {9},
			year = {2017}
			}

			@article{Preti2017,
			abstract = {Resting-state functional magnetic resonance imaging (fMRI) has highlighted the rich structure of brain activity in absence of a task or stimulus. A great effort has been dedicated in the last two decades to investigate functional connectivity (FC), i.e. the functional interplay between different regions of the brain, which was for a long time assumed to have stationary nature. Only recently was the dynamic behaviour of FC revealed, showing that on top of correlational patterns of spontaneous fMRI signal fluctuations, connectivity between different brain regions exhibits meaningful variations within a typical resting-state fMRI experiment. As a consequence, a considerable amount of work has been directed to assessing and characterising dynamic FC (dFC), and several different approaches were explored to identify relevant FC fluctuations. At the same time, several questions were raised about the nature of dFC, which would be of interest only if brought back to a neural origin. In support of this, correlations with electroencephalography (EEG) recordings, demographic and behavioural data were established, and various clinical applications were explored, where the potential of dFC could be preliminarily demonstrated. In this review, we aim to provide a comprehensive description of the dFC approaches proposed so far, and point at the directions that we see as most promising for the future developments of the field. Advantages and pitfalls of dFC analyses are addressed, helping the readers to orient themselves through the complex web of available methodologies and tools.},
			archivePrefix = {arXiv},
			arxivId = {1511.02976},
			author = {Preti, Maria Giulia and Bolton, Thomas AW and {Van De Ville}, Dimitri},
			doi = {10.1016/j.neuroimage.2016.12.061},
			eprint = {1511.02976},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/82520a646e5c81b0bee1a9b7f0ac277d1969ec03.pdf:pdf},
			isbn = {0896-6273},
			issn = {10959572},
			journal = {NeuroImage},
			keywords = {Dynamic functional connectivity,Dynamic graph analysis,Frame-wise description,Sliding window analysis,State characterization,Temporal modeling,Time/frequency analysis},
			number = {December 2016},
			pages = {41--54},
			pmid = {27693256},
			title = {{The dynamic functional connectome: State-of-the-art and perspectives}},
			url = {https://ac.els-cdn.com/S1053811916307881/1-s2.0-S1053811916307881-main.pdf?{\_}tid=740fef8f-835b-4993-86d3-a6f60b841679{\&}acdnat=1523360042{\_}29ea611d8892cbd9e44c8ac92ff16e62},
			volume = {160},
			year = {2017}
			}

			@article{Jung2000,
			author = {Jung, Tzyy-Ping and Makeig, Scott and Humphries, Colin and Lee, Te-Won and McKeown, Martin J. and Iragui, Vicente and Sejnowski, Terrence J.},
			doi = {10.1111/1469-8986.3720163},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Jung et al. - 2000 - Removing electroencephalographic artifacts by blind source separation.pdf:pdf},
			issn = {0048-5772},
			journal = {Psychophysiology},
			month = {mar},
			number = {2},
			pages = {163--178},
			title = {{Removing electroencephalographic artifacts by blind source separation}},
			url = {http://doi.wiley.com/10.1111/1469-8986.3720163},
			volume = {37},
			year = {2000}
			}

			@book{Hebb1949,
			address = {New York},
			author = {Hebb, D.O.},
			publisher = {Wiley {\&} Sons},
			title = {{The Organization of Behavior}},
			year = {1949}
			}

			@article{Yousefi2015,
			author = {Yousefi, Mahdi and {Van Heusden}, Klaske and Dumont, Guy a. and Ansermino, J. Mark},
			doi = {10.1213/ANE.0b013e3182973687},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Yousefi et al. - 2015 - Safety-Preserving Closed-Loop Control of Anesthesia.pdf:pdf},
			isbn = {9781424492701},
			issn = {0003-2999},
			journal = {Anesthesia {\&} Analgesia},
			keywords = {Product development process,Safety,Simulation,learning and training},
			number = {5},
			pages = {1130--1138},
			title = {{Safety-Preserving Closed-Loop Control of Anesthesia}},
			url = {http://content.wkhealth.com/linkback/openurl?sid=WKPTLP:landingpage{\&}an=00000539-201311000-00016},
			volume = {117},
			year = {2015}
			}

			@article{Barr2002,
			author = {Barr, RG},
			doi = {10.1001/archpedi.156.12.1172},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/a72dc782a4785dbe5acd39af054178c688eb2333.pdf:pdf},
			issn = {1072-4710},
			journal = {Archives of pediatrics {\&} adolescent medicine},
			pages = {1172--1174},
			pmid = {12444822},
			title = {{Changing our understanding of infant colic}},
			url = {http://archpedi.jamanetwork.com/article.aspx?articleid=204166},
			volume = {156},
			year = {2002}
			}

			@article{Behrmann2004,
			abstract = {The parietal lobe forms about 20{\%} of the human cerebral cortex and is divided into two major regions, the somatosensory cortex and the posterior parietal cortex. Posterior parietal cortex, located at the junction of multiple sensory regions, projects to several cortical and subcortical areas and is engaged in a host of cognitive operations. One such operation is selective attention, the process where by the input is filtered and a subset of the information is selected for preferential processing. Recent neuroimaging and neuropsychological studies have provided a more fine-grained understanding of the relationship between brain and behavior in the domain of selective attention.},
			author = {Behrmann, Marlene and Geng, Joy J and Shomstein, Sarah},
			doi = {10.1016/j.conb.2004.03.012},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Behrmann, Geng, Shomstein - 2004 - Parietal cortex and attention.pdf:pdf},
			issn = {0959-4388},
			journal = {Current opinion in neurobiology},
			keywords = {Attention,Attention: physiology,Cues,Humans,Magnetic Resonance Imaging,Neural Pathways,Neural Pathways: anatomy {\&} histology,Neural Pathways: physiology,Parietal Lobe,Parietal Lobe: anatomy {\&} histology,Parietal Lobe: physiology,Perception,Perception: physiology,Perceptual Disorders,Perceptual Disorders: pathology,Perceptual Disorders: physiopathology,Psychomotor Performance,Psychomotor Performance: physiology,Sensation,Sensation: physiology},
			month = {apr},
			number = {2},
			pages = {212--7},
			pmid = {15082327},
			title = {{Parietal cortex and attention.}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/15082327},
			volume = {14},
			year = {2004}
			}

			@article{Zhang2012,
			abstract = {Electroencephalographic gamma band oscillations (GBOs) induced over the human primary somatosensory cortex (SI) by nociceptive stimuli have been hypothesized to reflect cortical processing involved directly in pain perception, because their magnitude correlates with pain intensity. However, as stimuli perceived as more painful are also more salient, an alternative interpretation of this correlation is that GBOs reflect unspecific stimulus-triggered attentional processing. In fact, this is suggested by recent observations that other features of the electroencephalographic (EEG) response correlate with pain perception when stimuli are presented in isolation, but not when their saliency is reduced by repetition. Here, by delivering trains of three nociceptive stimuli at a constant 1 s interval, and using different energies to elicit graded pain intensities, we demonstrate that GBOs recorded over SI always predict the subjective pain intensity, even when saliency is reduced by repetition. These results provide evidence for a close relationship between GBOs and the cortical activity subserving pain perception.},
			author = {Zhang, Z. G. and Hu, L. and Hung, Y. S. and Mouraux, A. and Iannetti, G. D.},
			doi = {10.1523/JNEUROSCI.5877-11.2012},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/c2cd2fab677b9a61d406b050500b2d6999ca9df1.pdf:pdf},
			isbn = {1529-2401 (Electronic)$\backslash$r0270-6474 (Linking)},
			issn = {0270-6474},
			journal = {Journal of Neuroscience},
			number = {22},
			pages = {7429--7438},
			pmid = {22649223},
			title = {{Gamma-Band Oscillations in the Primary Somatosensory Cortex--A Direct and Obligatory Correlate of Subjective Pain Intensity}},
			url = {http://www.jneurosci.org/cgi/doi/10.1523/JNEUROSCI.5877-11.2012},
			volume = {32},
			year = {2012}
			}

			@article{Hasson2004,
			abstract = {To what extent do all brains work alike during natural conditions? We explored this question by letting five subjects freely view half an hour of a popular movie while undergoing functional brain imaging. Applying an unbiased analysis in which spatiotemporal activity patterns in one brain were used to "model" activity in another brain, we found a striking level of voxel-by-voxel synchronization between individuals, not only in primary and secondary visual and auditory areas but also in association cortices. The results reveal a surprising tendency of individual brains to "tick collectively" during natural vision. The intersubject synchronization consisted of a widespread cortical activation pattern correlated with emotionally arousing scenes and regionally selective components. The characteristics of these activations were revealed with the use of an open-ended "reverse-correlation" approach, which inverts the conventional analysis by letting the brain signals themselves "pick up" the optimal stimuli for each specialized cortical area.},
			archivePrefix = {arXiv},
			arxivId = {arXiv:1401.4290v2},
			author = {Hasson, Uri and Nir, Yuval and Levy, Ifat and Fuhrmann, Galit and Malach, Rafael},
			doi = {10.1126/science.1089506},
			eprint = {arXiv:1401.4290v2},
			file = {:Users/lorenafreitas/Downloads/Articles for Journal Club-20170517/Hasson{\_}Intersubject{\_}Synchronization.pdf:pdf},
			isbn = {1095-9203 (Electronic)},
			issn = {00071196},
			journal = {Science},
			keywords = {Adult,Attention,Auditory Cortex,Auditory Cortex: physiology,Brain Mapping,Cerebral Cortex,Cerebral Cortex: physiology,Emotions,Face,Female,Humans,Magnetic Resonance Imaging,Male,Middle Aged,Motion Pictures as Topic,Occipital Lobe,Occipital Lobe: physiology,Ocular,Photic Stimulation,Temporal Lobe,Temporal Lobe: physiology,Vision,Visual Cortex,Visual Cortex: physiology,Visual Perception},
			number = {MARCH},
			pages = {1634--1640},
			pmid = {15016991},
			title = {{Natural Vision}},
			url = {http://science.sciencemag.org/content/303/5664/1634.abstract},
			volume = {303},
			year = {2004}
			}

			@article{Stins2009,
			author = {Stins, John F. and Laureys, Steven},
			doi = {10.1007/s11097-009-9124-8},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Stins, Laureys - 2009 - Thought translation, tennis and Turing tests in the vegetative state.pdf:pdf},
			issn = {1568-7759},
			journal = {Phenomenology and the Cognitive Sciences},
			keywords = {consciousness,other minds,turing test,vegetative state},
			month = {mar},
			number = {3},
			pages = {361--370},
			title = {{Thought translation, tennis and Turing tests in the vegetative state}},
			url = {http://link.springer.com/10.1007/s11097-009-9124-8},
			volume = {8},
			year = {2009}
			}

			@article{Gorgolewski2011,
			abstract = {Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. Several sophisticated software packages (e.g., AFNI, BrainVoyager, FSL, FreeSurfer, Nipy, R, SPM) are used to process and analyze large and often diverse (highly multi-dimensional) data. However, this heterogeneous collection of specialized applications creates several issues that hinder replicable, efficient, and optimal use of neuroimaging analysis approaches: (1) No uniform access to neuroimaging analysis software and usage information; (2) No framework for comparative algorithm development and dissemination; (3) Personnel turnover in laboratories often limits methodological continuity and training new personnel takes time; (4) Neuroimaging software packages do not address computational efficiency; and (5) Methods sections in journal articles are inadequate for reproducing results. To address these issues, we present Nipype (Neuroimaging in Python: Pipelines and Interfaces; http://nipy.org/nipype), an open-source, community-developed, software package, and scriptable library. Nipype solves the issues by providing Interfaces to existing neuroimaging software with uniform usage semantics and by facilitating interaction between these packages using Workflows. Nipype provides an environment that encourages interactive exploration of algorithms, eases the design of Workflows within and between packages, allows rapid comparative development of algorithms and reduces the learning curve necessary to use different packages. Nipype supports both local and remote execution on multi-core machines and clusters, without additional scripting. Nipype is Berkeley Software Distribution licensed, allowing anyone unrestricted usage. An open, community-driven development philosophy allows the software to quickly adapt and address the varied needs of the evolving neuroimaging community, especially in the context of increasing demand for reproducible research.},
			author = {Gorgolewski, Krzysztof and Burns, Christopher D and Madison, Cindee and Clark, Dav and Halchenko, Yaroslav O and Waskom, Michael L and Ghosh, Satrajit S},
			doi = {10.3389/fninf.2011.00013},
			file = {:Users/lorenafreitas/Downloads/fninf-05-00013.pdf:pdf},
			isbn = {1662-5196},
			issn = {1662-5196},
			journal = {Frontiers in neuroinformatics},
			keywords = {data processing,neuroimaging,neuroimaging, data processing, workflow, pipeline,,pipeline,python,reproducible research,workflow},
			number = {August},
			pages = {13},
			pmid = {21897815},
			title = {{Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python.}},
			url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3159964{\&}tool=pmcentrez{\&}rendertype=abstract},
			volume = {5},
			year = {2011}
			}

			@article{Smyser2015,
			author = {Smyser, Christopher D and Neil, Jeffrey J and Louis, St},
			doi = {10.1053/j.semperi.2015.01.006},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/8169f6ba6d5853a272623eddf65a427804788111.pdf:pdf},
			issn = {0146-0005},
			journal = {Seminars in Perinatology},
			keywords = {resting-state functional mri},
			number = {2},
			pages = {130--140},
			publisher = {Elsevier},
			title = {{Use of resting-state functional MRI to study brain development and injury in neonates}},
			url = {http://dx.doi.org/10.1053/j.semperi.2015.01.006 http://ac.els-cdn.com/S0146000515000075/1-s2.0-S0146000515000075-main.pdf?{\_}tid=cdd3584a-357f-11e7-899a-00000aacb35f{\&}acdnat=1494421048{\_}add082429b8577731423654fe419eda2},
			volume = {39},
			year = {2015}
			}

			@article{Eavani2015,
			title={Identifying sparse connectivity patterns in the brain using resting-state fMRI},
			author={Eavani, Harini and Satterthwaite, Theodore D and Filipovych, Roman and Gur, Raquel E and Gur, Ruben C and Davatzikos, Christos},
			journal={Neuroimage},
			volume={105},
			pages={286--299},
			year={2015},
			publisher={Elsevier}
			}

			@article{Eavani2013,
			abstract = {Research in recent years has provided some evidence of temporal non-stationarity of functional connectivity in resting state fMRI. In this paper, we present a novel methodology that can decode connectivity dynamics into a temporal sequence of hidden network âstatesâ for each subject, using a Hidden Markov Modeling (HMM) framework. Each state is characterized by a unique covariance matrix or whole-brain network. Our model generates these covariance matrices from a common but unknown set of sparse basis networks, which capture the range of functional activity co-variations of regions of interest (ROIs). Distinct hidden states arise due to a variation in the strengths of these basis networks. Thus, our generative model combines a HMM framework with sparse basis learning of positive definite matrices. Results on simulated fMRI data show that our method can effectively recover underlying basis networks as well as hidden states. We apply this method on a normative dataset of resting state fMRI scans. Results indicate that the functional activity of a subject at any point during the scan is composed of combinations of overlapping task- positive/negative pairs of networks as revealed by our basis. Distinct hidden temporal states are produced due to a different set of basis networks dominating the covariance pattern in each state. Keywords},
			author = {Eavani, Harini and Satterthwaite, Theodore D. and Gur, Raquel E. and Gur, Ruben C. and Davatzikos, Christos},
			doi = {10.1007/978-3-642-38868-2_36},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/84ddeae2965dd12e68e33b9ef33d18e64cfbae20.pdf:pdf},
			isbn = {9783642388675},
			issn = {03029743},
			journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
			keywords = {functional connectivity,resting state fMRI,temporal network dynamics},
			pages = {426--437},
			pmid = {24683988},
			title = {{Unsupervised learning of functional network dynamics in resting state fMRI}},
			url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3974209/pdf/nihms-504470.pdf},
			volume = {7917 LNCS},
			year = {2013}
			}

			@article{Chen2015b,
			author = {Chen, Jingyuan E and Glover, Gary H},
			doi = {10.1007/s11065-015-9294-9},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/6d5d0e73f208afe77ff4ca4f099f30bd3b9c4280.pdf:pdf},
			keywords = {bold,fmri,methods,resting-state},
			pages = {289--313},
			title = {{Functional Magnetic Resonance Imaging Methods}},
			url = {http://download.springer.com/static/pdf/426/art{\%}253A10.1007{\%}252Fs11065-015-9294-9.pdf?originUrl=http{\%}3A{\%}2F{\%}2Flink.springer.com{\%}2Farticle{\%}2F10.1007{\%}2Fs11065-015-9294-9{\&}token2=exp=1494586918{~}acl={\%}2Fstatic{\%}2Fpdf{\%}2F426{\%}2Fart{\%}25253A10.1007{\%}25252Fs11065-015-929},
			volume = {2},
			year = {2015}
			}

			@article{Calhoun2001b,
			title={A method for making group inferences from functional MRI data using independent component analysis},
			author={Calhoun, Vince D and Adali, Tulay and Pearlson, Godfrey D and Pekar, James J},
			journal={Human brain mapping},
			volume={14},
			number={3},
			pages={140--151},
			year={2001},
			publisher={Wiley Online Library}
			}

			@article{Calhoun2001,
			author = {Calhoun, V D and Adali, T and Mcginty, V B and Pekar, J J and Watson, T D and Pearlson, G D},
			doi = {10.1006/nimg.2001.0921},
			file = {:Users/lorenafreitas/Downloads/1-s2.0-S1053811901909218-main.pdf:pdf},
			journal = {NeuroImage},
			keywords = {brain,fmri,functional,independent component analysis,tion,visual percep-},
			pages = {1080--1088},
			title = {{fMRI Activation in a Visual-Perception Task : Network of Areas Detected Using the General Linear Model and Independent Components Analysis}},
			volume = {1088},
			year = {2001}
			}

			@article{Fawcett2006,
			author = {Fawcett, Tom},
			doi = {10.1016/j.patrec.2005.10.010},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Fawcett - 2006 - An introduction to ROC analysis.pdf:pdf},
			issn = {01678655},
			journal = {Pattern Recognition Letters},
			keywords = {classifier evaluation,evaluation metrics,roc analysis},
			month = {jun},
			number = {8},
			pages = {861--874},
			title = {{An introduction to ROC analysis}},
			url = {http://linkinghub.elsevier.com/retrieve/pii/S016786550500303X},
			volume = {27},
			year = {2006}
			}

			@article{Posse1999,
			author = {Posse, Stefan and Wiese, Stefan and Gembris, Daniel and Mathiak, Klaus and {Kessler, Christoph} and Grosse-Ruyken, Maria-Liisa and {Elghahwagi, Barbara} and Richards, Todd and Dager, Stephen and Kiselev, Valerij},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/55ce04e674f18a79f7d64b2d65273a0da52aaf1f.html:html},
			journal = {Magnetic Resonance in Medicine},
			pages = {87--97},
			title = {{Enhancement of BOLD-Contrast Sensitivity by Single-Shot Multi-Echo Functional MR Imaging}},
			url = {http://onlinelibrary.wiley.com/doi/10.1002/(SICI)1522-2594(199907)42:1{\%}3C87::AID-MRM13{\%}3E3.0.CO;2-O/epdf},
			volume = {42},
			year = {1999}
			}

			@article{Ren2016,
			abstract = {The analysis of the topology and organisation of brain networks is known to greatly benefit from network measures in graph theory. However, to evaluate dynamic changes of brain functional connectivity, more sophisticated quantitative metrics characterising temporal evolution of brain topological features are required. To simplify conversion of time-varying brain connectivity to a static graph representation is straightforward but the procedure loses temporal information that could be critical in understanding the brain functions. To extend the understandings of functional segregation and integration to a dynamic fashion, we recommend dynamic graph metrics to characterise temporal changes of topological features of brain networks. This study investigated functional segregation and integration of brain networks over time by dynamic graph metrics derived from EEG signals during an experimental protocol: performance of complex flight simulation tasks with multiple levels of difficulty. We modelled time-varying brain functional connectivity as multilayer networks, in which each layer models brain connectivity at time window t + t. Dynamic graph metrics were calculated to quantify temporal and topological properties of the network. Results show that brain networks under the performance of complex tasks reveal a dynamic small-world architecture with a number of frequently connected nodes or hubs, which supports the balance of information segregation and integration in brain over time. The results also show that greater cognitive workloads caused by more difficult tasks induced a more globally efficient but less clustered dynamic small-world functional network. Our study illustrates that task-related changes of functional brain network segregation and integration can be characterised by dynamic graph metrics.},
			author = {Ren, Shen and Li, Junhua and Taya, Fumihiko and DeSouza, Joshua and Thakor, Nitish V. and Bezerianos, Anastasios},
			doi = {10.1109/TNSRE.2016.2597961},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Ren et al. - 2016 - Dynamic Functional Segregation and Integration in Human Brain Network during Complex Tasks.pdf:pdf},
			issn = {15344320},
			journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},
			keywords = {Brain Connectivity,Complex Task,Dynamic Brain Network,Dynamic Graph Metrics,EEG,Segregation and Integration,Temporal Network},
			number = {99},
			pages = {1--11},
			title = {{Dynamic Functional Segregation and Integration in Human Brain Network during Complex Tasks}},
			volume = {PP},
			year = {2016}
			}

			@article{Damaraju2018,
			author = {Petridou, Natalia and Gaudes, C{\'{e}}sar Caballero and Dryden, Ian L. and Francis, Susan T. and Gowland, Penny A.},
			doi = {10.1002/hbm.21513},
			file = {:Users/lorenafreitas/Downloads/380741.full.pdf:pdf},
			issn = {10659471},
			journal = {Human Brain Mapping},
			month = {jun},
			number = {6},
			pages = {1319--1329},
			title = {{Periods of rest in fMRI contain individual spontaneous events which are related to slowly fluctuating spontaneous activity}},
			url = {http://doi.wiley.com/10.1002/hbm.21513},
			volume = {34},
			year = {2013}
			}

			@article{Sethi2006,
			abstract = {Artifacts are signals recorded on the electroencephalogram that are not cerebral in origin and can be divided into physiological and non-physiological artifacts. Physiological artifacts are generated from the patient itself and include cardiac, glossokinetic, muscle, eye movement, respiratory and pulse artifact among many others. The EEG recording can be contaminated by numerous non-physiological artifacts generated from the immediate patient surroundings. Common non-physiological artifacts include those generated by monitoring devices, infusion pumps and suctioning devices though electrical devices like mobile phones may also contaminate the EEG record 1},
			author = {Sethi, N and Sethi, P and Torgovnick, J and Arsura, E},
			journal = {The Internet Journal of Neuromonitoring},
			number = {2},
			title = {{Physiological and non-physiological EEG artifacts}},
			volume = {5},
			year = {2006}
			}

			@article{Bressler2016,
			abstract = {Many researchers and clinicians in cognitive neuroscience hold to a modular view of cognitive function in which the cerebral cortex operates by the activation of areas with circumscribed elementary cognitive functions. Yet an ongoing paradigm shift to a dynamic network perspective is underway. This new viewpoint treats cortical function as arising from the coordination dynamics within and between cortical regions. Cortical coordination dynamics arises due to the unidirectional influences imposed on a cortical area by inputs from other areas that project to it, combined with the projection reciprocity that characterizes cortical connectivity and gives rise to reentrant processing. As a result, cortical dynamics exhibits both segregative and integrative tendencies and gives rise to both cooperative and competitive relations within and between cortical areas that are hypothesized to underlie the emergence of cognition in brains.},
			author = {Bressler, Steven L. and Kelso, J. A Scott},
			doi = {10.3389/fnins.2016.00397},
			file = {:Users/lorenafreitas/Downloads/fnins-10-00397.pdf:pdf},
			isbn = {1662-453X},
			issn = {1662453X},
			journal = {Frontiers in Neuroscience},
			keywords = {Cerebral cortex,Computational context,Event-related potential,HKB model,Interareal interaction,Local field potential,Neuronal communication,Relative coordination},
			number = {SEP},
			pages = {1--7},
			title = {{Coordination dynamics in cognitive neuroscience}},
			volume = {10},
			year = {2016}
			}

			@article{Friston1997,
			abstract = {In this paper we introduce the idea of explaining responses, in one cortical area, in terms of an interaction between the influence of another area and some experimental (sensory or task-related) parameter. We refer to these effects as psychophysiological interactions and relate them to interactions based solely on experimental factors (i.e., psychological interactions), in factorial designs, and interactions among neurophysiological measurements (i.e., physiological interactions). We have framed psychophysiological interactions in terms of functional integration by noting that the degree to which the activity in one area can be predicted, on the basis of activity in another, corresponds to the contribution of the second to the first, where this contribution can be related to effective connectivity. A psychophysiological interaction means that the contribution of one area to another changes significantly with the experimental or psychological context. Alternatively these interactions can be thought of as a contribution-dependent change in regional responses to an experimental or psychological factor. In other words the contribution can be thought of as modulating the responses elicited by a particular stimulus or psychological process. The potential importance of this approach lies in (i) conferring a degree of functional specificity on this aspect of effective connectivity and (ii) providing a model of modulation, where the contribution from a distal area can be considered to modulate responses to the psychological or stimulus-specific factor defining the interaction. Although distinct in neurobiological terms, these are equivalent perspectives on the same underlying interaction. We illustrate these points using a functional magnetic resonance imaging study of attention to visual motion and a position emission tomography study of visual priming. We focus on interactions among extrastriate, inferotemporal, and posterior parietal regions during visual processing, under different attentional and perceptual conditions.},
			author = {Friston, K.J and Buechel, C and Fink, G.R and Morris, J and Rolls, E and Dolan, R.J},
			doi = {10.1006/nimg.1997.0291},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Friston et al. - 1997 - Psychophysiological and Modulatory Interactions in Neuroimaging.pdf:pdf},
			isbn = {1053-8119 (Print)$\backslash$n1053-8119 (Linking)},
			issn = {10538119},
			journal = {NeuroImage},
			number = {3},
			pages = {218--229},
			pmid = {9344826},
			title = {{Psychophysiological and Modulatory Interactions in Neuroimaging}},
			url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811997902913},
			volume = {6},
			year = {1997}
			}

			@article{Blamire1992,
			author = {Blamire, Andrew M and Ogawab, Seiji and Ugurbilc, Kamil and Rothmand, Douglas and Mccarthye, Gregory and Ellermannc, Jutta M and Hyderf, Fahmeed and Rattner, Zachary and Shulmana, Robert G},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/621dba0838ae695f697987bc5ca26d61224313b3.pdf:pdf},
			journal = {Proceedings of the National Academy of Sciences},
			number = {November},
			pages = {11069--11073},
			title = {{Dynamic mapping of the human visual cortex by high-speed magnetic resonance imaging}},
			url = {http://www.pnas.org/content/89/22/11069.full.pdf},
			volume = {89},
			year = {1992}
			}

			@article{Menon2011,
			abstract = {The science of large-scale brain networks offers a powerful paradigm for investigating cognitive and affective dysfunction in psychiatric and neurological disorders. This review examines recent conceptual and methodological developments which are contributing to a paradigm shift in the study of psychopathology. I summarize methods for characterizing aberrant brain networks and demonstrate how network analysis provides novel insights into dysfunctional brain architecture. Deficits in access, engagement and disengagement of large-scale neurocognitive networks are shown to play a prominent role in several disorders including schizophrenia, depression, anxiety, dementia and autism. Synthesizing recent research, I propose a triple network model of aberrant saliency mapping and cognitive dysfunction in psychopathology, emphasizing the surprising parallels that are beginning to emerge across psychiatric and neurological disorders. {\textcopyright} 2011 Elsevier Ltd.},
			archivePrefix = {arXiv},
			arxivId = {NIHMS150003},
			author = {Menon, Vinod},
			doi = {10.1016/j.tics.2011.08.003},
			eprint = {NIHMS150003},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/a0d86da2e5a7571660bbd1c3d0a319b82cee45c6.pdf:pdf},
			isbn = {1879-307X (Electronic)$\backslash$n1364-6613 (Linking)},
			issn = {13646613},
			journal = {Trends in Cognitive Sciences},
			number = {10},
			pages = {483--506},
			pmid = {21908230},
			publisher = {Elsevier Ltd},
			title = {{Large-scale brain networks and psychopathology: A unifying triple network model}},
			url = {http://dx.doi.org/10.1016/j.tics.2011.08.003 https://ac.els-cdn.com/S1364661311001719/1-s2.0-S1364661311001719-main.pdf?{\_}tid=da2ca99e-db41-4468-88dc-2632db435dd0{\&}acdnat=1544202469{\_}86a9f5856a9e03671c5b5cb1722d77b9},
			volume = {15},
			year = {2011}
			}

			@article{Critchley2003,
			author = {Critchley, Hugo D and Mathias, Christopher J and Josephs, Oliver and Doherty, John O and Zanini, Sergio and Dewar, Bonnie-kate and Cipolotti, Lisa and Shallice, Tim and Dolan, Raymond J},
			doi = {10.1093/brain/awg216},
			file = {:Users/lorenafreitas/Downloads/critchley2003.pdf:pdf},
			journal = {Brain},
			keywords = {abbreviations,acc,anterior cingulate cortex,autonomic,cingulate cortex,cognition,fmri,functional mri,heart rate,hf,high frequency,hrv,lf,low frequency,sympathetic,variability},
			pages = {2139--2152},
			title = {{Human cingulate cortex and autonomic control : converging neuroimaging and clinical evidence}},
			year = {2003}
			}

			@article{Young2014b,
			abstract = {This study aims to examine the changes in task-related brain activity induced by rehabilitative therapy using brain-computer interface (BCI) technologies and whether these changes are relevant to functional gains achieved through the use of these therapies. Stroke patients with persistent upper-extremity motor deficits received interventional rehabilitation therapy using a closed-loop neurofeedback BCI device (n = 8) or no therapy (n = 6). Behavioral assessments using the Stroke Impact Scale, the Action Research Arm Test (ARAT), and the Nine-Hole Peg Test (9-HPT) as well as task-based fMRI scans were conducted before, during, after, and 1 month after therapy administration or at analogous intervals in the absence of therapy. Laterality Index (LI) values during finger tapping of each hand were calculated for each time point and assessed for correlation with behavioral outcomes. Brain activity during finger tapping of each hand shifted over the course of BCI therapy, but not in the absence of therapy, to greater involvement of the non-lesioned hemisphere (and lesser involvement of the stroke-lesioned hemisphere) as measured by LI. Moreover, changes from baseline LI values during finger tapping of the impaired hand were correlated with gains in both objective and subjective behavioral measures. These findings suggest that the administration of interventional BCI therapy can induce differential changes in brain activity patterns between the lesioned and non-lesioned hemispheres and that these brain changes are associated with changes in specific motor functions.},
			author = {Young, Brittany M and Nigogosyan, Zack and Walton, L{\'{e}}o M and Song, Jie and Nair, Veena a and Grogan, Scott W and Tyler, Mitchell E and Edwards, Dorothy F and Caldera, Kristin and Sattin, Justin a and Williams, Justin C and Prabhakaran, Vivek},
			doi = {10.3389/fneng.2014.00026},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Young et al. - 2014 - Changes in functional brain organization and behavioral correlations after rehabilitative therapy using a brain-co.pdf:pdf},
			issn = {1662-6443},
			journal = {Frontiers in neuroengineering},
			keywords = {bci therapy,brain-computer interface,brain-computer interface, stroke rehabilitation, l,laterality index,li,stroke rehabilitation,ue motor recovery},
			month = {jan},
			number = {July},
			pages = {26},
			pmid = {25076886},
			title = {{Changes in functional brain organization and behavioral correlations after rehabilitative therapy using a brain-computer interface.}},
			url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4097124{\&}tool=pmcentrez{\&}rendertype=abstract},
			volume = {7},
			year = {2014}
			}

			@article{CaballeroGaudes2017,
			abstract = {Blood oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI) has rapidly become a popular technique for the investigation of brain function in healthy individuals, patients as well as in animal studies. However, the BOLD signal arises from a complex mixture of neuronal, metabolic and vascular processes, being therefore an indirect measure of neuronal activity, which is further severely corrupted by multiple non-neuronal fluctuations of instrumental, physiological or subject-specific origin. This review aims to provide a comprehensive summary of existing methods for cleaning the BOLD fMRI signal. The description is given from a methodological point of view, focusing on the operation of the different techniques in addition to pointing out the advantages and limitations in their application. Since motion-related and physiological noise fluctuations are two of the main noise components of the signal, techniques targeting their removal are primarily addressed, including both data-driven approaches and using external recordings. Data-driven approaches, which are less specific in the assumed model and can simultaneously reduce multiple noise fluctuations, are mainly based on data decomposition techniques such as principal and independent component analysis. Importantly, the usefulness of strategies that benefit from the information available in the phase component of the signal, or in multiple signal echoes is also highlighted. The use of global signal regression for denoising is also addressed. Finally, practical recommendations regarding the optimization of the preprocessing pipeline for the purpose of denoising and future venues of research are indicated. Through the review, we summarize the importance of signal denoising as an essential step in the analysis pipeline of task-based and resting state fMRI studies.},
			author = {Caballero-Gaudes, C{\'{e}}sar and Reynolds, Richard C.},
			doi = {10.1016/j.neuroimage.2016.12.018},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/dcc05878a7c6fb162b57335db9f456d9d25e6d37.pdf:pdf},
			issn = {10959572},
			journal = {NeuroImage},
			keywords = {BOLD fMRI,Denoising methods,Motion artifacts,Multi-echo,Phase-based methods,Physiological noise,motion,preprocessing},
			mendeley-tags = {motion,preprocessing},
			number = {December 2016},
			pages = {128--149},
			pmid = {27956209},
			publisher = {Elsevier},
			title = {{Methods for cleaning the BOLD fMRI signal}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2016.12.018 https://ac.els-cdn.com/S1053811916307418/1-s2.0-S1053811916307418-main.pdf?{\_}tid=4d9da1b6-0cf3-11e8-b44b-00000aab0f26{\&}acdnat=1518110155{\_}67628ffd221945d1474611a55f364f34},
			volume = {154},
			year = {2017}
			}

			@article{Friston1995,
			author = {Friston, K.J. and {A.P. Holmes} and {K.J. Worsley} and {J.B. Poline} and {C. Frith} and {R.S.J. Frackowiak.}},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Friston et al. - 1995 - Statistical Parametric Maps in Functional Imaging A General Linear Approach.pdf:pdf},
			journal = {Human Brain Mapping},
			keywords = {analysis of variance,functional,functional anatomy,gaussian fields,general linear model,imaging,statistical parametric maps,statistics},
			pages = {189--210},
			title = {{Statistical Parametric Maps in Functional Imaging: A General Linear Approach}},
			volume = {2},
			year = {1995}
			}

			@article{Thomason2017,
			author = {Thomason, Moriah E and Scheinost, Dustin and Manning, Janessa H and Grove, Lauren E and Hect, Jasmine and Marshall, Narcis and Hernandez-andrade, Edgar and Berman, Susan and Pappas, Athina and Yeo, Lami and Hassan, Sonia S and Constable, R Todd and Ment, Laura R},
			doi = {10.1038/srep39286},
			file = {:Users/lorenafreitas/Downloads/srep39286 (1).pdf:pdf},
			journal = {Nature Publishing Group},
			number = {June 2016},
			pages = {1--10},
			publisher = {Nature Publishing Group},
			title = {{Weak functional connectivity in the human fetal brain prior to preterm birth}},
			url = {http://dx.doi.org/10.1038/srep39286},
			year = {2017}
			}

			@article{Thomas2013,
			abstract = {Brain-Computer Interface (BCI) is an alternative communication and control channel between brain and computer which finds applications in neuroprosthetics, brain wave controlled computer games etc. This paper proposes an Electroencephalogram (EEG) based neurofeedback computer game that allows the player to control the game with the help of attention based brain signals. The proposed game protocol requires the player to memorize a set of numbers in a matrix, and to correctly fill the matrix using his attention. The attention level of the player is quantified using sample entropy features of EEG. The statistically significant performance improvement of five healthy subjects after playing a number of game sessions demonstrates the effectiveness of the proposed game in enhancing their concentration and memory skills.},
			author = {Thomas, Kavitha P and Vinod, a P and Guan, Cuntai},
			doi = {10.1109/EMBC.2013.6609529},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Thomas, Vinod, Guan - 2013 - Design of an online EEG based neurofeedback game for enhancing attention and memory.pdf:pdf},
			isbn = {9781457702167},
			issn = {1557-170X},
			journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference},
			month = {jan},
			number = {ii},
			pages = {433--6},
			pmid = {24109716},
			title = {{Design of an online EEG based neurofeedback game for enhancing attention and memory.}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/24109716},
			volume = {2013},
			year = {2013}
			}

			@article{Cao2017,
			author = {Cao, Miao and He, Yong and Dai, Zhengjia and Liao, Xuhong and Jeon, Tina and Ouyang, Minhui and Chalak, Lina and Bi, Yanchao and Rollins, Nancy and Dong, Qi and Huang, Hao},
			doi = {10.1093/cercor/bhw038},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/7435e4fca582c5a61742fa4057f15d0a80d7327e.pdf:pdf},
			keywords = {connectome,functional connectivity,hub,preterm,rich club},
			number = {March 2016},
			pages = {1949--1963},
			title = {{Early Development of Functional Network Segregation Revealed by Connectomic Analysis of the Preterm Human Brain}},
			url = {https://oup.silverchair-cdn.com/oup/backfile/Content{\_}public/Journal/cercor/27/3/10.1093{\_}cercor{\_}bhw038/1/bhw038.pdf?Expires=1494518187{\&}Signature=U2KsHhZaKvyorBqlBcQI0p0PNlYdV12D79SNw{~}2RpNah0P3m-0Q9P3BfCcqewq1czsUkelxvNCes6BCUaXYVwk9ICuX7N7Zzl4N3o19lajGb4d0},
			year = {2017}
			}

			@article{Tagliazucchi2011b,
			archivePrefix = {arXiv},
			arxivId = {1605.03373},
			author = {Tagliazucchi, E and Balenzuela, P and Fraiman, D and Chialvo, D},
			doi = {10.1073/pnas.0709640104},
			eprint = {1605.03373},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/83752efb6c4fea4dbce7a6edbc81ea92a1d25b4c.pdf:pdf},
			issn = {0027-8424},
			journal = {Proceedings of the National Academy of Sciences},
			month = {dec},
			number = {51},
			pages = {20167--20172},
			title = {{Point process analysis of large-scale brain fMRI dynamics.}},
			url = {http://arxiv.org/abs/1605.03373 http://www.pnas.org/cgi/doi/10.1073/pnas.0709640104},
			volume = {104},
			year = {2011}
			}

			@article{Sporns2002,
			abstract = {This paper summarizes a set of graph theory methods that are of special relevance to the computational analysis of neural connectivity patterns. Methods characterizing average measures of connectivity, similarity of connection patterns, connectedness and components, paths, walks and cycles, distances, cluster indices, ranges and shortcuts, and node and edge cut sets are introduced and discussed in a neurobiological context. A set of Matlab functions implementing these methods is available for download at http://php.indiana.edu/{\~{}}osporns/graphmeasures.htm.},
			author = {Sporns, Olaf},
			doi = {10.1007/978-1-4615-1079-6_12},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Sporns - 2002 - Graph theory methods for the analysis of neural connectivity patterns.pdf:pdf},
			isbn = {978-1-4613-5384-3, 978-1-4615-1079-6},
			journal = {Neuroscience databases: A practical guide},
			pages = {169--183},
			title = {{Graph theory methods for the analysis of neural connectivity patterns}},
			url = {http://www.indiana.edu/{~}cortex/Koetter{\_}chapter.pdf},
			year = {2002}
			}

			@article{Gao2015,
			abstract = {The brain's mature functional network architecture has been extensively studied but the early emergence of the brain's network organization remains largely unknown. In this study, leveraging a large sample (143 subjects) with longitudinal rsfMRI scans (333 datasets), we aimed to characterize the important developmental process of the brain's functional network architecture during the first 2Â years of life. Based on spatial independent component analysis and longitudinal linear mixed effect modeling, our results unveiled the detailed topology and growth trajectories of nine cortical functional networks. Within networks, our findings clearly separated the brains networks into two categories: primary networks were topologically adult-like in neonates while higher-order networks were topologically incomplete and isolated in neonates but demonstrated consistent synchronization during the first 2Â years of life (connectivity increases 0.13-0.35). Between networks, our results demonstrated both network-level connectivity decreases (-0.02 to -0.64) and increases (0.05-0.18) but decreasing connections (nÂ =Â 14) dominated increasing ones (nÂ =Â 5). Finally, significant sex differences were observed with boys demonstrating faster network-level connectivity increases among the two frontoparietal networks (growth rate was 1.63e-4 per day for girls and 2.69e-4 per day for boys, pÂ {\textless}Â 1e-4). Overall, our study delineated the development of the whole brain functional architecture during the first 2Â years of life featuring significant changes of both within- and between-network interactions.},
			author = {Gao, Wei and Alcauter, Sarael and Smith, J. Keith and Gilmore, John H. and Lin, Weili},
			doi = {10.1007/s00429-014-0710-3},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/15826ee380e477027c71b9fdd40ef423efd71397.pdf:pdf},
			isbn = {1863-2653},
			issn = {18632661},
			journal = {Brain structure {\&} function},
			keywords = {early brain development {\'{a}},effect {\'{a}} brain functional,functional network {\'{a}} sex,resting state {\'{a}}},
			number = {2},
			pages = {1173--1186},
			pmid = {24469153},
			title = {{Development of human brain cortical network architecture during infancy}},
			url = {http://download.springer.com/static/pdf/669/art{\%}253A10.1007{\%}252Fs00429-014-0710-3.pdf?originUrl=http{\%}3A{\%}2F{\%}2Flink.springer.com{\%}2Farticle{\%}2F10.1007{\%}2Fs00429-014-0710-3{\&}token2=exp=1495469224{~}acl={\%}2Fstatic{\%}2Fpdf{\%}2F669{\%}2Fart{\%}25253A10.1007{\%}25252Fs00429-014-071},
			volume = {220},
			year = {2015}
			}

			@article{Satterthwaite2012,
			author = {Satterthwaite, Theodore D and Wolf, Daniel H and Loughead, James and Ruparel, Kosha and Elliott, Mark A and Hakonarson, Hakon and Gur, Ruben C and Gur, Raquel E},
			doi = {10.1016/j.neuroimage.2011.12.063},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/18cb8a1f824f0f4ba6e21fc04f8b505f30a6a4bd.pdf:pdf},
			issn = {1053-8119},
			journal = {NeuroImage},
			keywords = {Adolescent,Connectivity,Development,Independent component analysis,Network,fALFF,fMRI,motion,preprocessing},
			mendeley-tags = {motion,preprocessing},
			number = {1},
			pages = {623--632},
			publisher = {Elsevier Inc.},
			title = {{Impact of in-scanner head motion on multiple measures of functional connectivity : Relevance for studies of neurodevelopment in youth}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2011.12.063 http://ac.els-cdn.com/S1053811911014650/1-s2.0-S1053811911014650-main.pdf?{\_}tid=edf9d138-e3ce-11e6-bb29-00000aab0f6c{\&}acdnat=1485439037{\_}633f9a9e6b0b068775f861fadf339f66},
			volume = {60},
			year = {2012}
			}

			@article{Li2016,
			author = {Li, Gang and Wang, Li and Shi, Feng and Gilmore, John H and Lin, Weili and Shen, Dinggang and Engineering, Cognitive},
			doi = {10.1016/j.media.2015.04.005.Construction},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/fe6b50f09a780b1d5ed77d5e434fa73f15c54b92.pdf:pdf},
			number = {1},
			pages = {22--36},
			title = {{Construction of 4D High-definition Cortical Surface Atlases of Infants: Methods and Applications}},
			url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4540689/pdf/nihms682350.pdf},
			volume = {25},
			year = {2016}
			}

			@article{Kwong1992,
			author = {Kwong, Kenneth K and Belliveaut, John W and Cheslert, David A and Goldbergt, Inna E and Weisskofft, Robert M and Poncelett, Brigitte P and Kennedyt, David N and Hoppelt, Bernice E and Cohent, Mark S and Turnert, Robert and Cheng, Hong-ming and Bradyt, Thomas J and Rosent, Bruce R},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/247b9ed8274b51977c125559e23d2872950fd4b5.pdf:pdf},
			journal = {Proceedings of the National Academy of Sciences},
			number = {June},
			pages = {5675--5679},
			title = {{Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation}},
			url = {http://www.pnas.org/content/89/12/5675.long},
			volume = {89},
			year = {1992}
			}

			@article{Ray1985,
			abstract = {Two experiments were designed to examine the effects of attentional demands on the electroencephalogram during cognitive and emotional tasks. Wefound an interaction of task with hemisphere as well as more overall parietal alphafor tasks not requiring attention to the environment, such as mental arithmetic, thanfor those requiring such attention. Diferential hemispheric activation for beta wasfound most strongly in the temporal areas for emotionally positive or negative tasksand in the parietal areas for cognitive tasks.},
			author = {Ray, William J and Cole, Harry W},
			journal = {Science},
			pages = {750--752},
			title = {{EEG Alpha Activity Reflects Attentional Demands, and Beta Activity Reflects Emotional and Cognitive Processes}},
			volume = {228},
			year = {1985}
			}

			@article{VanSomeren2011,
			abstract = {The most important quest of cognitive neuroscience may be to unravel the mechanisms by which the brain selects, links, consolidates, and integrates new information into its neuronal network, while preventing saturation to occur. During the past decade, neuroscientists working within several disciplines have observed an important involvement of the specific types of brain oscillations that occur during sleep-the cortical slow oscillations; during the resting state - the fMRI resting state networks including the default-mode network (DMN); and during task performance - the performance modulations that link as well to modulations in electroencephalography or magnetoencephalography frequency content. Understanding the role of these slow oscillations thus appears to be essential for our fundamental understanding of brain function. Brain activity is characterized by oscillations occurring in spike frequency, field potentials or blood oxygen level-dependent functional magnetic resonance imaging signals. Environmental stimuli, reaching the brain through our senses, activate or inactivate neuronal populations and modulate ongoing activity. The effect they sort is to a large extent determined by the momentary state of the slow endogenous oscillations of the brain. In the absence of sensory input, as is the case during rest or sleep, brain activity does not cease. Rather, its oscillations continue and change with respect to their dominant frequencies and coupling topography. This chapter briefly introduces the topics that will be addressed in this dedicated volume of Progress in Brain Research on slow oscillations and sets the stage for excellent papers discussing their molecular, cellular, network physiological and cognitive performance aspects. Getting to know about slow oscillations is essential for our understanding of plasticity, memory, brain structure from synapse to DMN, cognition, consciousness, and ultimately for our understanding of the mechanisms and functions of sleep and vigilance.},
			author = {{Van Someren}, E J W and {Van Der Werf}, Y D and Roelfsema, P R and Mansvelder, H D and {Lopes da Silva}, F H},
			doi = {10.1016/B978-0-444-53839-0.00001-6},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Van Someren et al. - 2011 - Slow brain oscillations of sleep, resting state, and vigilance.pdf:pdf},
			institution = {Netherlands Institute for Neuroscience, Amsterdam, The Netherlands; Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, VU University, Amsterdam, The Netherlands.},
			isbn = {9780444538390},
			issn = {1875-7855},
			journal = {Progress in brain research},
			keywords = {Brain,Brain: physiology,Cerebral Cortex,Cerebral Cortex: anatomy {\&} histology,Cerebral Cortex: physiology,Humans,Neurons,Neurons: physiology,Sleep,Sleep Stages,Sleep Stages: physiology,Sleep: physiology},
			month = {jan},
			pages = {3--15},
			pmid = {21854952},
			title = {{Slow brain oscillations of sleep, resting state, and vigilance.}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/21854952},
			volume = {193},
			year = {2011}
			}

			@article{Royet2004,
			abstract = {Over the last ten years, methods of cerebral imaging have revolutionized our knowledge of cognitive processes in humans. An impressive number of papers dealing with cerebral imaging for olfaction have been published to date. Whereas the early works revealed those structures participating in the processing of odours presented passively to subjects, researchers later recorded brain activity when subjects performed specific olfactory tasks based on memory, emotion and identification. From these results, we suggest that there is a dissociation of olfactory processes, with involvement of the right hemisphere in memory processes and the left hemisphere in emotional processes. The review concludes with a summary of how these lateralized processes are consistent with the gestalt-nature of our olfactory perception.},
			author = {Royet, Jean Pierre and Plailly, Jane},
			doi = {10.1093/chemse/bjh067},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/80021f75d6c69fd89fecbe71d36a62d03c661ae5.pdf:pdf},
			isbn = {0379-864X},
			issn = {0379864X},
			journal = {Chemical Senses},
			keywords = {Emotion,Familiarity,Hedonicity,Judgement task,Lateralization,Olfactory processes,PET,fMRI},
			number = {8},
			pages = {731--745},
			pmid = {15466819},
			title = {{Lateralization of olfactory processes}},
			url = {https://oup.silverchair-cdn.com/oup/backfile/Content{\_}public/Journal/chemse/29/8/10.1093/chemse/bjh067/2/bjh067.pdf?Expires=1496231442{\&}Signature=ZGSl63xq1O7PkFr7qaWX9eslrQ{~}F1cOQND4arCoPINMNpc5GldyT7AIe9K45AhCeZjtMvbUoOSDhpO1hsu4sMkknhNJSsSkh83JwleHhDsnAktF},
			volume = {29},
			year = {2004}
			}

			@article{Yang2014,
			abstract = {Perspectives of human brain functional connectivity continue to evolve. Static representations of functional interactions between brain regions are rapidly giving way to dynamic perspectives, which emphasize non-random temporal variations in intrinsic functional connectivity (iFC) patterns. Here, we bring this dynamic perspective to our understanding of iFC patterns for posteromedial cortex (PMC), a cortical hub known for its functional diversity. Previous work has consistently differentiated iFC patterns among PMC subregions, though assumed static iFC over time. Here, we assessed iFC as a function of time utilizing a sliding-window correlation approach, and applied hierarchical clustering to detect representative iFC states from the windowed iFC. Across subregions, five iFC states were detected over time. Although with differing frequencies, each subregion was associated with each of the states, suggesting that these iFC states are "common" to PMC subregions. Importantly, each subregion possessed a unique preferred state(s) and distinct transition patterns, explaining previously observed iFC differentiations. These results resonate with task-based fMRI studies suggesting that large-scale functional networks can be flexibly reconfigured in response to changing task-demands. Additionally, we used retest scans ({\~{}}. 1. week later) to demonstrate the reproducibility of the iFC states identified, and establish moderate to high test-retest reliability for various metrics used to quantify switching behaviors. We also demonstrate the ability of dynamic properties in the visual PMC subregion to index inter-individual differences in a measure of concept formation and mental flexibility. These findings suggest functional relevance of dynamic iFC and its potential utility in biomarker identification over time, as d-iFC methodologies are refined and mature. ?? 2014 Elsevier Inc.},
			archivePrefix = {arXiv},
			arxivId = {NIHMS150003},
			author = {Yang, Zhen and Craddock, R. Cameron and Margulies, Daniel S. and Yan, Chao Gan and Milham, Michael P.},
			doi = {10.1016/j.neuroimage.2014.02.014},
			eprint = {NIHMS150003},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Yang et al. - 2014 - Common intrinsic connectivity states among posteromedial cortex subdivisions Insights from analysis of temporal dyn.pdf:pdf},
			isbn = {1053-8119},
			issn = {10959572},
			journal = {NeuroImage},
			keywords = {Connectivity states,Intrinsic functional connectivity,Resting-state fMRI,Temporal dynamics,The posteromedial cortex},
			number = {P1},
			pages = {124--137},
			pmid = {24560717},
			publisher = {Elsevier Inc.},
			title = {{Common intrinsic connectivity states among posteromedial cortex subdivisions: Insights from analysis of temporal dynamics}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2014.02.014},
			volume = {93},
			year = {2014}
			}

			@article{Friston1993,
			author = {Friston, Karl J and Frith, CD and Liddle, PF and Frackowiack, RSJ},
			file = {:Users/lorenafreitas/Downloads/Friston{\_}Functional connectivity.pdf:pdf},
			journal = {Journal of cerebral blood flow and metabolism},
			pages = {5.14},
			title = {{Functional Connectivity: The Principal-Component Analysis of Large (PET) Data Sets}},
			volume = {13},
			year = {1993}
			}

			@article{Gargano2015,
			author = {Gargano, Giancarlo and Nuccini, Francesca},
			doi = {10.1186/1824-7288-41-S1-A14},
			file = {:Users/lorenafreitas/Downloads/1824-7288-41-S1-A14.pdf:pdf},
			issn = {1824-7288},
			journal = {Italian Journal of Pediatrics},
			number = {Suppl 1},
			pages = {A14},
			publisher = {BioMed Central Ltd},
			title = {{Maternal voice and preterm infants development}},
			url = {http://www.ijponline.net/content/41/S1/A14},
			volume = {41},
			year = {2015}
			}

			@article{Anderson2001,
			abstract = {Abstract Hypothesis-testing methods for multivariate data are needed to make rigorous probability statements about the effects of factors and their interactions in experiments. Analysis of variance is particularly powerful for the analysis of univariate data. The traditional multivariate analogues, however, are too stringent in their assumptions for most ecological multivariate data sets. Non-parametric methods, based on permutation tests, are preferable. This paper describes a new non-parametric method for multivariate analysis of variance, after McArdle and Anderson (in press). It is given here, with several applications in ecology, to provide an alternative and perhaps more intuitive formulation for ANOVA (based on sums of squared distances) to complement the description provided by McArdle and Anderson (in press) for the analysis of any linear model. It is an improvement on previous non-parametric methods because it allows a direct additive partitioning of variation for complex models. It does this while maintaining the flexibility and lack of formal assumptions of other non-parametric methods. The test-statistic is a multivariate analogue to Fisher's F-ratio and is calculated directly from any symmetric distance or dissimilarity matrix. P-values are then obtained using permutations. Some examples of the method are given for tests involving several factors, including factorial and hierarchical (nested) designs and tests of interactions. [ABSTRACT FROM AUTHOR]},
			author = {Anderson, Marti J.},
			doi = {10.1046/j.1442-9993.2001.01070.x},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/038e8869b676aa365f2afdea935edf3f2003324d.pdf:pdf},
			isbn = {1442-9993},
			issn = {14429985},
			journal = {Austral Ecology},
			keywords = {ANOVA,Distance measure,Experimental design,Linear model,Multifactorial,Multivariate dissimilarity,Partitioning,Permutation tests,Statistics,permanova},
			mendeley-tags = {permanova},
			number = {1},
			pages = {32--46},
			pmid = {18385208},
			title = {{A new method for non-parametric multivariate analysis of variance}},
			url = {https://pdfs.semanticscholar.org/038e/8869b676aa365f2afdea935edf3f2003324d.pdf},
			volume = {26},
			year = {2001}
			}

			@article{Searle2009,
			abstract = {The concept of 'personalized medicine' in which a knowledge of genetic factors guides prescribing tailored to the individual is popularly considered to be an inevitable consequence of completion of the International Human Genome Project. We should not forget, however, that a personal or family history of one of several uncommon pharmacogenetic conditions has influenced the use of the implicated drug(s) during anaesthesia for the past 50 yr. Although this has been important for those affected, pharmacogenomics heralds the prospect of an individual's genetic profile informing every prescription. Progress has been rapid in some areas, notably cancer chemotherapy where response to treatment can be predicted on the basis of the genetic profile of the tumour cells. The situation is different for most currently available drugs, including those used by anaesthetists, where genetic variability to drug response is presumed to be the result of a complex interaction of multiple factors. We review the nature and investigation of pharmacogenomic variability and contrast the progress made with research into opioid variability with the more limited literature concerning i.v. and inhalation anaesthetics.},
			author = {Searle, R. and Hopkins, P. M.},
			doi = {10.1093/bja/aep130},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Searle, Hopkins - 2009 - Pharmacogenomic variability and anaesthesia.pdf:pdf},
			isbn = {1471-6771 (Electronic)$\backslash$r0007-0912 (Linking)},
			issn = {00070912},
			journal = {British Journal of Anaesthesia},
			keywords = {Anaesthetics gases, nitrous oxide,Anaesthetics i.v.,Anaesthetics volatile,Genetic factors,Malignant hyperthermia,Pharmacodynamics,Pharmacology agonists},
			number = {1},
			pages = {14--25},
			pmid = {19482856},
			title = {{Pharmacogenomic variability and anaesthesia}},
			url = {http://bja.oxfordjournals.org/content/103/1/14.full.pdf},
			volume = {103},
			year = {2009}
			}

			@article{Dogan2015,
			abstract = {Finite rate of innovation (FRI) is a recent framework for sampling and reconstruction of a large class of parametric signals that are characterized by finite number of innovations (parameters) per unit interval. In the absence of noise, exact recovery of FRI signals has been demonstrated. In the noisy scenario, there exist techniques to deal with non-ideal measurements. Yet, the accuracy and resiliency to noise and model mismatch are still challenging problems for real-world applications. We address the reconstruction of FRI signals, specifically a stream of Diracs, from few signal samples degraded by noise and we propose a new FRI reconstruction method that is based on a modelâfitting approach related to the structuredâTLS problem. The modelâfitting method is based on minimizing the training error, that is, the error between the computed and the recovered moments (i.e., the FRI-samples of the signal), subject to an annihilation system. We present our framework for three different constraints of the annihilation system. Moreover, we propose a model order selection framework to determine the innovation rate of the signal; i.e., the number of Diracs by estimating the noise level through the training error curve. We compare the performance of the modelâfitting approach with known FRI reconstruction algorithms and CramÂ´erâRao's lower bound (CRLB) to validate these contributions.},
			author = {Dogan, Zafer and Gilliam, Christopher and Blu, Thierry and {Van De Ville}, Dimitri},
			doi = {10.1109/TSP.2015.2461513},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Dogan et al. - 2015 - Reconstruction of Finite Rate of Innovation Signals with ModelâFitting Approach.pdf:pdf},
			issn = {1053-587X},
			journal = {IEEE Transactions on Signal Processing},
			keywords = {Annihilating Filter,Biological system modeling,Cadzow,Computational modeling,CramerâRaos lower bound (CRLB),Estimation,Finiteârateâofâinnovation,Kernel,Kumaresan-Tufts,Matrix Pencil,Noise,Reconstruction algorithms,Technological innovation,iterative quadratic maximum likelihood (IQML),model fitting,noise,reconstruction,sampling,structured total least squares (STLS),total least squares (TLS)},
			number = {99},
			pages = {1--1},
			title = {{Reconstruction of Finite Rate of Innovation Signals with ModelâFitting Approach}},
			url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7169606},
			volume = {PP},
			year = {2015}
			}

			@article{West2013,
			abstract = {BACKGROUND: During closed-loop control, a drug infusion is continually adjusted according to a measure of clinical effect (e.g., an electroencephalographic depth of hypnosis (DoH) index). Inconsistency in population-derived pediatric pharmacokinetic/pharmacodynamic models and the large interpatient variability observed in children suggest a role for closed-loop control in optimizing the administration of intravenous anesthesia. OBJECTIVE: To clinically evaluate a robustly tuned system for closed-loop control of the induction and maintenance of propofol anesthesia in children undergoing gastrointestinal endoscopy. METHODS: One hundred and eight children, aged 6-17, ASA I-II, were enrolled. Prior to induction of anesthesia, NeuroSENSEâ¢ sensors were applied to obtain the WAVCNS DoH index. An intravenous cannula was inserted and lidocaine (0.5 mg{\textperiodcentered}kg(-1) ) administered. Remifentanil was administered as a bolus (0.5 $\mu$g{\textperiodcentered}kg(-1) ), followed by continuous infusion (0.03 $\mu$g{\textperiodcentered}kg(-1) {\textperiodcentered}min(-1) ). The propofol infusion was closed-loop controlled throughout induction and maintenance of anesthesia, using WAVCNS as feedback. RESULTS: Anesthesia was closed-loop controlled in 102 cases. The system achieved and maintained an adequate DoH without manual adjustment in 87/102 (85{\%}) cases. Induction of anesthesia (to WAVCNS â¤ 60) was completed in median 3.8 min (interquartile range (IQR) 3.1-5.0), culminating in a propofol effect-site concentration (Ce ) of median 3.5 $\mu$g{\textperiodcentered}ml(-1) (IQR 2.7-4.5). During maintenance of anesthesia, WAVCNS was measured within 10 units of the target for median 89{\%} (IQR 79-96) of the time. Spontaneous breathing required no manual intervention in 91/102 (89{\%}) cases. CONCLUSIONS: A robust closed-loop system can provide effective propofol administration during induction and maintenance of anesthesia in children. Wide variation in the calculated Ce highlights the limitation of open-loop regimes based on pharmacokinetic/pharmacodynamic models.},
			author = {West, Nicholas and Dumont, Guy a. and {Van Heusden}, Klaske and Petersen, Christian L. and Khosravi, Sara and Soltesz, Kristian and Umedaly, Aryannah and Reimer, Eleanor and Ansermino, J. Mark},
			doi = {10.1111/pan.12183},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/West et al. - 2013 - Robust closed-loop control of induction and maintenance of propofol anesthesia in children(2).pdf:pdf},
			issn = {11555645},
			journal = {Paediatric Anaesthesia},
			keywords = {anesthetics,automation,drug delivery systems/methods,electroencephalography/drug effects,intravenous/administration {\&} dosage,propofol/administration {\&} dosage,software},
			number = {8},
			pages = {712--719},
			pmid = {23668370},
			title = {{Robust closed-loop control of induction and maintenance of propofol anesthesia in children}},
			volume = {23},
			year = {2013}
			}

			@article{Chang2010,
			abstract = {Most studies of resting-state functional connectivity using fMRI employ methods that assume temporal stationarity, such as correlation and data-driven decompositions computed across the duration of the scan. However, evidence from both task-based fMRI studies and animal electrophysiology suggests that functional connectivity may exhibit dynamic changes within time scales of seconds to minutes. In the present study, we investigated the dynamic behavior of resting-state connectivity across the course of a single scan, performing a time-frequency coherence analysis based on the wavelet transform. We focused on the connectivity of the posterior cingulate cortex (PCC), a primary node of the default-mode network, examining its relationship with both the "anticorrelated" ("task-positive") network as well as other nodes of the default-mode network. It was observed that coherence and phase between the PCC and the anticorrelated network was variable in time and frequency, and statistical testing based on Monte Carlo simulations revealed the presence of significant scale-dependent temporal variability. In addition, a sliding-window correlation procedure identified other regions across the brain that exhibited variable connectivity with the PCC across the scan, which included areas previously implicated in attention and salience processing. Although it is unclear whether the observed coherence and phase variability can be attributed to residual noise or modulation of cognitive state, the present results illustrate that resting-state functional connectivity is not static, and it may therefore prove valuable to consider measures of variability, in addition to average quantities, when characterizing resting-state networks. ?? 2009 Elsevier Inc. All rights reserved.},
			author = {Chang, Catie and Glover, Gary H.},
			doi = {10.1016/j.neuroimage.2009.12.011},
			file = {:Users/lorenafreitas/Library/Containers/com.apple.mail/Data/Library/Mail Downloads/8A1C91A4-1022-45A0-98F4-02A8FB5844D9/ChangGlover2010.pdf:pdf},
			isbn = {1095-9572 (Electronic)$\backslash$r1053-8119 (Linking)},
			issn = {10538119},
			journal = {NeuroImage},
			keywords = {Default mode network,Dynamics,Functional connectivity,Functional magnetic resonance imaging,Negative correlations,Networks,Nonstationarity,Resting state,Spontaneous activity,Wavelets},
			number = {1},
			pages = {81--98},
			pmid = {20006716},
			publisher = {Elsevier Inc.},
			title = {{Time-frequency dynamics of resting-state brain connectivity measured with fMRI}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2009.12.011},
			volume = {50},
			year = {2010}
			}

			@article{Arichi2010,
			author = {Arichi, T and Moraux, A and Melendez, A and Doria, V and Groppo, M and Merchant, N and Combs, S},
			doi = {10.1016/j.neuroimage.2009.10.038},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/a9b8c0d4e0c48cb066c02da46f29344fc5c6b158.pdf:pdf},
			issn = {1053-8119},
			journal = {NeuroImage},
			number = {3},
			pages = {2063--2071},
			publisher = {Elsevier B.V.},
			title = {{Somatosensory cortical activation identi fi ed by functional MRI in preterm and term infants}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2009.10.038 http://ac.els-cdn.com/S1053811909011094/1-s2.0-S1053811909011094-main.pdf?{\_}tid=449a2674-e3d3-11e6-8b2e-00000aab0f6b{\&}acdnat=1485440901{\_}5fdd5809eb9e82a153f9e5d156aa5766},
			volume = {49},
			year = {2010}
			}

			@article{Yeh2011,
			Author = {Yeh, Fang-Cheng and Wedeen, Van Jay and Tseng, Wen-Yih Isaac},
			Date-Added = {2017-07-05 00:37:50 +0000},
			Date-Modified = {2017-07-05 00:38:04 +0000},
			Journal = {Neuroimage},
			Month = {Apr.},
			Number = {3},
			Pages = {1054--1062},
			Title = {Estimation of fiber orientation and spin density distribution by diffusion deconvolution},
			Volume = {55},
			Year = {2011}}


			@article{VanEssen2013,
			abstract = {The Human Connectome Project consortium led by Washington University, University of Minnesota, and Oxford University is undertaking a systematic effort to map macroscopic human brain circuits and their relationship to behavior in a large population of healthy adults. This overview article focuses on progress made during the first half of the 5-year project in refining the methods for data acquisition and analysis. Preliminary analyses based on a finalized set of acquisition and preprocessing protocols demonstrate the exceptionally high quality of the data from each modality. The first quarterly release of imaging and behavioral data via the ConnectomeDB database demonstrates the commitment to making HCP datasets freely accessible. Altogether, the progress to date provides grounds for optimism that the HCP datasets and associated methods and software will become increasingly valuable resources for characterizing human brain connectivity and function, their relationship to behavior, and their heritability and genetic underpinnings. {\textcopyright} 2013 Elsevier Inc.},
			archivePrefix = {arXiv},
			arxivId = {NIHMS150003},
			author = {{Van Essen}, David C. and Smith, Stephen M. and Barch, Deanna M. and Behrens, Timothy E.J. and Yacoub, Essa and Ugurbil, Kamil},
			doi = {10.1016/j.neuroimage.2013.05.041},
			eprint = {NIHMS150003},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/735ba65d7cb72751e8a1bc75615022896248185c.pdf:pdf},
			isbn = {1053-8119},
			issn = {10538119},
			journal = {NeuroImage},
			pages = {62--79},
			pmid = {23684880},
			publisher = {Elsevier Inc.},
			title = {{The WU-Minn Human Connectome Project: An overview}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2013.05.041 https://ac.els-cdn.com/S1053811913005351/1-s2.0-S1053811913005351-main.pdf?{\_}tid=dd390d26-22c1-4bf3-8679-dd6749128504{\&}acdnat=1543411897{\_}b64e72580a4e4b27528bb4c99a53f9b8},
			volume = {80},
			year = {2013}
			}

			@article{Fort2012,
			author = {Fort, Alexandra and Maury, Bertrand and Lemercier, C{\'{e}}line},
			doi = {10.1136/bmj.e8105},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Fort, Maury, Lemercier - 2012 - Mind wandering and driving responsibility case-control.pdf:pdf},
			number = {December},
			pages = {1--7},
			title = {{Mind wandering and driving : responsibility case-control}},
			volume = {8105},
			year = {2012}
			}

			@article{Chen2016,
			abstract = {One of the most fundamental features of the human brain is its ability to detect and attend to salient goal-relevant events in a flexible manner. The salience network (SN), anchored in the anterior insula and the dorsal anterior cingulate cortex, plays a crucial role in this pro-cess through rapid detection of goal-relevant events and facilitation of access to appropriate cognitive resources. Here, we leverage the subsecond resolution of large multisession fMRI datasets from the Human Connectome Project and apply novel graph-theoretical tech-niques to investigate the dynamic spatiotemporal organization of the SN. We show that the large-scale brain dynamics of the SN are characterized by several distinctive and robust properties. First, the SN demonstrated the highest levels of flexibility in time-varying con-nectivity with other brain networks, including the frontoparietal network (FPN), the cingu-lateâopercular network (CON), and the ventral and dorsal attention networks (VAN and DAN). Second, dynamic functional interactions of the SN were among the most spatially varied in the brain. Third, SN nodes maintained a consistently high level of network central-ity over time, indicating that this network is a hub for facilitating flexible cross-network inter-actions. Fourth, time-varying connectivity profiles of the SN were distinct from all other prefrontal control systems. Fifth, temporal flexibility of the SN uniquely predicted individual differences in cognitive flexibility. Importantly, each of these results was also observed in a second retest dataset, demonstrating the robustness of our findings. Our study provides fundamental new insights into the distinct dynamic functional architecture of the SN and demonstrates how this network is uniquely positioned to facilitate interactions with multiple functional systems and thereby support a wide range of cognitive processes in the human brain.},
			author = {Chen, Tianwen and Cai, Weidong and Ryali, Srikanth and Supekar, Kaustubh and Menon, Vinod},
			doi = {10.1371/journal.pbio.1002469},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/64f291305622b1ca337064166bac84b351502371.pdf:pdf},
			isbn = {1545-7885},
			issn = {15457885},
			journal = {PLoS Biology},
			number = {6},
			pages = {1--21},
			pmid = {27270215},
			title = {{Distinct Global Brain Dynamics and Spatiotemporal Organization of the Salience Network}},
			url = {https://journals.plos.org/plosbiology/article/file?id=10.1371/journal.pbio.1002469{\&}type=printable},
			volume = {14},
			year = {2016}
			}

			@article{Westover2015,
			abstract = {Medical coma is an anesthetic-induced state of brain inactivation, manifest in the electroencephalogram by burst suppression. Feedback control can be used to regulate burst suppression, however, previous designs have not been robust. Robust control design is critical under real-world operating conditions, subject to substantial pharmacokinetic and pharmacodynamic parameter uncertainty and unpredictable external disturbances. We sought to develop a robust closed-loop anesthesia delivery (CLAD) system to control medical coma. We developed a robust CLAD system to control the burst suppression probability (BSP). We developed a novel BSP tracking algorithm based on realistic models of propofol pharmacokinetics and pharmacodynamics. We also developed a practical method for estimating patient-specific pharmacodynamics parameters. Finally, we synthesized a robust proportional integral controller. Using a factorial design spanning patient age, mass, height, and gender, we tested whether the system performed within clinically acceptable limits. Throughout all experiments we subjected the system to disturbances, simulating treatment of refractory status epilepticus in a real-world intensive care unit environment. In 5400 simulations, CLAD behavior remained within specifications. Transient behavior after a step in target BSP from 0.2 to 0.8 exhibited a rise time (the median (min, max)) of 1.4 [1.1, 1.9] min; settling time, 7.8 [4.2, 9.0] min; and percent overshoot of 9.6 [2.3, 10.8]{\%}. Under steady state conditions the CLAD system exhibited a median error of 0.1 [-0.5, 0.9]{\%}; inaccuracy of 1.8 [0.9, 3.4]{\%}; oscillation index of 1.8 [0.9, 3.4]{\%}; and maximum instantaneous propofol dose of 4.3 [2.1, 10.5] mg kg(-1). The maximum hourly propofol dose was 4.3 [2.1, 10.3] mg kg(-1) h(-1). Performance fell within clinically acceptable limits for all measures. A CLAD system designed using robust control theory achieves clinically acceptable performance in the presence of realistic unmodeled disturbances and in spite of realistic model uncertainty, while maintaining infusion rates within acceptable safety limits.},
			author = {Westover, M Brandon and Kim, Seong-eun and Ching, Shinung and Purdon, Patrick L and Brown, Emery N},
			doi = {10.1088/1741-2560/12/4/046004},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Westover et al. - 2015 - Robust control of burst suppression for medical coma.pdf:pdf},
			issn = {1741-2560},
			journal = {Journal of Neural Engineering},
			keywords = {anesthesia,burst suppression,control,electroencephalogram,medical coma},
			number = {4},
			pages = {046004},
			pmid = {26020243},
			title = {{Robust control of burst suppression for medical coma}},
			url = {http://dx.doi.org/10.1088/1741-2560/12/4/046004{\%}5Cnhttp://stacks.iop.org/1741-2552/12/i=4/a=046004?key=crossref.61747e700f71691b8a379bc79ca1bdf6},
			volume = {12},
			year = {2015}
			}

			@article{Power2010,
			author = {Power, Jonathan D and Fair, Damien A and Schlaggar, Bradley L and Petersen, Steven E},
			doi = {10.1016/j.neuron.2010.08.017},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/bd5b70e5d733bd5cd4aab4b1c1e88e38fa1a1895.pdf:pdf},
			issn = {0896-6273},
			journal = {Neuron},
			number = {5},
			pages = {735--748},
			publisher = {Elsevier Inc.},
			title = {{The Development of Human Functional Brain Networks}},
			url = {http://dx.doi.org/10.1016/j.neuron.2010.08.017 http://ac.els-cdn.com/S0896627310006276/1-s2.0-S0896627310006276-main.pdf?{\_}tid=530b28c0-358b-11e7-8056-00000aacb35d{\&}acdnat=1494425996{\_}7e5fc23be3ab984395a36c2a6fa42bfb},
			volume = {67},
			year = {2010}
			}

			@article{Douw2016,
			abstract = {The brain is a dynamic, flexible network that continuously reconfigures. However, the neural underpinnings of how state-dependent variability of dynamic functional connectivity (vdFC) relates to cognitive flexibility are unclear. We therefore investigated flexible functional connectivity during resting-state and task-state functional magnetic resonance imaging (rs-fMRI and t-fMRI, resp.) and performed separate, out-of-scanner neuropsychological testing. We hypothesize that state-dependent vdFC between the frontoparietal network (FPN) and the default mode network (DMN) relates to cognitive flexibility. Seventeen healthy subjects performed the Stroop color word test and underwent t-fMRI (Stroop computerized version) and rs-fMRI. Time series were extracted from a cortical atlas, and a sliding window approach was used to obtain a number of correlation matrices per subject. vdFC was defined as the standard deviation of connectivity strengths over these windows. Higher task-state FPN???DMN vdFC was associated with greater out-of-scanner cognitive flexibility, while the opposite relationship was present for resting-state FPN???DMN vdFC. Moreover, greater contrast between task-state and resting-state vdFC related to better cognitive performance. In conclusion, our results suggest that not only the dynamics of connectivity between these networks is seminal for optimal functioning, but also that the contrast between dynamics across states reflects cognitive performance.},
			author = {Douw, Linda and Wakeman, Daniel G. and Tanaka, Naoaki and Liu, Hesheng and Stufflebeam, Steven M.},
			doi = {10.1016/j.neuroscience.2016.09.034},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/bf8e57e17695bd3fd69bbf853b50ea5e78303ab6.pdf:pdf},
			isbn = {0306-4522},
			issn = {18737544},
			journal = {Neuroscience},
			keywords = {brain dynamics,cognition,resting-state fMRI},
			pages = {12--21},
			pmid = {27687802},
			publisher = {The Authors},
			title = {{State-dependent variability of dynamic functional connectivity between frontoparietal and default networks relates to cognitive flexibility}},
			url = {http://dx.doi.org/10.1016/j.neuroscience.2016.09.034 http://ac.els-cdn.com/S030645221630481X/1-s2.0-S030645221630481X-main.pdf?{\_}tid=5a27fb88-47a6-11e7-a8c6-00000aab0f02{\&}acdnat=1496416726{\_}cae19e08132eac4d77cd84844cef5832},
			volume = {339},
			year = {2016}
			}

			@article{Rodic2015,
			author = {Rodic, Stefan and Zhao, Pei Jun},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/11ff3012f70a875024c4ce55ef2f8c65b399b870.pdf:pdf},
			journal = {University of Western Ontario Medical Journal},
			number = {Meds 2018},
			pages = {13--15},
			title = {{A brief review of neuroimaging using functional magnetic resonance imaging ( fMRI )}},
			url = {http://www.uwomj.com/wp-content/uploads/2015/09/v84no1{\_}04.pdf},
			year = {2015}
			}

			@article{Li2014c,
			abstract = {In this study, a multiple kernel learning support vector machine algorithm is proposed for the identification of EEG signals including mental and cognitive tasks, which is a key component in EEG-based brain computer interface (BCI) systems. The presented BCI approach included three stages: (1) a pre-processing step was performed to improve the general signal quality of the EEG; (2) the features were chosen, including wavelet packet entropy and Granger causality, respectively; (3) a multiple kernel learning support vector machine (MKL-SVM) based on a gradient descent optimization algorithm was investigated to classify EEG signals, in which the kernel was defined as a linear combination of polynomial kernels and radial basis function kernels. Experimental results showed that the proposed method provided better classification performance compared with the SVM based on a single kernel. For mental tasks, the average accuracies for 2-class, 3-class, 4-class, and 5-class classifications were 99.20{\%}, 81.25{\%}, 76.76{\%}, and 75.25{\%} respectively. Comparing stroke patients with healthy controls using the proposed algorithm, we achieved the average classification accuracies of 89.24{\%} and 80.33{\%} for 0-back and 1-back tasks respectively. Our results indicate that the proposed approach is promising for implementing human-computer interaction (HCI), especially for mental task classification and identifying suitable brain impairment candidates.},
			author = {Li, Xiaoou and Chen, Xun and Yan, Yuning and Wei, Wenshi and Wang, Z Jane},
			doi = {10.3390/s140712784},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Li et al. - 2014 - Classification of EEG signals using a multiple kernel learning support vector machine.pdf:pdf},
			issn = {1424-8220},
			journal = {Sensors (Basel, Switzerland)},
			keywords = {brain computer interface,mental task,multiple kernel learning,polynomial kernel,radial basis function kernel,stroke patients},
			month = {jan},
			number = {7},
			pages = {12784--802},
			pmid = {25036334},
			title = {{Classification of EEG signals using a multiple kernel learning support vector machine.}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/25036334},
			volume = {14},
			year = {2014}
			}

			@article{Di2015,
			author = {Di, Xin and Fu, Zening and Chan, Shing Chow and Hung, Yeung Sam and Biswal, Bharat B},
			doi = {10.3389/fnhum.2015.00543},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/907bbc3d5d94b3edbef2072d7889e330dc732fc4.pdf:pdf},
			keywords = {dynamic connectivity,functional connectivity,time-varying correlation coefficient,vis,visual system},
			number = {September},
			pages = {1--11},
			title = {{Task-related functional connectivity dynamics in a block-designed visual experiment}},
			url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4588125/pdf/fnhum-09-00543.pdf},
			volume = {9},
			year = {2015}
			}

			@article{Daly2014,
			abstract = {A fully automated and online artifact removal method for the electroencephalogram (EEG) is developed for use in brain-computer interfacing. The method (FORCe) is based upon a novel combination of wavelet decomposition, independent component analysis, and thresholding. FORCe is able to operate on a small channel set during online EEG acquisition and does not require additional signals (e.g. electrooculogram signals). Evaluation of FORCe is performed offline on EEG recorded from 13 BCI particpants with cerebral palsy (CP) and online with three healthy participants. The method outperforms the state-of the-art automated artifact removal methods Lagged auto-mutual information clustering (LAMIC) and Fully automated statistical thresholding (FASTER), and is able to remove a wide range of artifact types including blink, electromyogram (EMG), and electrooculogram (EOG) artifacts.},
			author = {Daly, Ian and Scherer, Reinhold and Billinger, Martin and Muller-Putz, Gernot},
			doi = {10.1109/TNSRE.2014.2346621},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Daly et al. - 2014 - FORCe Fully Online and automated artifact Removal for brain-Computer interfacing.pdf:pdf},
			issn = {1558-0210},
			journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
			month = {aug},
			number = {c},
			pages = {1--13},
			pmid = {25134085},
			title = {{FORCe: Fully Online and automated artifact Removal for brain-Computer interfacing.}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/25134085},
			volume = {4320},
			year = {2014}
			}

			@article{Chen2011,
			address = {Totowa, NJ},
			author = {Chen, Yonghong and Dhamala, Mukesh and Bollimunta, Anil and Schroeder, Charles E and Ding, Mingzhou},
			doi = {10.1007/978-1-60327-202-5},
			editor = {Vertes, Robert P. and Stackman, Robert W.},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Chen et al. - 2011 - Electrophysiological Recording Techniques.pdf:pdf},
			isbn = {978-1-60327-201-8},
			keywords = {CSD,Current Source Densities,alpha oscillations,cortical,current source density analysis,local field potential},
			mendeley-tags = {CSD,Current Source Densities},
			pages = {27--41},
			publisher = {Humana Press},
			series = {Neuromethods},
			title = {{Electrophysiological Recording Techniques}},
			url = {http://link.springer.com/10.1007/978-1-60327-202-5},
			volume = {54},
			year = {2011}
			}

			@article{Friston2011,
			author = {Friston, Karl J},
			doi = {10.1089/brain.2011.0008},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/56052b0740f2b6c39c16c22087d7397f1b31a281.pdf:pdf},
			keywords = {brain connectivity,causal modeling,effective connectivity,functional connectivity},
			number = {1},
			title = {{Functional and Effective Connectivity : A Review}},
			url = {http://www.fil.ion.ucl.ac.uk/{~}karl/Functional and Effective Connectivity A Review.pdf},
			volume = {1},
			year = {2011}
			}

			@article{AndrewsHanna2014,
			title={The default network and self-generated thought: component processes, dynamic control, and clinical relevance},
			author={Andrews-Hanna, Jessica R and Smallwood, Jonathan and Spreng, R Nathan},
			journal={Annals of the New York Academy of Sciences},
			volume={1316},
			number={1},
			pages={29},
			year={2014},
			publisher={NIH Public Access}
			}

			@article{Lindquist2014,
			abstract = {To date, most functional Magnetic Resonance Imaging (fMRI) studies have assumed that the functional connectivity (FC) between time series from distinct brain regions is constant across time. However, recently, there has been an increased interest in quantifying possible dynamic changes in FC during fMRI experiments, as it is thought that this may provide insight into the fundamental workings of brain networks. In this work we focus on the specific problem of estimating the dynamic behavior of pair-wise correlations between time courses extracted from two different regions of the brain. We critique the commonly used sliding-window technique, and discuss some alternative methods used to model volatility in the finance literature that could also prove to be useful in the neuroimaging setting. In particular, we focus on the Dynamic Conditional Correlation (DCC) model, which provides a model-based approach towards estimating dynamic correlations. We investigate the properties of several techniques in a series of simulation studies and find that DCC achieves the best overall balance between sensitivity and specificity in detecting dynamic changes in correlations. We also investigate its scalability beyond the bivariate case to demonstrate its utility for studying dynamic correlations between more than two brain regions. Finally, we illustrate its performance in an application to test-retest resting state fMRI data.},
			archivePrefix = {arXiv},
			arxivId = {NIHMS150003},
			author = {Lindquist, Martin A. and Xu, Yuting and Nebel, Mary Beth and Caffo, Brain S.},
			doi = {10.1016/j.neuroimage.2014.06.052},
			eprint = {NIHMS150003},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/684f27093d91cd5c95acdc07a500b37b6843ea66.pdf:pdf},
			isbn = {1095-9572 (Electronic)$\backslash$r1053-8119 (Linking)},
			issn = {10959572},
			journal = {NeuroImage},
			keywords = {Dynamic conditional correlations,Dynamics,FMRI,Functional connectivity,Resting state},
			pages = {531--546},
			pmid = {24993894},
			publisher = {Elsevier Inc.},
			title = {{Evaluating dynamic bivariate correlations in resting-state fMRI: A comparison study and a new approach}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2014.06.052 http://ac.els-cdn.com/S1053811914005291/1-s2.0-S1053811914005291-main.pdf?{\_}tid=08385ecc-3fad-11e7-8cd5-00000aab0f26{\&}acdnat=1495539985{\_}cce1872f357316a9266c6a8d1ca50442},
			volume = {101},
			year = {2014}
			}

			@article{Kumar2006,
			author = {Kumar, Amod and Anand, Sneh and Yaddanapudi, L N},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Kumar, Anand, Yaddanapudi - 2006 - Fuzzy model for estimating induction dose for general anesthesia.pdf:pdf},
			journal = {Industrial Research},
			keywords = {body surface area,fuzzy logic,induction,initial anesthetic dose},
			number = {April},
			pages = {325--328},
			title = {{Fuzzy model for estimating induction dose for general anesthesia}},
			volume = {65},
			year = {2006}
			}

			@article{Spiers2007b,
			author = {Spiers, Hugo J. and Maguire, Eleanor A.},
			doi = {10.1016/j.tics.2007.06.002},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/da39a3ee5e6b4b0d3255bfef95601890afd80709.html:html},
			issn = {13646613},
			journal = {Trends in Cognitive Sciences},
			month = {aug},
			number = {8},
			pages = {356--365},
			title = {{Decoding human brain activity during real-world experiences}},
			url = {http://ac.els-cdn.com/S1053811916307881/1-s2.0-S1053811916307881-main.pdf?{\_}tid=c0ec2ed0-3724-11e7-9d5e-00000aacb360{\&}acdnat=1494601845{\_}66f1d51498cff520666bf1d0f62b0836 http://linkinghub.elsevier.com/retrieve/pii/S1364661307001519},
			volume = {11},
			year = {2007}
			}

			@article{Ekstrom2010,
			abstract = {Functional magnetic resonance imaging (fMRI) has become the dominant means of measuring behavior-related neural activity in the human brain. Yet the relation between the blood oxygen-level dependent (BOLD) signal and underlying neural activity remains an open and actively researched question. A widely accepted model, established for sensory neo-cortex, suggests that the BOLD signal reflects peri-synaptic activity in the form of the local field potential rather than the spiking rate of individual neurons. Several recent experimental results, however, suggest situations in which BOLD, spiking, and the local field potential dissociate. Two different models are discussed, based on the literature reviewed to account for this dissociation, a circuitry-based and vascular-based explanation. Both models are found to account for existing data under some testing situations and in certain brain regions. Because both the vascular and local circuitry-based explanations challenge the BOLD-LFP coupling model, these models provide guidance in predicting when BOLD can be expected to reflect neural processing and when the underlying relation with BOLD may be more complex than a direct correspondence. {\textcopyright} 2009 Elsevier B.V.},
			archivePrefix = {arXiv},
			arxivId = {NIHMS150003},
			author = {Ekstrom, Arne},
			doi = {10.1016/j.brainresrev.2009.12.004},
			eprint = {NIHMS150003},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/f55ca0556d1f22577055a226a29c29f46c6499be.pdf:pdf},
			isbn = {1872-6321 (Electronic)$\backslash$r0165-0173 (Linking)},
			issn = {01650173},
			journal = {Brain Research Reviews},
			keywords = {EEG,Electrophysiology,FMRI,Local field potential,Memory hippocampus,Neocortex,Perception,Single neuron,Vasculature},
			number = {2},
			pages = {233--244},
			pmid = {20026191},
			publisher = {Elsevier B.V.},
			title = {{How and when the fMRI BOLD signal relates to underlying neural activity: The danger in dissociation}},
			url = {http://dx.doi.org/10.1016/j.brainresrev.2009.12.004 http://ac.els-cdn.com/S0165017309001325/1-s2.0-S0165017309001325-main.pdf?{\_}tid=2b0e49f0-3fa0-11e7-a645-00000aacb362{\&}acdnat=1495534460{\_}735a2b389100ae5d14fa97b78c1a5bd0},
			volume = {62},
			year = {2010}
			}

			@article{Blankertz2011,
			abstract = {Analyzing brain states that correspond to event related potentials (ERPs) on a single trial basis is a hard problem due to the high trial-to-trial variability and the unfavorable ratio between signal (ERP) and noise (artifacts and neural background activity). In this tutorial, we provide a comprehensive framework for decoding ERPs, elaborating on linear concepts, namely spatio-temporal patterns and filters as well as linear ERP classification. However, the bottleneck of these techniques is that they require an accurate covariance matrix estimation in high dimensional sensor spaces which is a highly intricate problem. As a remedy, we propose to use shrinkage estimators and show that appropriate regularization of linear discriminant analysis (LDA) by shrinkage yields excellent results for single-trial ERP classification that are far superior to classical LDA classification. Furthermore, we give practical hints on the interpretation of what classifiers learned from the data and demonstrate in particular that the trade-off between goodness-of-fit and model complexity in regularized LDA relates to a morphing between a difference pattern of ERPs and a spatial filter which cancels non task-related brain activity.},
			author = {Blankertz, Benjamin and Lemm, Steven and Treder, Matthias and Haufe, Stefan and M{\"{u}}ller, Klaus-Robert},
			doi = {10.1016/j.neuroimage.2010.06.048},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Blankertz et al. - 2011 - Single-trial analysis and classification of ERP components--a tutorial.pdf:pdf},
			issn = {1095-9572},
			journal = {NeuroImage},
			keywords = {Algorithms,Brain,Brain Mapping,Brain Mapping: methods,Brain: physiology,Electroencephalography,Electroencephalography: methods,Evoked Potentials,Evoked Potentials: physiology,Humans,Signal Processing, Computer-Assisted},
			month = {may},
			number = {2},
			pages = {814--25},
			pmid = {20600976},
			title = {{Single-trial analysis and classification of ERP components--a tutorial.}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/20600976},
			volume = {56},
			year = {2011}
			}

			@article{Shimpo2013,
			abstract = {A brain-computer interface (BCI) is a technique for controlling devices with the measured human brain activities. Especially, an asynchronous BCI is one of the most important topics since practical input interfaces are incomplete without self-paced inputs. In order to construct an asynchronous BCI, it is essential to recognize the standby state, where a user enters no commands. In this paper, we propose a novel method for detecting the standby state and develop an asynchronous BCI based on event-related potentials with the intended movement direction.We conducted online experiments with developed asynchronous BCI. As a result, all three subjects showed considerable recognition accuracies.},
			author = {Shimpo, Keita and Tanaka, Toshihisa},
			doi = {10.1109/EMBC.2013.6610484},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Shimpo, Tanaka - 2013 - Asynchronous brain-computer interfacing based on intended movement direction.pdf:pdf},
			isbn = {9781457702167},
			issn = {1557-170X},
			journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference},
			keywords = {Biomedical signal classification,Neural networks in biosignal processing and classi,Support vector machines (SVM) applied to biosignal,asynchronous},
			mendeley-tags = {asynchronous},
			month = {jan},
			pages = {4251--4},
			pmid = {24110671},
			title = {{Asynchronous brain-computer interfacing based on intended movement direction.}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/24110671},
			volume = {2013},
			year = {2013}
			}

			@article{Gozzo2009,
			author = {Gozzo, Yeisid and Vohr, Betty and Lacadie, Cheryl and Hampson, Michelle and Katz, Karol H and Maller-kesselman, Jill and Schneider, Karen C and Peterson, Bradley S and Rajeevan, Nallakkandi and Makuch, Robert W and Constable, R Todd and Ment, Laura R},
			doi = {10.1016/j.neuroimage.2009.06.046},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/460b776493bbf1317fc2a3685978e5cc1421af51.pdf:pdf},
			issn = {1053-8119},
			journal = {NeuroImage},
			number = {2},
			pages = {458--463},
			publisher = {Elsevier Inc.},
			title = {{Alterations in neural connectivity in preterm children at school age â}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2009.06.046 http://ac.els-cdn.com/S105381190900682X/1-s2.0-S105381190900682X-main.pdf?{\_}tid=5eeb5872-3658-11e7-bfb6-00000aacb362{\&}acdnat=1494514063{\_}7965219a27fc90486e6d46498f7a9649},
			volume = {48},
			year = {2009}
			}

			@article{Kiviniemi2011,
			abstract = {Abstract Recent evidence on resting-state networks in functional (connectivity) magnetic resonance imaging (fcMRI) suggests that there may be significant spatial variability of activity foci over time. This study used a sliding time window approach with the spatial domainâindependent component analysis (SliTICA) to detect spatial maps of resting-state networks over time. The study hypothesis was that the spatial distribution of a functionally connected network would present marked variability over time. The spatial stability of successive sliding-window maps of the default mode network (DMN) from fcMRI data of 12 participants imaged in the resting state was analyzed. Control measures support previous findings on the stability of independent component analysis in measuring sliding-window sources accurately. The spatial similarity of successive DMN maps varied over time at low frequencies and presented a 1/f power spectral pattern. SliTICA maps show marked temporal variation within the DMN; a single voxel w...},
			author = {Kiviniemi, Vesa and Vire, Tapani and Remes, Jukka and Elseoud, Ahmed Abou and Starck, Tuomo and Tervonen, Osmo and Nikkinen, Juha},
			doi = {10.1089/brain.2011.0036},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/714ac020440ef53636935a142dda7d17d00f4b5a.pdf:pdf},
			isbn = {2158-0022 (Electronic)},
			issn = {2158-0014},
			journal = {Brain Connectivity},
			keywords = {connectivity,functional,functional connectivity,independent compo-,magnetic resonance imaging,nent analysis,resting-state sliding window,spatial stability},
			number = {4},
			pages = {339--347},
			pmid = {22432423},
			title = {{A Sliding Time-Window ICA Reveals Spatial Variability of the Default Mode Network in Time}},
			url = {http://www.liebertonline.com/doi/abs/10.1089/brain.2011.0036},
			volume = {1},
			year = {2011}
			}

			@article{Manuel2010,
			author = {Manuel, Iv{\'{a}}n and N{\'{u}}{\~{n}}ez, Benito},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Manuel, N{\'{u}}{\~{n}}ez - 2010 - Artifact Detection.pdf:pdf},
			number = {June},
			title = {{Artifact Detection}},
			year = {2010}
			}

			@article{Farewell1988,
			abstract = {This paper describes the development and testing of a system whereby one can communicate through a computer by using the P300 component of the event-related brain potential (ERP). Such a system may be used as a communication aid by individuals who cannot use any motor system for communication (e.g., 'locked-in' patients). The 26 letters of the alphabet, together with several other symbols and commands, are displayed on a computer screen which serves as the keyboard or prosthetic device. The subject focuses attention successively on the characters he wishes to communicate. The computer detects the chosen character on-line and in real time. This detection is achieved by repeatedly flashing rows and columns of the matrix. When the elements containing the chosen character are flashed, a P300 is elicited, and it is this P300 that is detected by the computer. We report an analysis of the operating characteristics of the system when used with normal volunteers, who took part in 2 experimental sessions. In the first session (the pilot study/training session) subjects attempted to spell a word and convey it to a voice synthesizer for production. In the second session (the analysis of the operating characteristics of the system) subjects were required simply to attend to individual letters of a word for a specific number of trials while data were recorded for off-line analysis. The analyses suggest that this communication channel can be operated accurately at the rate of 0.20 bits/sec. In other words, under the conditions we used, subjects can communicate 12.0 bits, or 2.3 characters, per min.},
			author = {Farewell, LA and Donchin, E.},
			journal = {Electroencephalogr Clin Neurophysiol.},
			keywords = {BCI,ERP},
			mendeley-tags = {BCI,ERP},
			number = {6},
			pages = {510--23},
			title = {{Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials.}},
			volume = {70},
			year = {1988}
			}

			@article{Schott2018,
			author = {Schott, Bj{\"{o}}rn H. and W{\"{u}}stenberg, Torsten and L{\"{u}}cke, Eva and Pohl, Ina-Maria and Richter, Anni and Seidenbecher, Constanze I. and Pollmann, Stefan and Kizilirmak, Jasmin M. and Richardson-Klavehn, Alan},
			doi = {10.1002/hbm.24467},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/ae0957d2f871be573232c2d25862705337e2e33a.html:html},
			issn = {10659471},
			journal = {Human Brain Mapping},
			month = {nov},
			title = {{Gradual acquisition of visuospatial associative memory representations via the dorsal precuneus}},
			url = {https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.24467 http://doi.wiley.com/10.1002/hbm.24467},
			year = {2018}
			}

			@article{McMillan2013,
			abstract = {Nearly 60 years ago, Jerome L. Singer launched a groundbreaking research program into daydreaming (Singer, 1955, 1975, 2009) that presaged and laid the foundation for virtually every major strand of mind wandering research active today (Antrobus, 1999; Klinger, 1999, 2009). Here we review Singer's enormous contribution to the field, which includes insights, methodologies, and tools still in use today, and trace his enduring legacy as revealed in the recent proliferation of mind wandering studies. We then turn to the central theme in Singer's work, the adaptive nature of positive constructive daydreaming, which was a revolutionary idea when Singer began his work in the 1950s and remains underreported today. Last, we propose a new approach to answering the enduring question: Why does mind wandering persist and occupy so much of our time, as much as 50{\%} of our waking time according to some estimates, if it is as costly as most studies suggest?},
			author = {McMillan, Rebecca L and Kaufman, Scott Barry and Singer, Jerome L},
			doi = {10.3389/fpsyg.2013.00626},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/McMillan, Kaufman, Singer - 2013 - Ode to positive constructive daydreaming.pdf:pdf},
			issn = {1664-1078},
			journal = {Frontiers in psychology},
			keywords = {creativity,daydreaming,daydreaming, positive constructive daydreaming, vo,default mode network,intelligence,jerome l,mind wandering,positive constructive daydreaming,singer,volitional daydreaming},
			month = {jan},
			number = {September},
			pages = {626},
			pmid = {24065936},
			title = {{Ode to positive constructive daydreaming.}},
			url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3779797{\&}tool=pmcentrez{\&}rendertype=abstract},
			volume = {4},
			year = {2013}
			}

			@article{Beck2015,
			abstract = {Modeling and control of drug dosing regimes are particularly well-suited for applications of control design and analysis techniques. These problems frequently incorporate the use of mathematical models, lending themselves to a large range of model-based control methods. There has been ongoing research aimed at the development of closed-loop drug dosing and delivery regimens in a number of specific medical domains for more than five decades. In this paper, we discuss the development of modeling and control methods aimed at closed-loop delivery of pharmaceutical agents. We focus most of this discussion on the problem of controlling sedation levels during surgical procedures; results from the application of linear parameter varying and robust {\textless}sup{\textgreater}L1{\textless}/sup{\textgreater}-adaptive modeling and control approaches are presented in some detail.},
			author = {Beck, Carolyn L.},
			doi = {10.1016/j.ejcon.2015.04.006},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Beck - 2015 - Modeling and control of pharmacodynamics.pdf:pdf},
			issn = {09473580},
			journal = {European Journal of Control},
			keywords = {Anesthesia control piecewise-linear systems,Linear parameter varying control,Pharmacodynamics,Pharmacokinetics,Robust and adaptive control},
			pages = {33--49},
			publisher = {Elsevier},
			title = {{Modeling and control of pharmacodynamics}},
			url = {http://dx.doi.org/10.1016/j.ejcon.2015.04.006},
			volume = {24},
			year = {2015}
			}

			@article{Feinberg2010,
			author = {Feinberg, David A and Moeller, Steen and Smith, Stephen M and Auerbach, Edward and Ramanna, Sudhir and Matt, F and Miller, Karla L and Ugurbil, Kamil and Yacoub, Essa},
			doi = {10.1371/journal.pone.0015710},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/33647debc4078a862afc65450a5a783e316a5116.pdf:pdf},
			journal = {PLoS ONE},
			number = {12},
			title = {{Multiplexed Echo Planar Imaging for Sub-Second Whole Brain FMRI and Fast Diffusion Imaging}},
			url = {http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0015710{\&}type=printable},
			volume = {c},
			year = {2010}
			}

			@article{Attwell2011,
			abstract = {Blood flow in the brain is regulated by neurons and astrocytes. Knowledge of how these cells control blood flow is crucial for understanding how neural computation is powered, for interpreting functional imaging scans of brains, and for developing treatments for neurological disorders. It is now recognized that neurotransmitter-mediated signalling has a key role in regulating cerebral blood flow, that much of this control is mediated by astrocytes, that oxygen modulates blood flow regulation, and that blood flow may be controlled by capillaries as well as by arterioles. These conceptual shifts in our understanding of cerebral blood flow control have important implications for the development of new therapeutic approaches.},
			author = {Attwell, David and Buchan, Alastair and Charpak, Serge and Lauritzen, Martin and MacVicar, Brian and Newman, Eric},
			doi = {10.1038/nature09613},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/1c9210bb3b2a7df990448020965c8201810dff33.pdf:pdf},
			journal = {Nature},
			keywords = {BOLD contrast,neurovascular coupling},
			mendeley-tags = {BOLD contrast,neurovascular coupling},
			number = {7321},
			pages = {232--243},
			title = {{Glial and neuronal control of brain blood flow}},
			url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3206737/pdf/nihms272381.pdf},
			volume = {468},
			year = {2011}
			}

			@article{GonzalesCastillo2015,
			abstract = {Functional connectivity (FC) patterns in functional MRI exhibit dynamic behavior on the scale of seconds, with rich spatiotemporal structure and limited sets of whole-brain, quasi-stable FC configu- rations (FC states) recurring across time and subjects. Based on previous evidence linking various aspects of cognition to group- level, minute-to-minute FC changes in localized connections, we hypothesized that whole-brain FC states may reflect the global, orchestrated dynamics of cognitive processing on the scale of seconds. To test this hypothesis, subjects were continuously scanned as they engaged in and transitioned between mental states dictated by tasks. FC states computed within windows as short as 22.5 s permitted robust tracking of cognition in single subjects with near perfect accuracy. Accuracy dropped markedly for subjects with the lowest task performance. Spatially restricting FC in- formation decreased accuracy at short time scales, emphasizing the distributed nature of whole-brain FC dynamics, beyond univariate magnitude changes, as valuable markers of cognition.},
			archivePrefix = {arXiv},
			arxivId = {arXiv:1408.1149},
			author = {Gonzalez-Castillo, Javier and Hoy, Colin W. and Handwerker, Daniel A. and Robinson, Meghan E. and Buchanan, Laura C. and Saad, Ziad S. and Bandettini, Peter A.},
			doi = {10.1073/pnas.1501242112},
			eprint = {arXiv:1408.1149},
			file = {:Users/lorenafreitas/Downloads/Articles for Journal Club-20170517/Gonzales{\_}Castillo{\_}Decoding{\_}States{\_}2.pdf:pdf},
			isbn = {1091-6490 (Electronic)$\backslash$r0027-8424 (Linking)},
			issn = {0027-8424},
			journal = {Proceedings of the National Academy of Sciences},
			number = {28},
			pages = {8762--8767},
			pmid = {26124112},
			title = {{Tracking ongoing cognition in individuals using brief, whole-brain functional connectivity patterns}},
			url = {http://www.pnas.org/lookup/doi/10.1073/pnas.1501242112},
			volume = {112},
			year = {2015}
			}

			@article{Turner1993,
			author = {Turner, R and Jezzard, P and Wen, H and Kwong, K K and Bihan, D Le and Zeffiro, T and Balaban, R S},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/3fd975869b15fb55321b606cd7227d0e2a7acb40.pdf:pdf},
			journal = {Magnetic Resonance in Medicine},
			pages = {277--279},
			title = {{Functional Mapping of the Human Visual Cortex at 4 and 1 . 5 Tesla Using Deoxygenation Contrast EPI}},
			url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.459.883{\&}rep=rep1{\&}type=pdf},
			volume = {29},
			year = {1993}
			}

			@article{Smyser2012,
			author = {Smyser, Christopher D and Snyder, AZ and Neil, JJ},
			doi = {10.1016/j.neuroimage.2011.02.073.Functional},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/8fa71bd5e49334f8946cd07f67229d737b488c68.pdf:pdf},
			number = {3},
			pages = {1437--1452},
			title = {{Functional Connectivity MRI in Infants: Exploration of the Functional Organization of the Developing Brain}},
			url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3089442/pdf/nihms286229.pdf},
			volume = {56},
			year = {2012}
			}

			@article{Bandettini1993,
			author = {Bandettini, Peter A and Jesmanowicz, A and Wong, Eric C and Hyde, James S},
			file = {:Users/lorenafreitas/Downloads/bandettini1993.pdf:pdf},
			journal = {Magnetic Resonance in Medicine},
			keywords = {been observed by several,cessing,echo planar imaging,functional mri,groups using several types,image pro-,including gradient-echo echo-planar imaging,motor cortex,of,sequences},
			pages = {161--173},
			title = {{Processing Strategies for Time-Course Data Sets in Functional MRI of the Human Brain}},
			volume = {30},
			year = {1993}
			}

			@article{Makeig1996,
			author = {Makeig, Scott and Bell, Anthony J and Sejnowski, Terrence J},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Makeig, Bell, Sejnowski - 1996 - Independent Component Analysis of Electroencephalographic Data.pdf:pdf},
			number = {3},
			pages = {145--151},
			title = {{Independent Component Analysis of Electroencephalographic Data}},
			url = {http://sccn.ucsd.edu/{~}scott/pdf/nips95b.pdf},
			year = {1996}
			}

			@article{Mognon2010,
			abstract = {Abstract A successful method for removing artifacts from electroencephalogram (EEG) recordings is Independent Component Analysis (ICA), but its implementation remains largely user-dependent. Here, we propose a completely automatic algorithm (ADJUST) that identifies artifacted independent components by combining stereotyped artifact-specific spatial and temporal features. Features were optimized to capture blinks, eye movements, and generic discontinuities on a feature selection dataset. Validation on a totally different EEG dataset shows that (1) ADJUST's classification of independent components largely matches a manual one by experts (agreement on 95.2{\%} of the data variance), and (2) Removal of the artifacted components detected by ADJUST leads to neat reconstruction of visual and auditory event-related potentials from heavily artifacted data. These results demonstrate that ADJUST provides a fast, efficient, and automatic way to use ICA for artifact removal.},
			author = {Mognon, Andrea and Jovicich, Jorge and Bruzzone, Lorenzo and Buiatti, Marco},
			doi = {10.1111/j.1469-8986.2010.01061.x},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Mognon et al. - 2010 - ADJUST An automatic EEG artifact detector based on the joint use of spatial and temporal features.pdf:pdf},
			issn = {1540-5958},
			journal = {Psychophysiology},
			month = {jul},
			pages = {229--240},
			pmid = {20636297},
			title = {{ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features.}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/20636297},
			volume = {48},
			year = {2010}
			}

			@article{Yang2015,
			author = {Yang, Yuxiao and Shanechi, Maryam M.},
			doi = {10.1109/EMBC.2015.7318557},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Yang, Shanechi - 2015 - A generalizable adaptive brain-machine interface design for control of anesthesia.pdf:pdf},
			isbn = {9781424492718},
			issn = {1557170X},
			journal = {Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS},
			keywords = {Brain-computer/machine interface,Brain physiology},
			pages = {1099--1102},
			title = {{A generalizable adaptive brain-machine interface design for control of anesthesia}},
			volume = {2015-Novem},
			year = {2015}
			}

			@article{Andre2016,
			author = {Andre, Julia and Picchioni, Marco and Zhang, Ruibin and Toulopoulou, Timothea},
			doi = {10.1016/j.nicl.2015.12.002},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/eb452f2343d9bfbe3f47488d5c8b7d7297b16da2.pdf:pdf},
			issn = {2213-1582},
			journal = {NeuroImage: Clinical},
			keywords = {Brain activation,Neurodevelopment,Neurodevelopmental disorders,Schizophrenia,Working memory,fMRI},
			pages = {940--948},
			publisher = {The Authors},
			title = {{Working memory circuit as a function of increasing age in healthy adolescence : A systematic review and meta-analyses}},
			url = {http://dx.doi.org/10.1016/j.nicl.2015.12.002 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5153561/pdf/main.pdf},
			volume = {12},
			year = {2016}
			}

			@article{Bibian2015,
			author = {Bibian, Stephane and Dumont, Guy a. and Black, Ian},
			doi = {10.7205/MILMED-D-14-00380},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Bibian, Dumont, Black - 2015 - Closed-Loop Target-Controlled Infusion Systems Stability and Performance Aspects.pdf:pdf},
			issn = {0026-4075},
			journal = {Military Medicine},
			number = {3S},
			pages = {96--103},
			title = {{Closed-Loop Target-Controlled Infusion Systems: Stability and Performance Aspects}},
			url = {http://publications.amsus.org/doi/10.7205/MILMED-D-14-00380},
			volume = {180},
			year = {2015}
			}

			@article{Cohen2017,
			author = {Cohen, Jonathan D and Daw, Nathaniel and Engelhardt, Barbara and Hasson, Uri and Li, Kai and Niv, Yael and Norman, Kenneth A and Pillow, Jonathan and Ramadge, Peter J and Turk-browne, Nicholas B and Willke, Theodore L},
			doi = {10.1038/nn.4499},
			file = {:Users/lorenafreitas/Downloads/nn.4499.pdf:pdf},
			number = {3},
			title = {{Computational approaches to fMRI analysis}},
			volume = {20},
			year = {2017}
			}

			@article{Harmony1996,
			abstract = {In previous papers we proposed that an increase in delta EEG activity during mental tasks might be related to an increase in subjects' attention to internal processing. In this paper we have made a narrow band analysis to detect those EEG frequencies that change selectively during the performance of a mental task that requires attention to internal processing. Two different experiments were performed: (1) a difficult mental calculation task and a control stimulus with the same physical characteristics as the arithmetical symbols were presented in random order; (2) the Sternberg paradigm for the analysis of short term memory using a memory set of 5 or 3 digits was also presented in random order. Referential recordings to linked ears were obtained in all leads of the 10/20 system. In the first experiment, the increase of power from 1.56 to 5.46 Hz was observed only during the performance of the task and not during the control condition. In the Sternberg paradigm, the increase of power from 1.56 to 3.90 Hz was greater during the difficult than during the easy condition. These results support our hypothesis that an increase in delta activity may be related to attention to internal processing during the performance of a mental task.},
			author = {Harmony, T and Fern{\'{a}}ndez, T and Silva, J and Bernal, J and D{\'{i}}az-Comas, L and Reyes, a and Marosi, E and Rodr{\'{i}}guez, M},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Harmony et al. - 1996 - EEG delta activity an indicator of attention to internal processing during performance of mental tasks.pdf:pdf},
			issn = {0167-8760},
			journal = {International journal of psychophysiology : official journal of the International Organization of Psychophysiology},
			keywords = {Adult,Alpha Rhythm,Alpha Rhythm: statistics {\&} numerical data,Attention,Attention: physiology,Delta Rhythm,Delta Rhythm: statistics {\&} numerical data,Electroencephalography,Electroencephalography: statistics {\&} numerical dat,Evoked Potentials,Evoked Potentials: physiology,Humans,Male,Mental Processes,Mental Processes: physiology,Psychomotor Performance,Psychomotor Performance: physiology},
			month = {nov},
			number = {1-2},
			pages = {161--71},
			pmid = {8978441},
			title = {{EEG delta activity: an indicator of attention to internal processing during performance of mental tasks.}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/8978441},
			volume = {24},
			year = {1996}
			}

			@article{Haynes2006,
			abstract = {Recent advances in human neuroimaging have shown that it is possible to accurately decode a person's conscious experience based only on non-invasive measurements of their brain activity. Such 'brain reading' has mostly been studied in the domain of visual perception, where it helps reveal the way in which individual experiences are encoded in the human brain. The same approach can also be extended to other types of mental state, such as covert attitudes and lie detection. Such applications raise important ethical issues concerning the privacy of personal thought.},
			author = {Haynes, John-Dylan and Rees, Geraint},
			doi = {10.1038/nrn1931},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Haynes, Rees - 2006 - Decoding mental states from brain activity in humans.pdf:pdf},
			issn = {1471-003X},
			journal = {Nature reviews. Neuroscience},
			keywords = {Brain,Brain Mapping,Brain Mapping: methods,Brain: physiology,Consciousness,Consciousness: physiology,Humans,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Mental Processes,Mental Processes: physiology,Photic Stimulation,Photic Stimulation: methods,Visual Perception,Visual Perception: physiology},
			month = {jul},
			number = {7},
			pages = {523--34},
			pmid = {16791142},
			title = {{Decoding mental states from brain activity in humans.}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/16791142},
			volume = {7},
			year = {2006}
			}

			@article{Haxby2001,
			abstract = {The functional architecture of the object vision pathway in the human brain was investigated using functional magnetic resonance imaging to measure patterns of response in ventral temporal cortex while subjects viewed faces, cats, five categories of man-made objects, and nonsense pictures. A distinct pattern of response was found for each stimulus category. The distinctiveness of the response to a given category was not due simply to the regions that responded maximally to that category, because the category being viewed also could be identified on the basis of the pattern of response when those regions were excluded from the analysis. Patterns of response that discriminated among all categories were found even within cortical regions that responded maximally to only one category. These results indicate that the representations of faces and objects in ventral temporal cortex are widely distributed and overlapping.},
			archivePrefix = {arXiv},
			arxivId = {arXiv:1011.1669v3},
			author = {Haxby, J. V.},
			doi = {10.1126/science.1063736},
			eprint = {arXiv:1011.1669v3},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/b48c524006b6436b2a81702d9b8f6fe0750de763.pdf:pdf},
			isbn = {0036-8075},
			issn = {00368075},
			journal = {Science},
			number = {5539},
			pages = {2425--2430},
			pmid = {11577229},
			title = {{Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex}},
			url = {http://www.sciencemag.org/cgi/doi/10.1126/science.1063736},
			volume = {293},
			year = {2001}
			}

			@article{Jirayucharoensak2013,
			author = {Jirayucharoensak, Suwicha and Israsena, Pasin and Pan-ngum, Setha and Hemrungrojn, Solaphat},
			doi = {10.1109/BMEiCon.2013.6687708},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Jirayucharoensak et al. - 2013 - Online EEG artifact suppression for neurofeedback training systems.pdf:pdf},
			isbn = {978-1-4799-1467-8},
			journal = {The 6th 2013 Biomedical Engineering International Conference},
			keywords = {eeg},
			month = {oct},
			pages = {1--5},
			publisher = {Ieee},
			title = {{Online EEG artifact suppression for neurofeedback training systems}},
			url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6687708},
			year = {2013}
			}

			@article{Daly2013,
			abstract = {Contamination of the electroencephalogram (EEG) by artifacts greatly reduces the quality of the recorded signals. There is a need for automated artifact removal methods. However, such methods are rarely evaluated against one another via rigorous criteria, with results often presented based upon visual inspection alone. This work presents a comparative study of automatic methods for removing blink, electrocardiographic, and electromyographic artifacts from the EEG. Three methods are considered; wavelet, blind source separation (BSS), and multivariate singular spectrum analysis (MSSA)-based correction. These are applied to data sets containing mixtures of artifacts. Metrics are devised to measure the performance of each method. The BSS method is seen to be the best approach for artifacts of high signal to noise ratio (SNR). By contrast, MSSA performs well at low SNRs but at the expense of a large number of false positive corrections.},
			author = {Daly, Ian and Nicolaou, Nicoletta and Nasuto, Slawomir Jaroslaw and Warwick, Kevin},
			doi = {10.1177/1550059413476485},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Daly et al. - 2013 - Automated artifact removal from the electroencephalogram a comparative study.pdf:pdf},
			issn = {1550-0594},
			journal = {Clinical EEG and neuroscience},
			keywords = {Algorithms,Artifacts,Brain,Brain: physiopathology,Data Interpretation, Statistical,Diagnosis, Computer-Assisted,Diagnosis, Computer-Assisted: methods,Electroencephalography,Electroencephalography: methods,Humans,Pattern Recognition, Automated,Pattern Recognition, Automated: methods,Reproducibility of Results,Sensitivity and Specificity,Wavelet Analysis},
			month = {oct},
			number = {4},
			pages = {291--306},
			pmid = {23666954},
			title = {{Automated artifact removal from the electroencephalogram: a comparative study.}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/23666954},
			volume = {44},
			year = {2013}
			}

			@article{Moiseev2014,
			author = {Moiseev, Alexander and Doesburg, Sam M and Grunau, Ruth E},
			doi = {10.1007/s10548-014-0416-0},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/f575e169009886ddbbbd64b0d8593a51f1603528.pdf:pdf},
			isbn = {1054801404160},
			issn = {1573-6792},
			journal = {Brain Topography},
			keywords = {Developmental neuroimaging,Functional connectivity,Magnetoencephalography,Neural networks,Neural oscillations,Neural synchrony,Prematurity,Preterm,Short-term memory,connectivity {\'{a}} developmental neuroimaging,magnetoencephalography {\'{a}} neural,memory {\'{a}} neural networks,prematurity {\'{a}} functional,synchrony {\'{a}} preterm {\'{a}},{\'{a}} neural oscillations,{\'{a}} short-term},
			number = {5},
			pages = {726--745},
			publisher = {Springer US},
			title = {{Altered Network Oscillations and Functional Connectivity Dynamics in Children Born Very Preterm}},
			url = {http://download.springer.com/static/pdf/555/art{\%}253A10.1007{\%}252Fs10548-014-0416-0.pdf?originUrl=http{\%}3A{\%}2F{\%}2Flink.springer.com{\%}2Farticle{\%}2F10.1007{\%}2Fs10548-014-0416-0{\&}token2=exp=1494515116{~}acl={\%}2Fstatic{\%}2Fpdf{\%}2F555{\%}2Fart{\%}25253A10.1007{\%}25252Fs10548-014-041},
			volume = {28},
			year = {2015}
			}

			@article{Vallesi2015,
			abstract = {The ability to shift between different tasks according to internal or external demands, which is at the core of our behavioral flexibility, has been generally linked to the functionality of left fronto-parietal regions. Traditionally, the left and right hemispheres have also been associated with verbal and spatial processing, respectively. We therefore investigated with functional MRI whether the processes engaged during task-switching interact in the brain with the domain of the tasks to be switched, that is, verbal or spatial. Importantly, physical stimuli were exactly the same and participants' performance was matched between the two domains. The fMRI results showed a clearly left-lateralized involvement of fronto-parietal regions when contrasting task-switching versussingle task blocks in the context of verbal rules. A more bilateral pattern, especially in the prefrontal cortex, was instead observed for switching between spatial tasks. Moreover, while a conjunction analysis showed that the core regions involved in task-switching, independently of the switching context, were localized both in left inferior prefrontal and parietal cortices and in bilateral supplementary motor area, a direct analysis of functional lateralization revealed that hemispheric asymmetries in the frontal lobes were more biased toward the left side for the verbal domain than for the spatial one and vice versa. Overall, these findings highlight the role of left fronto-parietal regions in task-switching, above and beyond the specific task requirements, but also show that hemispheric asymmetries may be modulated by the more specific nature of the tasks to be performed during task-switching.},
			author = {Vallesi, Antonino and Arbula, Sandra and Capizzi, Mariagrazia and Causin, Francesco and D'Avella, Domenico},
			doi = {10.1016/j.cortex.2015.01.016},
			file = {:Users/lorenafreitas/Downloads/VallesiArbulaCapizziCausinDAvellaCortex2015{\_}AuthorsVersion.pdf:pdf},
			isbn = {1973-8102 (Electronic)$\backslash$r0010-9452 (Linking)},
			issn = {19738102},
			journal = {Cortex},
			keywords = {Conjunction analysis,Executive functions,Hemispheric asymmetries,Left prefrontal cortex,Task-switching},
			number = {February},
			pages = {173--183},
			pmid = {25734897},
			title = {{Domain-independent neural underpinning of task-switching: AN fMRI investigation}},
			volume = {65},
			year = {2015}
			}

			@article{Delorme2007,
			abstract = {Detecting artifacts produced in EEG data by muscle activity, eye blinks and electrical noise is a common and important problem in EEG research. It is now widely accepted that independent component analysis (ICA) may be a useful tool for isolating artifacts and/or cortical processes from electroencephalographic (EEG) data. We present results of simulations demonstrating that ICA decomposition, here tested using three popular ICA algorithms, Infomax, SOBI, and FastICA, can allow more sensitive automated detection of small non-brain artifacts than applying the same detection methods directly to the scalp channel data. We tested the upper bound performance of five methods for detecting various types of artifacts by separately optimizing and then applying them to artifact-free EEG data into which we had added simulated artifacts of several types, ranging in size from thirty times smaller (-50 dB) to the size of the EEG data themselves (0 dB). Of the methods tested, those involving spectral thresholding were most sensitive. Except for muscle artifact detection where we found no gain of using ICA, all methods proved more sensitive when applied to the ICA-decomposed data than applied to the raw scalp data: the mean performance for ICA was higher and situated at about two standard deviations away from the performance distribution obtained on raw data. We note that ICA decomposition also allows simple subtraction of artifacts accounted for by single independent components, and/or separate and direct examination of the decomposed non-artifact processes themselves.},
			author = {Delorme, Arnaud and Sejnowski, Terrence and Makeig, Scott},
			doi = {10.1016/j.neuroimage.2006.11.004},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Delorme, Sejnowski, Makeig - 2007 - Enhanced detection of artifacts in EEG data using higher-order statistics and independent component.pdf:pdf},
			issn = {1053-8119},
			journal = {NeuroImage},
			keywords = {Algorithms,Automation,Computer Simulation,Electroencephalography,Electroencephalography: methods,Electroencephalography: standards,Humans,Reproducibility of Results,Sensitivity and Specificity,feature,kurtosis},
			mendeley-tags = {feature,kurtosis},
			month = {feb},
			number = {4},
			pages = {1443--9},
			pmid = {17188898},
			title = {{Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis.}},
			url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2895624/pdf/nihms18541.pdf http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2895624{\&}tool=pmcentrez{\&}rendertype=abstract},
			volume = {34},
			year = {2007}
			}

			@article{Muller2008,
			abstract = {Machine learning methods are an excellent choice for compensating the high variability in EEG when analyzing single-trial data in real-time. This paper briefly reviews preprocessing and classification techniques for efficient EEG-based brain-computer interfacing (BCI) and mental state monitoring applications. More specifically, this paper gives an outline of the Berlin brain-computer interface (BBCI), which can be operated with minimal subject training. Also, spelling with the novel BBCI-based Hex-o-Spell text entry system, which gains communication speeds of 6-8 letters per minute, is discussed. Finally the results of a real-time arousal monitoring experiment are presented.},
			author = {M{\"{u}}ller, Klaus-Robert and Tangermann, Michael and Dornhege, Guido and Krauledat, Matthias and Curio, Gabriel and Blankertz, Benjamin},
			doi = {10.1016/j.jneumeth.2007.09.022},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/M{\"{u}}ller et al. - 2008 - Machine learning for real-time single-trial EEG-analysis from brain-computer interfacing to mental state monitor.pdf:pdf},
			issn = {0165-0270},
			journal = {Journal of neuroscience methods},
			keywords = {Algorithms,Brain,Brain Mapping,Brain: physiology,Communication Aids for Disabled,Electroencephalography,Electromyography,Feedback,Functional Laterality,Humans,Man-Machine Systems,Mental Processes,Mental Processes: physiology,Signal Processing, Computer-Assisted,Spectrum Analysis,User-Computer Interface},
			month = {jan},
			number = {1},
			pages = {82--90},
			pmid = {18031824},
			title = {{Machine learning for real-time single-trial EEG-analysis: from brain-computer interfacing to mental state monitoring.}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/18031824},
			volume = {167},
			year = {2008}
			}

			@article{Bandettini1992,
			author = {Bandettini, Peter A. and Wong, Eric and Hinks, Scott and Tikofsky, Ronald and Hyde, James},
			file = {:Users/lorenafreitas/Downloads/bandettini1992.pdf:pdf},
			journal = {Magnetic Resonance in Medicine},
			pages = {390--397},
			title = {{Time Course EPI of Human Brain Function during Task Activation}},
			volume = {397},
			year = {1992}
			}

			@article{Arichi2012,
			abstract = {In the rodent brain the hemodynamic response to a brief external stimulus changes significantly during development. Analogous changes in human infants would complicate the determination and use of the hemodynamic response function (HRF) for functional magnetic resonance imaging (fMRI) in developing populations. We aimed to characterize HRF in human infants before and after the normal time of birth using rapid sampling of the Blood Oxygen Level Dependent (BOLD) signal. A somatosensory stimulus and an event related experimental design were used to collect data from 10 healthy adults, 15 sedated infants at term corrected post menstrual age (PMA) (median 41 + 1 weeks), and 10 preterm infants (median PMA 34 + 4 weeks). A positive amplitude HRF waveform was identified across all subject groups, with a systematic maturational trend in terms of decreasing time-to-peak and increasing positive peak amplitude associated with increasing age. Application of the age-appropriate HRF models to fMRI data significantly improved the precision of the fMRI analysis. These findings support the notion of a structured development in the brain's response to stimuli across the last trimester of gestation and beyond. {\textcopyright} 2012 Elsevier Inc.},
			author = {Arichi, Tomoki and Fagiolo, Gianlorenzo and Varela, Marta and Melendez-Calderon, Alejandro and Allievi, Alessandro and Merchant, Nazakat and Tusor, Nora and Counsell, Serena J. and Burdet, Etienne and Beckmann, Christian F. and Edwards, A. David},
			doi = {10.1016/j.neuroimage.2012.06.054},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/179275708596b86cf05b6703c427d8bb40eb4aee.pdf:pdf},
			isbn = {1053-8119},
			issn = {10538119},
			journal = {NeuroImage},
			keywords = {Brain development,Functional MRI,Hemodynamic response function,Neonate},
			number = {2},
			pages = {663--673},
			pmid = {22776460},
			publisher = {Elsevier Inc.},
			title = {{Development of BOLD signal hemodynamic responses in the human brain}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2012.06.054 http://cogns.northwestern.edu/cbmg/development of HRF NI 2012.pdf},
			volume = {63},
			year = {2012}
			}

			@article{Buckner1996,
			author = {Buckner, Randy and Bandettini, Peter and O'craven, Kathleen and Savoy, Robert and Petersen, Steven and Raichle, Marcus and Rosen, Bruce},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/646cbcf95531b62fd9740d0189b038f11c34f9f1.pdf:pdf},
			journal = {Proceedings of the National Academy of Sciences},
			number = {December},
			pages = {14878--14883},
			title = {{Detection of cortical activation during averaged single trials of a cognitive task using functional magnetic resonance imaging}},
			url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC26230/pdf/pq014878.pdf},
			volume = {93},
			year = {1996}
			}

			@article{GonzalezCastillo2012,
			abstract = {The brain is the body's largest energy consumer, even in the ab-sence of demanding tasks. Electrophysiologists report on-going neuronal firing during stimulation or task in regions beyond those of primary relationship to the perturbation. Although the biolog-ical origin of consciousness remains elusive, it is argued that it emerges from complex, continuous whole-brain neuronal collabo-ration. Despite converging evidence suggesting the whole brain is continuously working and adapting to anticipate and actuate in response to the environment, over the last 20 y, task-based func-tional MRI (fMRI) have emphasized a localizationist view of brain function, with fMRI showing only a handful of activated regions in response to task/stimulation. Here, we challenge that view with evidence that under optimal noise conditions, fMRI activations extend well beyond areas of primary relationship to the task; and blood-oxygen level-dependent signal changes correlated with task-timing appear in over 95{\%} of the brain for a simple visual stimulation plus attention control task. Moreover, we show that response shape varies substantially across regions, and that whole-brain parcellations based on those differences produce dis-tributed clusters that are anatomically and functionally meaning-ful, symmetrical across hemispheres, and reproducible across subjects. These findings highlight the exquisite detail lying in fMRI signals beyond what is normally examined, and emphasize both the pervasiveness of false negatives, and how the sparseness of fMRI maps is not a result of localized brain function, but a conse-quence of high noise and overly strict predictive response models. fMRI \| activation extent \| transient responses \| clustering F or years, positive gamma-like responses were the primary type of blood-oxygen level-dependent (BOLD) response that researchers used as indirect markers of neuronal activity. Other methods for extracting neural information from BOLD time se-ries are increasingly popular today. Spontaneous fluctuations recorded in the absence of a controlled stimulus have rapidly gained attention and shown great potential in the study of normal (1, 2) and abnormal brain function (3, 4). Multivariate methods have demonstrated that detailed information about stimulus in-put can be obtained by jointly analyzing activity in voxels that show no significance using conventional univariate analysis tech-niques. Even within the framework of univariate analysis, con-sideration of BOLD responses other than conventional positively sustained responses, such as negatively correlated or stimulus onset/offset responses, has proven useful at differentiating audi-tory or visual stimuli within primary sensory cortices (5, 6). Despite all such evidence highlighting the exquisite sensitivity of the BOLD contrast to underlying brain function, few block-design task-based functional MRI (fMRI) studies examine temporal responses other than the conventional positively sus-tained gamma-like response or conduct secondary analysis on areas of no statistical significance. Factors contributing to this practice may include the lack of desire to look at temporal dy-namics once a region is labeled statistically significant, or the fact that conventional responses have proven sufficient to uncover the neuronal correlates of a myriad of human behaviors. Unfortunately, if, as the previous discussion suggests, true neu-ronal responses are continuously passing undetected in fMRI, our conceptualizations of brain function based on task-based fMRI research might be incomplete. As Lieberman and Cunningham stated previously (7), a long-standing preoccupation with the reduction of false-positives in fMRI creates a bias toward reporting only large and obvious effects, neglecting what perhaps represents more subtle complex cognitive and affective processes. Here, we explore this hypoth-esis in detail and evaluate whether the sparseness of task-based fMRI activation maps is real or a consequence of noise levels and modeling decisions. We approach this question using low-noise fMRI time-series generated by combining unconventionally large amounts of data (100 runs per subject). With these data, we also evaluate how regional differences in BOLD response may reveal how distant regions collaborate during a particular task.},
			archivePrefix = {arXiv},
			arxivId = {arXiv:1408.1149},
			author = {Gonzalez-Castillo, J. and Saad, Z. S. and Handwerker, D. A. and Inati, S. J. and Brenowitz, N. and Bandettini, P. A.},
			doi = {10.1073/pnas.1121049109},
			eprint = {arXiv:1408.1149},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/344e582b8026d546aa24028ad9658a0d7c247f8d.pdf:pdf},
			isbn = {1121049109},
			issn = {0027-8424},
			journal = {Proceedings of the National Academy of Sciences},
			number = {14},
			pages = {5487--5492},
			pmid = {22431587},
			title = {{Whole-brain, time-locked activation with simple tasks revealed using massive averaging and model-free analysis}},
			url = {http://www.pnas.org/cgi/doi/10.1073/pnas.1121049109},
			volume = {109},
			year = {2012}
			}

			@article{Abbass2014,
			author = {Abbass, Hussein A},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Abbass - 2014 - Calibrating Independent Component Analysis with Laplacian Reference for Real-Time EEG Artifact Removal.pdf:pdf},
			journal = {Neural Information Processing Lecture Notes in Computer Science},
			keywords = {eeg pre-,electroencephalography,independent component analysis},
			pages = {68--75},
			title = {{Calibrating Independent Component Analysis with Laplacian Reference for Real-Time EEG Artifact Removal}},
			url = {http://download.springer.com/static/pdf/79/chp:10.1007/978-3-319-12643-2{\_}9.pdf?auth66=1425465467{\_}3b8b597c7ae154087be3309abb2c1c1f{\&}ext=.pdf},
			volume = {8836},
			year = {2014}
			}

			@article{Poldrack2013,
			author = {Poldrack, Russell A. and Barch, Deanna M. and Mitchell, Jason P. and Wager, Tor D and Wagner, Anthony D and Devlin, Joseph T and Cumba, Chad and Koyejo, Oluwasanmi and Milham, Michael P.},
			doi = {10.3389/fninf.2013.00012},
			file = {:Users/lorenafreitas/Downloads/fninf-07-00012.pdf:pdf},
			issn = {1662-5196},
			journal = {Frontiers in Neuroinformatics},
			keywords = {classification,data sharing,informatics,metadata,multivariate},
			number = {July},
			pages = {1--12},
			title = {{Toward open sharing of task-based fMRI data: the OpenfMRI project}},
			url = {http://journal.frontiersin.org/article/10.3389/fninf.2013.00012/abstract},
			volume = {7},
			year = {2013}
			}

			@article{Mason2007,
			author = {Mason, Malia F and Norton, Michael I and Horn, John D Van and Wegner, Daniel M and Scott, T and Macrae, C Neil},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/be314db9c457b8d636aaaebb673efb8fb1f79813.pdf:pdf},
			journal = {Science},
			number = {5810},
			pages = {393--395},
			title = {{Wandering Minds: The Default Network and Stimulus-Independent Thought}},
			url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1821121/pdf/nihms18906.xml.fixed.pdf},
			volume = {315},
			year = {2007}
			}

			@article{Robinson2009,
			author = {Robinson, Lucy F and Wager, Tor D and Lindquist, Martin A},
			doi = {10.1016/j.neuroimage.2009.08.061},
			file = {:Users/lorenafreitas/Downloads/RobinsonNI2009.pdf:pdf},
			issn = {1053-8119},
			journal = {NeuroImage},
			publisher = {Elsevier Inc.},
			title = {{Change point estimation in multi-subject fMRI studies}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2009.08.061},
			year = {2009}
			}

			@article{Birbaumer2006,
			abstract = {The discovery of event-related desynchronization (ERD) and event-related synchronization (ERS) by Pfurtscheller paved the way for the development of brain-computer interfaces (BCIs). BCIs allow control of computers or external devices with the regulation of brain activity only. Two different research traditions produced two different types of BCIs: invasive BCIs, realized with implanted electrodes in brain tissue and noninvasive BCIs using electrophysiological recordings in humans such as electroencephalography (EEG) and magnetoencephalography (MEG) and metabolic changes such as functional magnetic resonance imaging (fMRI) and near infrared spectroscopy (NIRS). Clinical applications were reserved with few exceptions for the noninvasive approach: communication with the completely paralyzed and locked-in syndrome with slow cortical potentials (SCPs), sensorimotor rhythms (SMRs), and P300 and restoration of movement and cortical reorganization in high spinal cord lesions and chronic stroke. It was demonstrated that noninvasive EEG-based BCIs allow brain-derived communication in paralyzed and locked-in patients. Movement restoration was achieved with noninvasive BCIs based on SMRs control in single cases with spinal cord lesions and chronic stroke. At present no firm conclusion about the clinical utility of BCI for the control of voluntary movement can be made. Invasive multielectrode BCIs in otherwise healthy animals allowed execution of reaching, grasping, and force variations from spike patterns and extracellular field potentials. Whether invasive approaches allow superior brain control of motor responses compared to noninvasive BCI with intelligent peripheral devices and electrical muscle stimulation and EMG feedback remains to be demonstrated. The newly developed fMRI-BCIs and NIRS-BCIs offer promise for the learned regulation of emotional disorders and also disorders of small children (in the case of NIRS).},
			author = {Birbaumer, Niels and Weber, Cornelia and Neuper, Christa},
			doi = {10.1016/S0079-6123(06)59024-7},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Birbaumer, Weber, Neuper - 2006 - Physiological regulation of thinking brainâcomputer interface (BCI) research.pdf:pdf},
			issn = {0079-6123},
			journal = {{\ldots} in brain research},
			keywords = {Amyotrophic Lateral Sclerosis,Amyotrophic Lateral Sclerosis: psychology,Amyotrophic Lateral Sclerosis: therapy,Animals,Brain,Brain Chemistry,Brain Chemistry: physiology,Brain: physiology,Communication,Computers,Humans,Paralysis,Paralysis: rehabilitation,Seizures,Seizures: therapy,User-Computer Interface},
			month = {jan},
			pages = {369--91},
			pmid = {17071243},
			title = {{Physiological regulation of thinking: brainâcomputer interface (BCI) research}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/17071243 http://www.sciencedirect.com/science/article/pii/S0079612306590247},
			volume = {159},
			year = {2006}
			}

			@article{Schalk2008,
			abstract = {Many studies over the past two decades have shown that people can use brain signals to convey their intent to a computer through brain-computer interfaces (BCIs). These devices operate by recording signals from the brain and translating these signals into device commands. They can be used by people who are severely paralyzed to communicate without any use of muscle activity. One of the major impediments in translating this novel technology into clinical applications is the current requirement for preliminary analyses to identify the brain signal features best suited for communication. This paper introduces and validates signal detection, which does not require such analysis procedures, as a new concept in BCI signal processing. This detection concept is realized with Gaussian mixture models (GMMs) that are used to model resting brain activity so that any change in relevant brain signals can be detected. It is implemented in a package called SIGFRIED (SIGnal modeling For Real-time Identification and Event Detection). The results indicate that SIGFRIED produces results that are within the range of those achieved using a common analysis strategy that requires preliminary identification of signal features. They indicate that such laborious analysis procedures could be replaced by merely recording brain signals during rest. In summary, this paper demonstrates how SIGFRIED could be used to overcome one of the present impediments to translation of laboratory BCI demonstrations into clinically practical applications.},
			author = {Schalk, G and Brunner, P and Gerhardt, L a and Bischof, H and Wolpaw, J R},
			doi = {10.1016/j.jneumeth.2007.08.010},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Schalk et al. - 2008 - Brain-computer interfaces (BCIs) detection instead of classification.pdf:pdf},
			issn = {0165-0270},
			journal = {Journal of neuroscience methods},
			keywords = {Adult,Algorithms,Brain,Brain Mapping,Brain: physiology,Electrocardiography,Electrocardiography: methods,Electroencephalography,Electroencephalography: methods,Humans,Male,Man-Machine Systems,Normal Distribution,Online Systems,Signal Detection, Psychological,Signal Detection, Psychological: physiology,Signal Processing, Computer-Assisted,Software Validation,User-Computer Interface},
			month = {jan},
			number = {1},
			pages = {51--62},
			pmid = {17920134},
			title = {{Brain-computer interfaces (BCIs): detection instead of classification.}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/17920134},
			volume = {167},
			year = {2008}
			}

			@article{Bensafi2012,
			abstract = {Functional neuroimaging studies of the olfactory system during the last two decades have studied the role of the piriform cortex (PC) in human olfaction. Whereas some reported absence of PC activation, most suggested that the PC is more than a relay and plays an active role from sensory to more cognitive aspects of human olfactory perception. The aim of the present review is to clarify our current understanding of the role of the PC in human odor perception, by underscoring the sensory and cognitive factors which are important in piriform activity modulation.},
			author = {Bensafi, Moustafa},
			doi = {10.1007/s12078-011-9110-8},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/b548a4037c6e64c70963eaac346cf9a0fb8d29a3.pdf:pdf},
			isbn = {1936-5802},
			issn = {19365802},
			journal = {Chemosensory Perception},
			keywords = {Cognition,Human olfaction,Intensity,Memory,PET,Piriform cortex,Pleasantness,fMRI},
			number = {1},
			pages = {4--10},
			title = {{The role of the piriform cortex in human olfactory perception: Insights from functional neuroimaging studies}},
			url = {http://download.springer.com/static/pdf/265/art{\%}253A10.1007{\%}252Fs12078-011-9110-8.pdf?originUrl=http{\%}3A{\%}2F{\%}2Flink.springer.com{\%}2Farticle{\%}2F10.1007{\%}2Fs12078-011-9110-8{\&}token2=exp=1495577197{~}acl={\%}2Fstatic{\%}2Fpdf{\%}2F265{\%}2Fart{\%}25253A10.1007{\%}25252Fs12078-011-911},
			volume = {5},
			year = {2012}
			}

			@article{Ching2013,
			abstract = {BACKGROUND: A medically induced coma is an anesthetic state of profound brain inactivation created to treat status epilepticus and to provide cerebral protection after traumatic brain injuries. The authors hypothesized that a closed-loop anesthetic delivery system could automatically and precisely control the electroencephalogram state of burst suppression and efficiently maintain a medically induced coma.$\backslash$n$\backslash$nMETHODS: In six rats, the authors implemented a closed-loop anesthetic delivery system for propofol consisting of: a computer-controlled pump infusion, a two-compartment pharmacokinetics model defining propofol's electroencephalogram effects, the burst-suppression probability algorithm to compute in real time from the electroencephalogram the brain's burst-suppression state, an online parameter-estimation procedure and a proportional-integral controller. In the control experiment each rat was randomly assigned to one of the six burst-suppression probability target trajectories constructed by permuting the burst-suppression probability levels of 0.4, 0.65, and 0.9 with linear transitions between levels.$\backslash$n$\backslash$nRESULTS: In each animal the controller maintained approximately 60 min of tight, real-time control of burst suppression by tracking each burst-suppression probability target level for 15 min and two between-level transitions for 5-10 min. The posterior probability that the closed-loop anesthetic delivery system was reliable across all levels was 0.94 (95{\%} CI, 0.77-1.00; n = 18) and that the system was accurate across all levels was 1.00 (95{\%} CI, 0.84-1.00; n = 18).$\backslash$n$\backslash$nCONCLUSION: The findings of this study establish the feasibility of using a closed-loop anesthetic delivery systems to achieve in real time reliable and accurate control of burst suppression in rodents and suggest a paradigm to precisely control medically induced coma in patients.},
			archivePrefix = {arXiv},
			arxivId = {NIHMS150003},
			author = {Ching, ShiNung and Liberman, Max Y and Chemali, Jessica J and Westover, M Brandon and Kenny, Jonathan D and Solt, Ken and Purdon, Patrick L and Brown, Emery N},
			doi = {10.1097/ALN.0b013e31829d4ab4},
			eprint = {NIHMS150003},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Ching et al. - 2013 - Real-time closed-loop control in a rodent model of medically induced coma using burst suppression.pdf:pdf},
			isbn = {2122633255},
			issn = {1528-1175},
			journal = {Anesthesiology},
			keywords = {Anesthesia, Intravenous,Anesthesia, Intravenous: instrumentation,Anesthesia, Intravenous: methods,Anesthetics, Intravenous,Anesthetics, Intravenous: administration {\&} dosage,Animals,Brain,Brain: drug effects,Coma,Coma: chemically induced,Disease Models, Animal,Drug Delivery Systems,Electroencephalography,Electroencephalography: drug effects,Electroencephalography: methods,Feasibility Studies,Infusions, Intravenous,Infusions, Intravenous: instrumentation,Infusions, Intravenous: methods,Male,Propofol,Propofol: administration {\&} dosage,Rats,Rats, Sprague-Dawley,Reproducibility of Results},
			number = {4},
			pages = {848--60},
			pmid = {23770601},
			title = {{Real-time closed-loop control in a rodent model of medically induced coma using burst suppression.}},
			url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3857134{\&}tool=pmcentrez{\&}rendertype=abstract},
			volume = {119},
			year = {2013}
			}

			@article{Kindermans2014,
			abstract = {Despite several approaches to realize subject-to-subject transfer of pre-trained classifiers, the full performance of a Brain-Computer Interface (BCI) for a novel user can only be reached by presenting the BCI system with data from the novel user. In typical state-of-the-art BCI systems with a supervised classifier, the labeled data is collected during a calibration recording, in which the user is asked to perform a specific task. Based on the known labels of this recording, the BCI's classifier can learn to decode the individual's brain signals. Unfortunately, this calibration recording consumes valuable time. Furthermore, it is unproductive with respect to the final BCI application, e.g. text entry. Therefore, the calibration period must be reduced to a minimum, which is especially important for patients with a limited concentration ability. The main contribution of this manuscript is an online study on unsupervised learning in an auditory event-related potential (ERP) paradigm. Our results demonstrate that the calibration recording can be bypassed by utilizing an unsupervised trained classifier, that is initialized randomly and updated during usage. Initially, the unsupervised classifier tends to make decoding mistakes, as the classifier might not have seen enough data to build a reliable model. Using a constant re-analysis of the previously spelled symbols, these initially misspelled symbols can be rectified posthoc when the classifier has learned to decode the signals. We compare the spelling performance of our unsupervised approach and of the unsupervised posthoc approach to the standard supervised calibration-based dogma for nâ=â10 healthy users. To assess the learning behavior of our approach, it is unsupervised trained from scratch three times per user. Even with the relatively low SNR of an auditory ERP paradigm, the results show that after a limited number of trials (30 trials), the unsupervised approach performs comparably to a classic supervised model.},
			author = {Kindermans, Pieter-Jan and Schreuder, Martijn and Schrauwen, Benjamin and M{\"{u}}ller, Klaus-Robert and Tangermann, Michael},
			doi = {10.1371/journal.pone.0102504},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Kindermans et al. - 2014 - True zero-training brain-computer interfacing - an online study.pdf:pdf},
			issn = {1932-6203},
			journal = {PloS one},
			month = {jan},
			number = {7},
			pages = {e102504},
			pmid = {25068464},
			title = {{True zero-training brain-computer interfacing - an online study.}},
			url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4113217{\&}tool=pmcentrez{\&}rendertype=abstract},
			volume = {9},
			year = {2014}
			}

			@article{Bie2010,
			author = {Bie, Henrica M A De and Boersma, Maria and Wattjes, Mike P and Adriaanse, Sofie and Waal, Henriette A Delemarre-van De},
			doi = {10.1007/s00431-010-1181-z},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/2eafe0503d387f931a8b684872b5d179c0de39cc.pdf:pdf},
			isbn = {0043101011},
			journal = {European Journal of Pediatrics},
			keywords = {functional mri,mock scanner,mri},
			pages = {1079--1085},
			title = {{Preparing children with a mock scanner training protocol results in high quality structural and functional MRI scans}},
			url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2908445/pdf/431{\_}2010{\_}Article{\_}1181.pdf},
			volume = {169},
			year = {2010}
			}

			@article{Barachant2013,
			author = {Barachant, Alexandre and Andreev, Anton and Congedo, Marco and Riemannian, The and Barachant, A and Andreev, A and Congedo, M},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Barachant et al. - 2013 - The Riemannian Potato an automatic and adaptive artifact detection method for online experiments using Rieman.pdf:pdf},
			keywords = {artifact detection,bci,eeg,riemannian geometry},
			title = {{The Riemannian Potato : an automatic and adaptive artifact detection method for online experiments using Riemannian geometry To cite this version : The Riemannian Potato : an automatic and adaptive artifact detection method for online experiments using Ri}},
			url = {https://hal.archives-ouvertes.fr/hal-00781701/document},
			year = {2013}
			}

			@article{Harmony2013,
			abstract = {Ample evidence suggests that electroencephalographic (EEG) oscillatory activity is linked to a broad variety of perceptual, sensorimotor, and cognitive operations. However, few studies have investigated the delta band (0.5-3.5 Hz) during different cognitive processes. The aim of this review is to present data and propose the hypothesis that sustained delta oscillations inhibit interferences that may affect the performance of mental tasks, possibly by modulating the activity of those networks that should be inactive to accomplish the task. It is clear that two functionally distinct and potentially competing brain networks can be broadly distinguished by their contrasting roles in attention to the external world vs. the internally directed mentation or concentration. During concentration, EEG delta (1-3.5 Hz) activity increases mainly in frontal leads in different tasks: mental calculation, semantic tasks, and the Sternberg paradigm. This last task is considered a working memory task, but in neural, as well as phenomenological, terms, working memory can be best understood as attention focused on an internal representation. In the Sternberg task, increases in power in the frequencies from 1 to 3.90 Hz in frontal regions are reported. In a Go/No-Go task, power increases at 1 Hz in both conditions were observed during 100-300 ms in central, parietal and temporal regions. However, in the No-Go condition, power increases were also observed in frontal regions, suggesting its participation in the inhibition of the motor response. Increases in delta power were also reported during semantic tasks in children. In conclusion, the results suggest that power increases of delta frequencies during mental tasks are associated with functional cortical deafferentation, or inhibition of the sensory afferences that interfere with internal concentration. These inhibitory oscillations would modulate the activity of those networks that should be inactive to accomplish the task.},
			author = {Harmony, Thal{\'{i}}a},
			doi = {10.3389/fnint.2013.00083},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Harmony - 2013 - The functional significance of delta oscillations in cognitive processing.pdf:pdf},
			issn = {1662-5145},
			journal = {Frontiers in integrative neuroscience},
			keywords = {EEG delta, attention, working memory, frontal lobe,attention,cognition,eeg delta,frontal lobes,inhibition,working memory},
			month = {jan},
			number = {December},
			pages = {83},
			pmid = {24367301},
			title = {{The functional significance of delta oscillations in cognitive processing.}},
			url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3851789{\&}tool=pmcentrez{\&}rendertype=abstract},
			volume = {7},
			year = {2013}
			}

			@article{Di2016,
			abstract = {Psychophysiological interaction (PPI) is a widely used regression-based method to study connectivity changes in different experimental conditions. A PPI effect is generated by point-by-point multiplication of a psychological variable (experimental design) and a physiological variable (time series of a seed region). If the psychological variable is non-centered with a constant component, the constant component will add a physiological variable to the PPI term. The physiological component would in theory be accounted for by the physiological main effect in the model. But due to imperfect deconvolution and convolution with hemodynamic response function, the physiological component in PPI may no longer be exactly the same as the physiological main effect. This issue was illustrated by analyzing two block-designed fMRI datasets, one simple visual checkerboard task and a set of different tasks designed to activate different hemispheres. When PPI was calculated with deconvolution but without centering, significant results were usually observed between regions that are known to have baseline functional connectivity. These results could be suppressed by simply centering the psychological variable when calculating the PPI term or adding a deconvolveâreconvolved version of the physiological covariate. The PPI results with centering and with deconvolveâreconvolved physiological covariate are consistent with an explicit test for differences in coupling between conditions. It was, therefore, suggested that centering of the psychological variable or the addition of a deconvolveâ reconvolved covariate is necessary for PPI analysis.},
			author = {Di, Xin and Reynolds, Richard C and Biswal, Bharat B},
			doi = {10.1002/hbm.23413},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/1b6f1095a08a7c4b70642d0c18207f676a171fa7.pdf:pdf},
			issn = {10659471},
			journal = {Human brain mapping},
			keywords = {connectivity,fmri,mean centering,psychophysiological interactions},
			pages = {1--18},
			pmid = {28105655},
			title = {{Imperfect ( De ) Convolution May Introduce Spurious Psychophysiological Interactions and How to Avoid It}},
			url = {https://s3.amazonaws.com/objects.readcube.com/articles/downloaded/wiley/411c171e3f0d96e5ec7a84e6eb255978330faf458d07518bb80938f844eda225.pdf?X-Amz-Algorithm=AWS4-HMAC-SHA256{\&}X-Amz-Credential=AKIAIS5LBPCM5JPOCDGQ{\%}2F20170515{\%}2Fus-east-1{\%}2Fs3{\%}2Faws4{\_}request{\&}},
			volume = {00000},
			year = {2016}
			}

			@article{Wolpaw2002,
			author = {Wolpaw, Jonathan R and Birbaumer, Niels and McFarland, Dennis J and Pfurtscheller, Gert and Vaughan, Theresa M},
			doi = {10.1016/S1388-2457(02)00057-3},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Wolpaw et al. - 2002 - Brainâcomputer interfaces for communication and control.pdf:pdf},
			issn = {13882457},
			journal = {Clinical Neurophysiology},
			keywords = {- see front matter,02,1 1-518-473-3631,1 1-518-486-4910,1388-2457,all rights reserved,augmentative communication,brain,computer interface,corresponding author,e-mail address,electroencephalography,fax,ireland ltd,j,machine interface,neuroprosthesis,org,q 2002 elsevier science,r,rehabilitation,tel,wadsworth,wolpaw},
			month = {jun},
			number = {6},
			pages = {767--791},
			title = {{Brainâcomputer interfaces for communication and control}},
			url = {http://linkinghub.elsevier.com/retrieve/pii/S1388245702000573},
			volume = {113},
			year = {2002}
			}

			@article{Diaz2007,
			author = {Diaz, B Alexander and Sloot, Lizeth H and Mansvelder, Huibert D and Linkenkaer-hansen, Klaus},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Diaz et al. - 2007 - EEG-Biofeedback as a Tool to Modulate Arousal Trends and Perspectives for Treatment of ADHD and Insomnia.pdf:pdf},
			title = {{EEG-Biofeedback as a Tool to Modulate Arousal : Trends and Perspectives for Treatment of ADHD and Insomnia}},
			year = {2007}
			}

			@article{Muschelli2014,
			author = {Muschelli, John and Beth, Mary and Caffo, Brian S and Barber, Anita D and Pekar, James J and Mostofsky, Stewart H},
			doi = {10.1016/j.neuroimage.2014.03.028},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/684365437dedad66afd6cbbd8ad506fa3dc81d63.pdf:pdf},
			issn = {1053-8119},
			journal = {NeuroImage},
			keywords = {Head motion,Nuisance regression,Resting state fMRI,Specificity},
			pages = {22--35},
			publisher = {Elsevier Inc.},
			title = {{NeuroImage Reduction of motion-related artifacts in resting state fMRI using aCompCor}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2014.03.028 http://ac.els-cdn.com/S105381191400175X/1-s2.0-S105381191400175X-main.pdf?{\_}tid=d7a2b54e-e3ce-11e6-bb17-00000aacb361{\&}acdnat=1485439000{\_}6262734b96072a1a1934fe8e2ebcb128},
			volume = {96},
			year = {2014}
			}

			@article{Tagliazucchi2016,
			abstract = {Large efforts are currently under way to systematically map functional connectivity between all pairs of millimeter-scale brain regions based on large neuroimaging databases. The exploratory unraveling of this " functional connectome " based on functional Magnetic Resonance Imaging (fMRI) can benefit from a better understanding of the contributors to resting state functional connectivity. In this work, we introduce a sparse representation of fMRI data in the form of a discrete point-process encoding high-amplitude events in the blood oxygenation level-dependent (BOLD) signal and we show it contains sufficient information for the estimation of functional connectivity between all pairs of voxels. We validate this method by replicating results obtained with standard whole-brain voxel-wise linear correlation matrices in two datasets. In the first one (n = 71), we study the changes in node strength (a measure of network centrality) during deep sleep. The second is a large database (n = 1147) of subjects in which we look at the age-related reorganization of the voxel-wise network of functional connections. In both cases it is shown that the proposed method compares well with standard techniques, despite requiring only data on the order of 1{\%} of the original BOLD signal time series. Furthermore, we establish that the point-process approach does not reduce (and in one case increases) classification accuracy compared to standard linear correlations. Our results show how large fMRI datasets can be drastically simplified to include only the timings of large-amplitude events, while still allowing the recovery of all pair-wise interactions between voxels. The practical importance of this dimensionality reduction is manifest in the increasing number of collaborative efforts aiming to study large cohorts of healthy subjects as well as patients suffering from brain disease. Our method also suggests that the electrophysiological signals underlying the dynamics of fMRI time series consist of all-or-none temporally localized events, analogous to the avalanches of neural activity observed in recordings of local field potentials (LFP), an observation of potentially high neurobiological relevance.},
			author = {Tagliazucchi, Enzo and Siniatchkin, Michael and Laufs, Helmut and Chialvo, Dante R.},
			doi = {10.3389/fnins.2016.00381},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/87733f9d5119af221c814ee81d5a34ba7e5e0e07.pdf:pdf},
			issn = {1662453X},
			journal = {Frontiers in Neuroscience},
			keywords = {Dimensionality reduction,Functional connectivity,Functional connectome,Point process,Resting state fMRI},
			number = {AUG},
			pages = {1--13},
			pmid = {27601975},
			title = {{The voxel-wise functional connectome can be efficiently derived from co-activations in a sparse spatio-temporal point-process}},
			url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4994538/pdf/fnins-10-00381.pdf},
			volume = {10},
			year = {2016}
			}

			@article{Smith2015,
			abstract = {The Human Connectome Project (HCP) 1 is acquiring high-quality in vivo macroscopic-level connectome imaging data from over a thousand healthy adult subjects in an effort to elucidate the neural pathways and networks that underlie brain function and behavior. An overarching aim is to reveal much about what makes us uniquely human and what makes individuals different from each other by understanding how brain networks integrate information through the complex pattern of neural connections. To date, data sets from 500 subjects have been publicly released, including imaging data measuring functional and structural brain connectivity, as well as 280 non-imaging subject measures (SMs), including demographics (age, sex, income, education level, drug use, etc.), psychometrics (IQ, language performance, etc.) and other behavioral measures such as 'rule-breaking behavior' . We sought to relate functional connectomes to behavior in a single integrated analysis. This goes further than simply investigating which SMs correlate with other SMs; we wanted to discover whether any specific patterns of brain connectivity are associated with specific sets of correlated demographics and behavior, as brain-behavior modes of population co-variation. We used resting-state functional magnetic resonance imaging (fMRI) data from 461 HCP subjects, and network modeling tools from FSL (FMRIB Software Library). A population-average brain parcellation was estimated using independent component analysis 2 , yielding 200 distinct brain regions; these constitute the nodes in our network modeling. The functional connections (edges) between these nodes were estimated using Tikhonov-regularized partial correlation, resulting in a 200 Ã 200 connectome for each subject. These connec-tomes were combined into a single large connectome matrix (con-taining all connectomes from all subjects; Supplementary Fig. 1). Separately, 158 behavioral and demographic non-imaging SMs from the same set of subjects were formed into a subject measure matrix. We regressed potential confounds (including brain size and head motion) out of both matrices. Redundancies among connectomes and SMs were reduced by (separately) keeping just the first 100 principal components of each matrix. A natural choice of method for investigating underlying relation-ships between two sets of variables is canonical correlation analysis (CCA) 3 , a procedure that seeks maximal correlations between com-binations of variables in both sets. Using CCA, we estimated pairs of canonical variates along which sets of SMs and patterns of brain con-nectivity co-vary in a similar way across subjects. We refer to each such pair of variates as a mode of co-variation. Strict tests were applied to avoid over-fitting and false-positive inflation. Statistical significance was determined with a permutation test that accounted for the fam-ily structure of the HCP data 4 . This analysis revealed a single highly significant CCA mode that relates functional connectomes to subject measures (r = 0.87, P {\textless} 10 â5 corrected for multiple comparisons across all modes estimated). These analyses were driven by and report only correlations; inferring and interpreting the (presumably complex and diverse) causalities remains a challenging issue for the future.},
			author = {Smith, Stephen M and Nichols, Thomas E and Vidaurre, Diego and Winkler, Anderson M and Behrens, Timothy E J and Glasser, Matthew F and Ugurbil, Kamil and Barch, Deanna M and {Van Essen}, David C and Miller, Karla L},
			doi = {10.1038/nn.4125},
			file = {:Users/lorenafreitas/Downloads/Articles for Journal Club-20170517/Functional{\_}Demographics{\_}CCA.pdf:pdf},
			isbn = {1546-1726 (Electronic)$\backslash$r1097-6256 (Linking)},
			issn = {1097-6256},
			journal = {Nature Neuroscience},
			number = {11},
			pages = {1565--1567},
			pmid = {26414616},
			publisher = {Nature Publishing Group},
			title = {{A positive-negative mode of population covariation links brain connectivity, demographics and behavior}},
			url = {http://www.nature.com/doifinder/10.1038/nn.4125},
			volume = {18},
			year = {2015}
			}

			@article{Biswal1995b,
			abstract = {- An MRI time course of 512 echo-planar images (EPI) in resting human brain},
			author = {Biswal, Bharat and {Zerrin Yetkin}, F. and Haughton, Victor M. and Hyde, James S.},
			doi = {10.1002/mrm.1910340409},
			file = {:Users/lorenafreitas/Downloads/01-NIH-biswal{\_}mrm1995.pdf:pdf},
			isbn = {0740-3194 (Print)$\backslash$r0740-3194 (Linking)},
			issn = {15222594},
			journal = {Magnetic Resonance in Medicine},
			keywords = {EPI,fMRI,functional connectivity,motor cortex},
			number = {4},
			pages = {537--541},
			pmid = {8524021},
			title = {{Functional connectivity in the motor cortex of resting human brain using echo-planar mri}},
			volume = {34},
			year = {1995}
			}

			@article{Troyk2012,
			abstract = {This paper reports on a wireless stimulator device for use in animal experiments as part of an ongoing investigation into intraspinal stimulation (ISMS) for restoration of walking in humans with spinal cord injury. The principle behind using ISMS is the activation of residual motor-control neural networks within the spinal cord ventral horn below the level of lesion following a spinal cord injury. The attractiveness to this technique is that a small number of electrodes can be used to induce bilateral walking patterns in the lower limbs. In combination with advanced feedback algorithms, ISMS has the potential to restore walking for distances that exceed that produced by other types of functional electrical stimulation. Recent acute animal experiments have demonstrated the feasibility of using ISMS to produce the coordinated walking patterns. Here we described a wireless implantable stimulation system to be used in chronic animal experiments and for providing the basis for a system suitable for use in humans. Electrical operation of the wireless system is described, including a demonstration of reverse telemetry for monitoring the stimulating electrode voltages.},
			author = {Troyk, Philip R and Mushahwar, Vivian K and Stein, Richard B and Suh, Sungjae and Everaert, Dirk and Holinski, Brad and Hu, Zhe and DeMichele, Glenn and Kerns, Douglas and Kayvani, Kevin},
			doi = {10.1109/EMBC.2012.6346077},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Troyk et al. - 2012 - An implantable neural stimulator for intraspinal microstimulation.pdf:pdf},
			issn = {1557-170X},
			journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
			keywords = {Algorithms,Animals,Cats,Electric Stimulation Therapy,Electric Stimulation Therapy: instrumentation,Electric Stimulation Therapy: methods,Electrodes,Humans,Implanted,Lower Extremity,Lower Extremity: physiopathology,Motor Neurons,Motor Neurons: pathology,Nerve Net,Nerve Net: pathology,Nerve Net: physiopathology,Spinal Cord,Spinal Cord Injuries,Spinal Cord Injuries: pathology,Spinal Cord Injuries: physiopathology,Spinal Cord Injuries: therapy,Wireless Technology,Wireless Technology: instrumentation},
			month = {jan},
			pages = {900--3},
			pmid = {23366038},
			title = {{An implantable neural stimulator for intraspinal microstimulation.}},
			url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3795508{\&}tool=pmcentrez{\&}rendertype=abstract},
			volume = {2012},
			year = {2012}
			}

			@techreport{Sthapit2014,
			abstract = {This study investigates alternatives to the common gel-based systems in BCI. There are two considered approaches namely water-based and dry electrode sys- tems, where this study examines the usability and signal quality of a water-based cap. This is done with five probands on the basis of two ERP spellers. It is shown that the user comfort and cap setup of this system is practical, whereas the signal quality has a high inter-subject variance ranging from fine to unusable data.},
			address = {Berlin},
			author = {Sthapit, Shristina and Mongalgi, Nabil and Neeb, Moritz},
			institution = {Technische Universit{\"{a}}t},
			title = {{Evaluation of the TMSi water-based cap}},
			year = {2014}
			}

			@article{Ejaz2015,
			abstract = {Nature Neuroscience, (2015). doi:10.1038/nn.4038},
			author = {Ejaz, Naveed and Hamada, Masashi and Diedrichsen, J{\"{o}}rn},
			doi = {10.1038/nn.4038},
			file = {:Users/lorenafreitas/Downloads/Articles for Journal Club-20170517/Ejaz{\_}HandUse.pdf:pdf},
			isbn = {1546-1726 (Electronic)$\backslash$r1097-6256 (Linking)},
			issn = {1097-6256},
			journal = {Nature Neuroscience},
			number = {7},
			pages = {1034--1040},
			pmid = {26030847},
			title = {{Hand use predicts the structure of representations in sensorimotor cortex}},
			url = {http://www.nature.com/doifinder/10.1038/nn.4038},
			volume = {18},
			year = {2015}
			}

			@article{Garrison2013,
			abstract = {Neurophenomenological studies seek to utilize first-person self-report to elucidate cognitive processes related to physiological data. Grounded theory offers an approach to the qualitative analysis of self-report, whereby theoretical constructs are derived from empirical data. Here we used grounded theory methodology (GTM) to assess how the first-person experience of meditation relates to neural activity in a core region of the default mode network-the posterior cingulate cortex (PCC). We analyzed first-person data consisting of meditators' accounts of their subjective experience during runs of a real time fMRI neurofeedback study of meditation, and third-person data consisting of corresponding feedback graphs of PCC activity during the same runs. We found that for meditators, the subjective experiences of "undistracted awareness" such as "concentration" and "observing sensory experience," and "effortless doing" such as "observing sensory experience," "not efforting," and "contentment," correspond with PCC deactivation. Further, the subjective experiences of "distracted awareness" such as "distraction" and "interpreting," and "controlling" such as "efforting" and "discontentment," correspond with PCC activation. Moreover, we derived several novel hypotheses about how specific qualities of cognitive processes during meditation relate to PCC activity, such as the difference between meditation and "trying to meditate." These findings offer novel insights into the relationship between meditation and mind wandering or self-related thinking and neural activity in the default mode network, driven by first-person reports.},
			author = {Garrison, Kathleen a and Santoyo, Juan F and Davis, Jake H and Thornhill, Thomas a and Kerr, Catherine E and Brewer, Judson a},
			doi = {10.3389/fnhum.2013.00440},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Garrison et al. - 2013 - Effortless awareness using real time neurofeedback to investigate correlates of posterior cingulate cortex acti.pdf:pdf},
			issn = {1662-5161},
			journal = {Frontiers in human neuroscience},
			keywords = {grounded theory,introspection,meditation,neurophenomenology,neurophenomenology, grounded theory, real time fMR,posterior cingulate cortex,real time fmri,report,self-,self-referential processing},
			month = {jan},
			number = {August},
			pages = {440},
			pmid = {23964222},
			title = {{Effortless awareness: using real time neurofeedback to investigate correlates of posterior cingulate cortex activity in meditators' self-report.}},
			url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3734786{\&}tool=pmcentrez{\&}rendertype=abstract},
			volume = {7},
			year = {2013}
			}

			@article{Tangermann2012,
			abstract = {The BCI competition IV stands in the tradition of prior BCI competitions that aim to provide high quality neuroscientific data for open access to the scientific community. As experienced already in prior competitions not only scientists from the narrow field of BCI compete, but scholars with a broad variety of backgrounds and nationalities. They include high specialists as well as students. The goals of all BCI competitions have always been to challenge with respect to novel paradigms and complex data. We report on the following challenges: (1) asynchronous data, (2) synthetic, (3) multi-class continuous data, (4) session-to-session transfer, (5) directionally modulated MEG, (6) finger movements recorded by ECoG. As after past competitions, our hope is that winning entries may enhance the analysis methods of future BCIs.},
			author = {Tangermann, Michael and M{\"{u}}ller, Klaus-Robert and Aertsen, Ad and Birbaumer, Niels and Braun, Christoph and Brunner, Clemens and Leeb, Robert and Mehring, Carsten and Miller, Kai J and M{\"{u}}ller-Putz, Gernot R and Nolte, Guido and Pfurtscheller, Gert and Preissl, Hubert and Schalk, Gerwin and Schl{\"{o}}gl, Alois and Vidaurre, Carmen and Waldert, Stephan and Blankertz, Benjamin},
			doi = {10.3389/fnins.2012.00055},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Tangermann et al. - 2012 - Review of the BCI Competition IV.pdf:pdf},
			issn = {1662-453X},
			journal = {Frontiers in neuroscience},
			keywords = {bci,brain-computer interface,brain-computer interface, BCI, competition,competition},
			month = {jan},
			number = {July},
			pages = {55},
			pmid = {22811657},
			title = {{Review of the BCI Competition IV.}},
			url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3396284{\&}tool=pmcentrez{\&}rendertype=abstract},
			volume = {6},
			year = {2012}
			}

			@article{Fransson2011,
			abstract = {The functional network topology of the adult human brain has recently begun to be noninvasively mapped using resting-state functional connectivity magnetic resonance imaging and described using mathematical tools originating from graph theory. Previous studies have revealed the existence of disproportionally connected brain regions, so called cortical hubs, which act as information convergence zones and supposedly capture key aspects of how the brain's architecture supports human behavior and how it is affected by disease. In this study, we present results showing that cortical hubs and their associated cortical networks are largely confined to primary sensory and motor brain regions in the infant brain. Our findings in infants stand in stark contrast to the situation found in adults where the majority of cortical hubs and hub-related networks are located in heteromodal association cortex. Our findings suggest that the functional network architecture in infants is linked to support tasks that are of a perception-action nature.},
			author = {Fransson, Peter and {\AA}den, Ulrika and Blennow, Mats and Lagercrantz, Hugo},
			doi = {10.1093/cercor/bhq071},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/06f5b9af57a9b8b2c1dd54d52a8719229cd0ec04.pdf:pdf},
			isbn = {1047-3211},
			issn = {10473211},
			journal = {Cerebral Cortex},
			keywords = {Default mode network,Functional connectivity,Infant brain,Intrinsic activity,Resting state},
			number = {1},
			pages = {145--154},
			pmid = {20421249},
			title = {{The functional architecture of the infant brain as revealed by resting-state fMRI}},
			url = {https://oup.silverchair-cdn.com/oup/backfile/Content{\_}public/Journal/cercor/21/1/10.1093{\_}cercor{\_}bhq071/2/bhq071.pdf?Expires=1495560461{\&}Signature=IOoclQ7iFaxCeF1UWnHPxS8qvTQK7ofHWvFhNoVs1RFame1hQTRavy2GPLuw73Xq05{~}3Qe0{~}6N4lg4D{~}nar9RXhZl-Bu9RgQghyh90{~}cLNzPELH},
			volume = {21},
			year = {2011}
			}

			@article{Smith2018,
			author = {Smith, Stephen M. and Nichols, Thomas E.},
			doi = {10.1016/j.neuron.2017.12.018},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/baffd30a1959d3c457fbaabf11e228af42847c08.html:html},
			issn = {08966273},
			journal = {Neuron},
			month = {jan},
			number = {2},
			pages = {263--268},
			title = {{Statistical Challenges in âBig Dataâ Human Neuroimaging}},
			url = {https://reader.elsevier.com/reader/sd/pii/S0896627317311418?token=5C61F612A8ED61CA0BC09B04E0673C19835F20EA4C1F0CBB40684F056A21C5A8599FF3FF5EDFD68866AFD796789B5DED https://linkinghub.elsevier.com/retrieve/pii/S0896627317311418},
			volume = {97},
			year = {2018}
			}

			@article{FischiGomez2016,
			author = {Fischi-Gomez, Elda and Mu{\~{n}}oz-Moreno, Emma and Vasung, Lana and Griffa, Alessandra and Borradori-Tolsa, Cristina and Monnier, Maryline and Lazeyras, Fran{\c{c}}ois and Thiran, Jean-Philippe and H{\"{u}}ppi, Petra S.},
			doi = {10.1016/j.nicl.2016.02.001},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Fischi-Gomez et al. - 2016 - Brain network characterization of high-risk preterm-born school-age children.pdf:pdf},
			issn = {22131582},
			journal = {NeuroImage: Clinical},
			keywords = {Brain connectivity,Brain networks,Connectomics,Extreme prematurity,Human brain development,Intrauterine growth restriction,Social cognition},
			pages = {195--209},
			publisher = {The Authors},
			title = {{Brain network characterization of high-risk preterm-born school-age children}},
			url = {http://linkinghub.elsevier.com/retrieve/pii/S2213158216300237},
			volume = {11},
			year = {2016}
			}

			@article{Deco2015,
			abstract = {The brain regulates information flow by balancing the segregation and integration of incoming stimuli to facilitate flexible cognition and behaviour. The topological features of brain networks - in particular, network communities and hubs - support this segregation and integration but do not provide information about how external inputs are processed dynamically (that is, over time). Experiments in which the consequences of selective inputs on brain activity are controlled and traced with great precision could provide such information. However, such strategies have thus far had limited success. By contrast, recent whole-brain computational modelling approaches have enabled us to start assessing the effect of input perturbations on brain dynamics in silico.},
			author = {Deco, Gustavo and Tononi, Giulio and Boly, Melanie and Kringelbach, Morten L.},
			doi = {10.1038/nrn3963},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/dfcda130de8a8057e9ca0aef8a4a082cdf341b09.pdf:pdf},
			isbn = {1471-0048 (Electronic)$\backslash$r1471-003X (Linking)},
			issn = {1471-003X},
			journal = {Nature Reviews Neuroscience},
			number = {7},
			pages = {430--439},
			pmid = {26081790},
			publisher = {Nature Publishing Group},
			title = {{Rethinking segregation and integration: contributions of whole-brain modelling}},
			url = {http://www.nature.com/doifinder/10.1038/nrn3963},
			volume = {16},
			year = {2015}
			}

			@article{Friston2003,
			abstract = {In this paper we present an approach to the identification of nonlinear input-state-output systems. By using a bilinear approximation to the dynamics of interactions among states, the parameters of the implicit causal model reduce to three sets. These comprise (1) parameters that mediate the influence of extrinsic inputs on the states, (2) parameters that mediate intrinsic coupling among the states, and (3) [bilinear] parameters that allow the inputs to modulate that coupling. Identification proceeds in a Bayesian framework given known, deterministic inputs and the observed responses of the system. We developed this approach for the analysis of effective connectivity using experimentally designed inputs and fMRI responses. In this context, the coupling parameters correspond to effective connectivity and the bilinear parameters reflect the changes in connectivity induced by inputs. The ensuing framework allows one to characterise fMRI experiments, conceptually, as an experimental manipulation of integration among brain regions (by contextual or trial-free inputs, like time or attentional set) that is revealed using evoked responses (to perturbations or trial-bound inputs, like stimuli). As with previous analyses of effective connectivity, the focus is on experimentally induced changes in coupling (cf., psychophysiologic interactions). However, unlike previous approaches in neuroimaging, the causal model ascribes responses to designed deterministic inputs, as opposed to treating inputs as unknown and stochastic. {\textcopyright} 2003 Elsevier Science (USA). All rights reserved.},
			author = {Friston, K. J. and Harrison, L. and Penny, W.},
			doi = {10.1016/S1053-8119(03)00202-7},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/f671474dfb71e5a0119a39f5ebde4e2ff94549cc.pdf:pdf},
			isbn = {9780122648410},
			issn = {10538119},
			journal = {NeuroImage},
			keywords = {Bilinear model,Effective connectivity,Functional neuroimaging,Hemodynamic response function,Nonlinear system identification,fMRI},
			number = {4},
			pages = {1273--1302},
			pmid = {12948688},
			title = {{Dynamic causal modelling}},
			url = {http://www.fil.ion.ucl.ac.uk/{~}karl/Dynamic causal modelling.pdf},
			volume = {19},
			year = {2003}
			}

			@article{Lin2017,
			author = {Lin, Pan and Yang, Yong and Gao, Junfeng and Pisapia, Nicola De and Ge, Sheng and Wang, Xiang and Zuo, Chun S and Levitt, James Jonathan and Niu, Chen},
			doi = {10.1038/srep46088},
			file = {:Users/lorenafreitas/Downloads/srep46088.pdf:pdf},
			journal = {Nature Publishing Group},
			number = {October 2016},
			pages = {1--13},
			publisher = {Nature Publishing Group},
			title = {{Dynamic Default Mode Network across Different Brain States}},
			url = {http://dx.doi.org/10.1038/srep46088},
			year = {2017}
			}

			@article{Buxton2013,
			abstract = {Functional magnetic resonance imaging (fMRI) is a methodology for detecting dynamic patterns of activity in the working human brain. Although the initial discoveries that led to fMRI are only about 20 years old, this new field has revolutionized the study of brain function. The ability to detect changes in brain activity has a biophysical basis in the magnetic properties of deoxyhemoglobin, and a physiological basis in the way blood flow increases more than oxygen metabolism when local neural activity increases. These effects translate to a subtle increase in the local magnetic resonance signal, the blood oxygenation level dependent (BOLD) effect, when neural activity increases. With current techniques, this pattern of activation can be measured with resolution approaching 1Â mm(3) spatially and 1Â s temporally. This review focuses on the physical basis of the BOLD effect, the imaging methods used to measure it, the possible origins of the physiological effects that produce a mismatch of blood flow and oxygen metabolism during neural activation, and the mathematical models that have been developed to understand the measured signals. An overarching theme is the growing field of quantitative fMRI, in which other MRI methods are combined with BOLD methods and analyzed within a theoretical modeling framework to derive quantitative estimates of oxygen metabolism and other physiological variables. That goal is the current challenge for fMRI: to move fMRI from a mapping tool to a quantitative probe of brain physiology.},
			author = {Buxton, Richard B},
			doi = {10.1088/0034-4885/76/9/096601},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/80d84462fcd4cd2e3d711548a56bd00a90551a3e.pdf:pdf},
			isbn = {1361-6633 (Electronic)$\backslash$r0034-4885 (Linking)},
			issn = {1361-6633},
			journal = {Reports on progress in physics. Physical Society (Great Britain)},
			keywords = {Action Potentials,Action Potentials: physiology,Animals,Brain,Brain Mapping,Brain Mapping: methods,Brain: physiology,Cerebrovascular Circulation,Cerebrovascular Circulation: physiology,Computer Simulation,Humans,Image Interpretation, Computer-Assisted,Image Interpretation, Computer-Assisted: methods,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Models, Neurological,Oximetry,Oximetry: methods,Oxygen Consumption,Oxygen Consumption: physiology},
			number = {9},
			pages = {096601},
			pmid = {24006360},
			title = {{The physics of functional magnetic resonance imaging (fMRI).}},
			url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4376284{\&}tool=pmcentrez{\&}rendertype=abstract},
			volume = {76},
			year = {2013}
			}

			@article{Ogawa1992,
			author = {Ogawa, Seiji and Tank, David and Menon, Ravi and Ellermann, Jutta and Kim, Seong-Gi and Merkle, Hellmut and Ugurbil, Kamil},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/63c7503f92cfc01eedcecb283984726d4caac629.pdf:pdf},
			journal = {Proceedings of the National Academy of Sciences},
			number = {July},
			pages = {5951--5955},
			title = {{Intrinsic signal changes accompanying sensory stimulation: Functional brain mapping with magnetic resonance imaging}},
			url = {http://www.pnas.org/content/89/13/5951.full.pdf},
			volume = {89},
			year = {1992}
			}

			@article{Meskaldji2016,
			abstract = {Resting-state functional MRI (rs-fMRI) opens a window on large-scale organization of brain function. However, establishing relationships between resting-state brain activity and cognitive or clinical scores is still a difficult task, in particular in terms of prediction as would be meaningful for clinical applications such as early diagnosis of Alzheimer's disease. In this work, we employed partial least square regression under cross-validation scheme to predict episodic memory performance from functional connectivity (FC) patterns in a set of fifty-five MCI subjects for whom rs-fMRI acquisition and neuropsychological evaluation was carried out. We show that a newly introduced FC measure capturing the moments of anti-correlation between brain areas, discordance, contains key information to predict long-term memory scores in MCI patients, and performs better than standard measures of correlation to do so. Our results highlighted that stronger discordance within default mode network (DMN) areas, as well as across DMN, attentional and limbic networks, favor episodic memory performance in MCI.},
			author = {Meskaldji, Djalel Eddine and Preti, Maria Giulia and Bolton, Thomas AW and Montandon, Marie Louise and Rodriguez, Cristelle and Morgenthaler, Stephan and Giannakopoulos, Panteleimon and Haller, Sven and {Van De Ville}, Dimitri},
			doi = {10.1016/j.nicl.2016.10.004},
			file = {:Users/lorenafreitas/Downloads/1-s2.0-S2213158216301851-main.pdf:pdf},
			isbn = {2213-1582 (Electronic)2213-1582 (Linking)},
			issn = {22131582},
			journal = {NeuroImage: Clinical},
			keywords = {Cross-validation partial least square regression,Extreme value modeling,Functional brain connectivity,Long term memory,Medial temporal lobe,Mild cognitive impairment},
			pages = {785--795},
			pmid = {27812505},
			publisher = {The Authors},
			title = {{Prediction of long-term memory scores in MCI based on resting-state fMRI}},
			volume = {12},
			year = {2016}
			}

			@article{Hanslmayr2011,
			abstract = {Our brain does not process incoming sensory stimuli mechanistically. Instead the current brain state modulates our reaction to a stimulus. This modulation can be investigated by cognitive paradigms such as the attentional blink, which reveal that identical visual target stimuli are correctly reported only on about half the trials. Support for the notion that the fluctuating state of the brain determines stimulus detection comes from electrophysiological investigations of brain oscillations, which have shown that different parameters of ongoing oscillatory alpha activity ({\~{}}10 Hz) can predict whether a visual stimulus will be perceived or not. The present article reviews recent findings on the role of prestimulus alpha oscillatory activity for visual perception and incorporates these results into a neurocognitive model that is able to account for various findings in temporal attention paradigms, specifically the attentional blink.},
			author = {Hanslmayr, Simon and Gross, Joachim and Klimesch, Wolfgang and Shapiro, Kimron L},
			doi = {10.1016/j.brainresrev.2011.04.002},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Hanslmayr et al. - 2011 - The role of $\alpha$ oscillations in temporal attention.pdf:pdf},
			issn = {1872-6321},
			journal = {Brain research reviews},
			keywords = {Alpha Rhythm,Alpha Rhythm: physiology,Animals,Attention,Attention: physiology,Brain,Brain: physiology,Humans,Neural Pathways,Neural Pathways: physiology,Periodicity,Reaction Time,Reaction Time: physiology,Visual Perception,Visual Perception: physiology},
			month = {jun},
			number = {1-2},
			pages = {331--43},
			pmid = {21592583},
			publisher = {Elsevier B.V.},
			title = {{The role of $\alpha$ oscillations in temporal attention.}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/21592583},
			volume = {67},
			year = {2011}
			}

			@article{Haartsen2016,
			author = {Haartsen, Rianne and Jones, Emily J H and Johnson, Mark H},
			doi = {10.1016/j.cobeha.2016.05.015},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/6a92d3c3f67c4272c594042775ea1c05a6a51241.pdf:pdf},
			issn = {2352-1546},
			journal = {Current Opinion in Behavioral Sciences},
			pages = {149--154},
			publisher = {Elsevier Ltd},
			title = {{Human brain development over the early years}},
			url = {http://dx.doi.org/10.1016/j.cobeha.2016.05.015 http://ac.els-cdn.com/S2352154616301164/1-s2.0-S2352154616301164-main.pdf?{\_}tid=1838b6a4-3586-11e7-989a-00000aacb360{\&}acdnat=1494423750{\_}9a354f7c359dc9efa9b20baa62afb460},
			volume = {10},
			year = {2016}
			}

			@article{Laufs2003b,
			abstract = {Electroencephalography-correlated functional magnetic resonance imaging (EEG/fMRI) can be used to identify blood oxygen level-dependent (BOLD) signal changes associated with both physiological and pathological EEG events. Here, we implemented continuous and simultaneous EEG/fMRI to identify BOLD signal changes related to spontaneous power fluctuations in the alpha rhythm (8-12 Hz), the dominant EEG pattern during relaxed wakefulness. Thirty-two channels of EEG were recorded in 10 subjects during eyes-closed rest inside a 1.5-T magnet resonance (MR) scanner using an MR-compatible EEG recording system. Functional scanning by echoplanar imaging covered almost the entire cerebrum every 4 s. Off-line MRI artifact subtraction software was applied to obtain continuous EEG data during fMRI acquisition. The average alpha power over 1-s epochs was derived at several electrode positions using a Fast Fourier Transform. The power time course was then convolved with a canonical hemodynamic response function, down-sampled, and used for statistical parametric mapping of associated signal changes in the image time series. At all electrode positions studied, a strong negative correlation of parietal and frontal cortical activity with alpha power was found. Conversely, only sparse and nonsystematic positive correlation was detected. The relevance of these findings is discussed in view of the current theories on the generation and significance of the alpha rhythm and the related functional neuroimaging findings. {\textcopyright} 2003 Elsevier Inc. All rights reserved.},
			author = {Laufs, H. and Kleinschmidt, A. and Beyerle, A. and Eger, E. and Salek-Haddadi, A. and Preibisch, C. and Krakow, K.},
			doi = {10.1016/S1053-8119(03)00286-6},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Laufs et al. - 2003 - EEG-correlated fMRI of human alpha activity.pdf:pdf},
			isbn = {1053-8119 (Print)$\backslash$n1053-8119 (Linking)},
			issn = {10538119},
			journal = {NeuroImage},
			number = {4},
			pages = {1463--1476},
			pmid = {12948703},
			title = {{EEG-correlated fMRI of human alpha activity}},
			volume = {19},
			year = {2003}
			}

			@article{Di2013b,
			author = {Di, Xin and Gohel, Suril and Kim, Eun H and Biswal, Bharat B},
			doi = {10.3389/fnhum.2013.00493},
			file = {:Users/lorenafreitas/Downloads/fnhum-07-00493.pdf:pdf},
			keywords = {brain network,coactivation,hub shift,meta-analy,meta-analysis,modularity,resting-state,small world,thalamus},
			number = {September},
			pages = {1--9},
			title = {{Task vs . rest â different network configurations between the coactivation and the resting-state brain networks}},
			volume = {7},
			year = {2013}
			}

			@article{Satterthwaite2013,
			abstract = {Several recent reports in large, independent samples have demonstrated the influence of motion artifact on resting-state functional connectivity MRI (rsfc-MRI). Standard rsfc-MRI preprocessing typically includes regression of confounding signals and band-pass filtering. However, substantial heterogeneity exists in how these techniques are implemented across studies, and no prior study has examined the effect of differing approaches for the control of motion-induced artifacts. To better understand how in-scanner head motion affects rsfc-MRI data, we describe the spatial, temporal, and spectral characteristics of motion artifacts in a sample of 348 adolescents. Analyses utilize a novel approach for describing head motion on a voxelwise basis. Next, we systematically evaluate the efficacy of a range of confound regression and filtering techniques for the control of motion-induced artifacts. Results reveal that the effectiveness of preprocessing procedures on the control of motion is heterogeneous, and that improved preprocessing provides a substantial benefit beyond typical procedures. These results demonstrate that the effect of motion on rsfc-MRI can be substantially attenuated through improved preprocessing procedures, but not completely removed. {\textcopyright} 2012 Elsevier Inc.},
			archivePrefix = {arXiv},
			arxivId = {NIHMS150003},
			author = {Satterthwaite, Theodore D. and Elliott, Mark A. and Gerraty, Raphael T. and Ruparel, Kosha and Loughead, James and Calkins, Monica E. and Eickhoff, Simon B. and Hakonarson, Hakon and Gur, Ruben C. and Gur, Raquel E. and Wolf, Daniel H.},
			doi = {10.1016/j.neuroimage.2012.08.052},
			eprint = {NIHMS150003},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/06be0ef0f215ef9b000c7237e3ca454b25f8f69f.pdf:pdf},
			isbn = {1095-9572 (Electronic)$\backslash$n1053-8119 (Linking)},
			issn = {10538119},
			journal = {NeuroImage},
			keywords = {Adolescence,Artifact,Connectivity,Connectome,Development,FMRI,Motion,Network,Resting-state,despike,despikin,motion,preprocessing},
			mendeley-tags = {despike,despikin,motion,preprocessing},
			number = {1},
			pages = {240--256},
			pmid = {22926292},
			publisher = {Elsevier Inc.},
			title = {{An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2012.08.052 https://ac.els-cdn.com/S1053811912008609/1-s2.0-S1053811912008609-main.pdf?{\_}tid=6eb8e364-10b9-11e8-a7ff-00000aab0f6b{\&}acdnat=1518525105{\_}f7e9a92d77f9084f6b5d4fee9fd3afcf},
			volume = {64},
			year = {2013}
			}

			@article{Chen2015,
			abstract = {Recently, fMRI researchers have begun to realize that the brain's intrinsic network patterns may undergo substantial changes during a single resting state (RS) scan. However, despite the growing interest in brain dynamics, metrics that can quantify the variability of network patterns are still quite limited. Here, we first introduce various quantification metrics based on the extension of co-activation pattern (CAP) analysis, a recently proposed point-process analysis that tracks state alternations at each individual time frame and relies on very few assumptions; then apply these proposed metrics to quantify changes of brain dynamics during a sustained 2-back working memory (WM) task compared to rest. We focus on the functional connectivity of two prominent RS networks, the default-mode network (DMN) and executive control network (ECN). We first demonstrate less variability of global Pearson correlations with respect to the two chosen networks using a sliding-window approach during WM task compared to rest; then we show that the macroscopic decrease in variations in correlations during a WM task is also well characterized by the combined effect of a reduced number of dominant CAPs, increased spatial consistency across CAPs, and increased fractional contributions of a few dominant CAPs. These CAP metrics may provide alternative and more straightforward quantitative means of characterizing brain network dynamics than time-windowed correlation analyses.},
			archivePrefix = {arXiv},
			arxivId = {15334406},
			author = {Chen, Jingyuan E. and Chang, Catie and Greicius, Michael D. and Glover, Gary H.},
			doi = {10.1016/j.neuroimage.2015.01.057},
			eprint = {15334406},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/80ba9593fa6583a59ea43d6e3c32cbfab26f663a.pdf:pdf},
			isbn = {1095-9572 (Electronic)$\backslash$r1053-8119 (Linking)},
			issn = {10959572},
			journal = {NeuroImage},
			keywords = {Brain dynamics,Co-activation patterns,Point process analysis,Resting state networks,Working memory},
			pages = {476--488},
			pmid = {25662866},
			publisher = {Elsevier Inc.},
			title = {{Introducing co-activation pattern metrics to quantify spontaneous brain network dynamics}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2015.01.057 https://ac.els-cdn.com/S105381191500083X/1-s2.0-S105381191500083X-main.pdf?{\_}tid=2c7b093c-4b4e-4d97-9bd9-190384ad259b{\&}acdnat=1544202702{\_}cedc69f04647a2cd94944990aff8eb19},
			volume = {111},
			year = {2015}
			}

			@article{Freeman1979,
			title={Centrality in Social Networks Conceptual Clarification in Hawaii Nets Conferences},
			author={Freeman, Linton C},
			journal={Social Networks. An International Journal of Structural Analysis Lausanne},
			volume={1},
			number={3},
			pages={215--239},
			year={1979}
			}

			@article{Smallwood2007,
			abstract = {Successful learning requires that individuals integrate information from the external environment with their own internal representations. In this article, we consider the role that mind wandering plays in education. Mind wandering represents a state of decoupled attention because, instead of processing information from the external environment, our attention is directed toward our own private thoughts and feelings. In principle, because mind wandering is a state of decoupled attention, it represents a fundamental breakdown in the individual's ability to attend (and therefore integrate) information from the external environment. We consider evidence that mind wandering impairs the encoding of information, leading to failures in building a propositional model of a sentence and, ultimately, impairing the building of a narrative model with sufficient detail to allow generating inferences. Next, because recognizing and correcting for mind wandering is a metacognitive skill, certain client groups, such as those suffering from dysphoria or attention deficit disorder, may be unable to correct for the deficits associated with mind wandering, and so may suffer greater negative consequences during education. Finally, we consider how to apply this research to educational settings.},
			author = {Smallwood, Jonathan and Fishman, Daniel J and Schooler, Jonathan W},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Smallwood, Fishman, Schooler - 2007 - Counting the cost of an absent mind mind wandering as an underrecognized influence on educational.pdf:pdf},
			issn = {1069-9384},
			journal = {Psychonomic bulletin {\&} review},
			keywords = {Achievement,Attention,Brain,Child,Cognition,Female,Humans,Male,Thinking},
			month = {apr},
			number = {2},
			pages = {230--6},
			pmid = {17694906},
			title = {{Counting the cost of an absent mind: mind wandering as an underrecognized influence on educational performance.}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/17694906},
			volume = {14},
			year = {2007}
			}

			@article{Diaz2013,
			abstract = {Resting-state neuroimaging is a dominant paradigm for studying brain function in health and disease. It is attractive for clinical research because of its simplicity for patients, straightforward standardization, and sensitivity to brain disorders. Importantly, non-sensory experiences like mind wandering may arise from ongoing brain activity. However, little is known about the link between ongoing brain activity and cognition, as phenotypes of resting-state cognition-and tools to quantify them-have been lacking. To facilitate rapid and structured measurements of resting-state cognition we developed a 50-item self-report survey, the Amsterdam Resting-State Questionnaire (ARSQ). Based on ARSQ data from 813 participants assessed after 5 min eyes-closed rest in their home, we identified seven dimensions of resting-state cognition using factor analysis: Discontinuity of Mind, Theory of Mind, Self, Planning, Sleepiness, Comfort, and Somatic Awareness. Further, we showed that the structure of cognition was similar during resting-state fMRI and EEG, and that the test-retest correlations were remarkably high for all dimensions. To explore whether inter-individual variation of resting-state cognition is related to health status, we correlated ARSQ-derived factor scores with psychometric scales measuring depression, anxiety, and sleep quality. Mental health correlated positively with Comfort and negatively with Discontinuity of Mind. Finally, we show that sleepiness may partially explain a resting-state EEG profile previously associated with Alzheimer's disease. These findings indicate that the ARSQ readily provides information about cognitive phenotypes and that it is a promising tool for research on the neural correlates of resting-state cognition in health and disease.},
			author = {Diaz, B Alexander and {Van Der Sluis}, Sophie and Moens, Sarah and Benjamins, Jeroen S and Migliorati, Filippo and Stoffers, Diederick and {Den Braber}, Anouk and Poil, Simon-Shlomo and Hardstone, Richard and {Van't Ent}, Dennis and Boomsma, Dorret I and {De Geus}, Eco and Mansvelder, Huibert D and {Van Someren}, Eus J W and Linkenkaer-Hansen, Klaus},
			doi = {10.3389/fnhum.2013.00446},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Diaz et al. - 2013 - The Amsterdam Resting-State Questionnaire reveals multiple phenotypes of resting-state cognition.pdf:pdf},
			issn = {1662-5161},
			journal = {Frontiers in human neuroscience},
			keywords = {consciousness,consciousness, EEG, introspection, mental health,,eeg,introspection,mental health,mind wandering},
			month = {jan},
			number = {August},
			pages = {446},
			pmid = {23964225},
			title = {{The Amsterdam Resting-State Questionnaire reveals multiple phenotypes of resting-state cognition.}},
			url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3737475{\&}tool=pmcentrez{\&}rendertype=abstract},
			volume = {7},
			year = {2013}
			}

			@article{Amico2014,
			abstract = {BACKGROUND: Recent studies have been shown that functional connectivity of cerebral areas is not a static phenomenon, but exhibits spontaneous fluctuations over time. There is evidence that fluctuating connectivity is an intrinsic phenomenon of brain dynamics that persists during anesthesia. Lately, point process analysis applied on functional data has revealed that much of the information regarding brain connectivity is contained in a fraction of critical time points of a resting state dataset. In the present study we want to extend this methodology for the investigation of resting state fMRI spatial pattern changes during propofol-induced modulation of consciousness, with the aim of extracting new insights on brain networks consciousness-dependent fluctuations.$\backslash$n$\backslash$nMETHODS: Resting-state fMRI volumes on 18 healthy subjects were acquired in four clinical states during propofol injection: wakefulness, sedation, unconsciousness, and recovery. The dataset was reduced to a spatio-temporal point process by selecting time points in the Posterior Cingulate Cortex (PCC) at which the signal is higher than a given threshold (i.e., BOLD intensity above 1 standard deviation). Spatial clustering on the PCC time frames extracted was then performed (number of clustersâ=â8), to obtain 8 different PCC co-activation patterns (CAPs) for each level of consciousness.$\backslash$n$\backslash$nRESULTS: The current analysis shows that the core of the PCC-CAPs throughout consciousness modulation seems to be preserved. Nonetheless, this methodology enables to differentiate region-specific propofol-induced reductions in PCC-CAPs, some of them already present in the functional connectivity literature (e.g., disconnections of the prefrontal cortex, thalamus, auditory cortex), some others new (e.g., reduced co-activation in motor cortex and visual area).$\backslash$n$\backslash$nCONCLUSION: In conclusion, our results indicate that the employed methodology can help in improving and refining the characterization of local functional changes in the brain associated to propofol-induced modulation of consciousness.},
			author = {Amico, Enrico and Gomez, Francisco and {Di Perri}, Carol and Vanhaudenhuyse, Audrey and Lesenfants, Damien and Boveroux, Pierre and Bonhomme, Vincent and Brichant, Jean Fran{\c{c}}ois and Marinazzo, Daniele and Laureys, Steven},
			doi = {10.1371/journal.pone.0100012},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/c71942043bea93fc53e88cf4b071c89ab4bd823e.pdf:pdf},
			isbn = {1932-6203 (Electronic)$\backslash$r1932-6203 (Linking)},
			issn = {19326203},
			journal = {PLoS ONE},
			number = {6},
			pages = {1--9},
			pmid = {24979748},
			title = {{Posterior cingulate cortex-related co-activation patterns: A resting state fMRI study in propofol-induced loss of consciousness}},
			url = {https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0100012{\&}type=printable},
			volume = {9},
			year = {2014}
			}

			@article{Cisler2014,
			abstract = {Many cognitive and clinical neuroscience research studies seek to determine how contextual factors modulate cognitive processes. In fMRI, hypotheses about how context modulates distributed patterns of information processing are often tested by comparing functional connectivity between neural regions A and B as a function of task conditions X and Y, which is termed context-modulated functional connectivity (FC). There exist two exploratory statistical approaches to testing context-modulated FC: the beta-series method and psychophysiological interaction (PPI) analysis methods. While these approaches are commonly used, their relative power for detecting context-modulated FC is unknown, especially with respect to real-world experimental parameters (e.g., number of stimulus repetitions, inter-trial-interval, stimulus duration). Here, we use simulations to compare power for detecting context-modulated FC between the standard PPI formulation (sPPI), generalized PPI formulation (gPPI), and beta series methods. Simulation results demonstrate that gPPI and beta series methods are generally more powerful than sPPI. Whether gPPI or beta series methods performed more powerfully depended on experiment parameters: block designs favor the gPPI, whereas the beta series method was more powerful for designs with more trial repetitions and it also retained more power under conditions of hemodynamic response function variability. On a real dataset of adolescent girls, the PPI methods appeared to have greater sensitivity in detecting task-modulated FC when using a block design and the beta series method appeared to have greater sensitivity when using an event-related design with many trial repetitions. Implications of these performance results are discussed. {\textcopyright} 2013 Elsevier Inc.},
			archivePrefix = {arXiv},
			arxivId = {NIHMS150003},
			author = {Cisler, Josh M. and Bush, Keith and Steele, J. Scott},
			doi = {10.1016/j.neuroimage.2013.09.018},
			eprint = {NIHMS150003},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/d9d0a73109ddaa9c426439e8a1a3f67f66391830.pdf:pdf},
			isbn = {1095-9572 (Electronic)$\backslash$r1053-8119 (Linking)},
			issn = {10959572},
			journal = {NeuroImage},
			keywords = {FMRI,Functional connectivity,Psychophysiological interaction analysis},
			pages = {1042--1052},
			pmid = {24055504},
			publisher = {Elsevier Inc.},
			title = {{A comparison of statistical methods for detecting context-modulated functional connectivity in fMRI}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2013.09.018 http://psychiatry.uams.edu/files/2012/03/A-Comparison-of-Statistical-Methods-for-Detecting-Context-Modulated-Functional-Connectivity-in-fMRI.pdf},
			volume = {84},
			year = {2014}
			}

			@article{Satterthwaite2013b,
			abstract = {Several independent studies have demonstrated that small amounts of in-scanner motion systematically bias estimates of resting-state functional connectivity. This confound is of particular importance for studies of neurodevelopment in youth because motion is strongly related to subject age during this period. Critically, the effects of motion on connectivity mimic major findings in neurodevelopmental research, specifically an age-related strengthening of distant connections and weakening of short-range connections. Here, in a sample of 780 subjects ages 8-22, we re-evaluate patterns of change in functional connectivity during adolescent development after rigorously controlling for the confounding influences of motion at both the subject and group levels. We find that motion artifact inflates both overall estimates of age-related change as well as specific distance-related changes in connectivity. When motion is more fully accounted for, the prevalence of age-related change as well as the strength of distance-related effects is substantially reduced. However, age-related changes remain highly significant. In contrast, motion artifact tends to obscure age-related changes in connectivity associated with segregation of functional brain modules; improved preprocessing techniques allow greater sensitivity to detect increased within-module connectivity occurring with development. Finally, we show that subject's age can still be accurately estimated from the multivariate pattern of functional connectivity even while controlling for motion. Taken together, these results indicate that while motion artifact has a marked and heterogeneous impact on estimates of connectivity change during adolescence, functional connectivity remains a valuable phenotype for the study of neurodevelopment. {\textcopyright} 2013 Elsevier Inc.},
			archivePrefix = {arXiv},
			arxivId = {NIHMS150003},
			author = {Satterthwaite, Theodore D. and Wolf, Daniel H. and Ruparel, Kosha and Erus, Guray and Elliott, Mark A. and Eickhoff, Simon B. and Gennatas, Efstathios D. and Jackson, Chad and Prabhakaran, Karthik and Smith, Alex and Hakonarson, Hakon and Verma, Ragini and Davatzikos, Christos and Gur, Raquel E. and Gur, Ruben C.},
			doi = {10.1016/j.neuroimage.2013.06.045},
			eprint = {NIHMS150003},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/e131f3b41ad390429c466b9d9932550abf0239e8.pdf:pdf},
			isbn = {1095-9572 (Electronic) 1053-8119 (Linking)},
			issn = {10538119},
			journal = {NeuroImage},
			keywords = {Adolescence,Connectivity,Connectome,Development,FMRI,Motion artifact,Network,Resting-state,motion,preprocessing},
			mendeley-tags = {motion,preprocessing},
			pages = {45--57},
			pmid = {23792981},
			publisher = {Elsevier Inc.},
			title = {{Heterogeneous impact of motion on fundamental patterns of developmental changes in functional connectivity during youth}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2013.06.045 https://ac.els-cdn.com/S1053811913006824/1-s2.0-S1053811913006824-main.pdf?{\_}tid=1bcc5834-10b9-11e8-ba08-00000aacb35e{\&}acdnat=1518524974{\_}c00e3131cdc79704583f6b0e88ac8e0f},
			volume = {83},
			year = {2013}
			}

			@article{Lancaster2012,
			abstract = {Behavioral categories of functional imaging experiments along with standardized brain coordinates of associated activations were used to develop a method to automate regional behavioral analysis of human brain images. Behavioral and coordinate data were taken from the BrainMap database (http://www.brainmap.org/), which documents over 20 years of published functional brain imaging studies. A brain region of interest (ROI) for behavioral analysis can be defined in functional images, anatomical images or brain atlases, if images are spatially normalized to MNI or Talairach standards. Results of behavioral analysis are presented for each of BrainMap's 51 behavioral sub-domains spanning five behavioral domains (Action, Cognition, Emotion, Interoception, and Perception). For each behavioral sub-domain the fraction of coordinates falling within the ROI was computed and compared with the fraction expected if coordinates for the behavior were not clustered, i.e., uniformly distributed. When the difference between these fractions is large behavioral association is indicated. A z-score â¥ 3.0 was used to designate statistically significant behavioral association. The left-right symmetry of {\~{}}100K activation foci was evaluated by hemisphere, lobe, and by behavioral sub-domain. Results highlighted the classic left-side dominance for language while asymmetry for most sub-domains ({\~{}}75{\%}) was not statistically significant. Use scenarios were presented for anatomical ROIs from the Harvard-Oxford cortical (HOC) brain atlas, functional ROIs from statistical parametric maps in a TMS-PET study, a task-based fMRI study, and ROIs from the ten "major representative" functional networks in a previously published resting state fMRI study. Statistically significant behavioral findings for these use scenarios were consistent with published behaviors for associated anatomical and functional regions.},
			author = {Lancaster, Jack L. and Laird, Angela R. and Eickhoff, Simon B. and Martinez, Michael J. and Fox, P. Mickle and Fox, Peter T.},
			doi = {10.3389/fninf.2012.00023},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Lancaster et al. - 2012 - Automated regional behavioral analysis for human brain images.pdf:pdf},
			issn = {1662-5196},
			journal = {Frontiers in Neuroinformatics},
			keywords = {BrainMap, Mango, behavior analysis, region of inte,behavior analysis,brain atlas,brainmap,fmri,ica,mango,region of interest,tms-pet},
			number = {August},
			pages = {1--12},
			pmid = {22973224},
			title = {{Automated regional behavioral analysis for human brain images}},
			volume = {6},
			year = {2012}
			}

			@article{Klimesch1998,
			abstract = {Induced alpha power (in a lower, intermediate and upper band) which is deprived from evoked electroencephalograph (EEG) activity was analyzed in an oddball task in which a warning signal (WS) preceded a target or non-target. The lower band, reflecting phasic alertness, desynchronizes only in response to the WS and target. The intermediate band, reflecting expectancy, desynchronizes about 1 s before a target or non-target appears. Upper alpha desynchronizes only after a target is presented and, thus, reflects the performance of the task which was to count the targets. Thus, only slower alpha frequencies reflect attentional demands such as alertness and expectancy.},
			author = {Klimesch, W and Doppelmayr, M and Russegger, H and Pachinger, T and Schwaiger, J},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Klimesch et al. - 1998 - Induced alpha band power changes in the human EEG and attention.pdf:pdf},
			issn = {0304-3940},
			journal = {Neuroscience letters},
			keywords = {Adult,Alpha Rhythm,Attention,Attention: physiology,Electroencephalography,Evoked Potentials,Female,Humans,Male,Reaction Time},
			month = {mar},
			number = {2},
			pages = {73--6},
			pmid = {9572588},
			title = {{Induced alpha band power changes in the human EEG and attention.}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/9572588},
			volume = {244},
			year = {1998}
			}

			@article{VanDenHeuvel2010,
			abstract = {Our brain is a network. It consists of spatially distributed, but functionally linked regions that continuously share information with each other. Interestingly, recent advances in the acquisition and analysis of functional neuroimaging data have catalyzed the exploration of functional connectivity in the human brain. Functional connectivity is defined as the temporal dependency of neuronal activation patterns of anatomically separated brain regions and in the past years an increasing body of neuroimaging studies has started to explore functional connectivity by measuring the level of co-activation of resting-state fMRI time-series between brain regions. These studies have revealed interesting new findings about the functional connections of specific brain regions and local networks, as well as important new insights in the overall organization of functional communication in the brain network. Here we present an overview of these new methods and discuss how they have led to new insights in core aspects of the human brain, providing an overview of these novel imaging techniques and their implication to neuroscience. We discuss the use of spontaneous resting-state fMRI in determining functional connectivity, discuss suggested origins of these signals, how functional connections tend to be related to structural connections in the brain network and how functional brain communication may form a key role in cognitive performance. Furthermore, we will discuss the upcoming field of examining functional connectivity patterns using graph theory, focusing on the overall organization of the functional brain network. Specifically, we will discuss the value of these new functional connectivity tools in examining believed connectivity diseases, like Alzheimer's disease, dementia, schizophrenia and multiple sclerosis. ?? 2010 Elsevier B.V.},
			author = {van den Heuvel, Martijn P. and {Hulshoff Pol}, Hilleke E.},
			doi = {10.1016/j.euroneuro.2010.03.008},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/ebbd2cfd1f98d77d7a16db4509c5aaee5ec6f63c.pdf:pdf},
			isbn = {1873-7862 (Electronic)$\backslash$r0924-977X (Linking)},
			issn = {0924977X},
			journal = {European Neuropsychopharmacology},
			keywords = {Anatomical connectivity,Complex systems,Complexity,DTI,Diffusion tensor imaging,FMRI,Functional brain networks,Functional connectivity,Graph analysis,Network,Network analysis,Resting-state connectivity,Resting-state fMRI,Review,White matter},
			number = {8},
			pages = {519--534},
			pmid = {20471808},
			publisher = {Elsevier B.V.},
			title = {{Exploring the brain network: A review on resting-state fMRI functional connectivity}},
			url = {http://dx.doi.org/10.1016/j.euroneuro.2010.03.008 http://ac.els-cdn.com/S0924977X10000684/1-s2.0-S0924977X10000684-main.pdf?{\_}tid=fdbdcb7a-2f0a-11e6-aa60-00000aab0f26{\&}acdnat=1465563673{\_}fb1979470c79785c2420817960d339e6},
			volume = {20},
			year = {2010}
			}

			@article{Greicius2008,
			title={Resting-state functional connectivity in neuropsychiatric disorders},
			author={Greicius, Michael},
			journal={Current opinion in neurology},
			volume={21},
			number={4},
			pages={424--430},
			year={2008},
			publisher={LWW}
			}

			@article{Goldenberg2015,
			author = {Goldenberg, Diane and Galv{\'{a}}n, Adriana},
			doi = {10.1016/j.dcn.2015.01.011},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/e6a99591ef01b96c6e7f6197c0eacf1bbb9bef3d.pdf:pdf},
			issn = {1878-9293},
			journal = {Accident Analysis and Prevention},
			pages = {155--164},
			publisher = {Elsevier Ltd},
			title = {{The use of functional and effective connectivity techniques to understand the developing brain}},
			url = {http://dx.doi.org/10.1016/j.dcn.2015.01.011 http://ac.els-cdn.com/S1878929315000250/1-s2.0-S1878929315000250-main.pdf?{\_}tid=3f4910f6-3584-11e7-bbaa-00000aacb360{\&}acdnat=1494422957{\_}3eb0ef20d7b5f37e7d99380a066e151a},
			volume = {12},
			year = {2015}
			}

			@article{Tong2012,
			author = {Tong, F and Pratte, MS},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/2cd84f436377d4424eacc1c682dafa4617f8bac1.pdf:pdf},
			journal = {Annual review of psychology},
			pages = {483--509},
			title = {{Decoding Patterns of Human Brain Activity}},
			url = {http://www.annualreviews.org/doi/pdf/10.1146/annurev-psych-120710-100412},
			volume = {63},
			year = {2012}
			}

			@article{Hutchison2013b,
			abstract = {The brain must dynamically integrate, coordinate, and respond to internal and external stimuli across multiple time scales. Non-invasive measurements of brain activity with fMRI have greatly advanced our understanding of the large-scale functional organization supporting these fundamental features of brain function. Conclusions from previous resting-state fMRI investigations were based upon static descriptions of functional connectivity (FC), and only recently studies have begun to capitalize on the wealth of information contained within the temporal features of spontaneous BOLD FC. Emerging evidence suggests that dynamic FC metrics may index changes in macroscopic neural activity patterns underlying critical aspects of cognition and behavior, though limitations with regard to analysis and interpretation remain. Here, we review recent findings, methodological considerations, neural and behavioral correlates, and future directions in the emerging field of dynamic FC investigations. {\textcopyright} 2013 Elsevier Inc.},
			archivePrefix = {arXiv},
			arxivId = {NIHMS150003},
			author = {Hutchison, R. Matthew and Womelsdorf, Thilo and Allen, Elena A. and Bandettini, Peter A. and Calhoun, Vince D. and Corbetta, Maurizio and {Della Penna}, Stefania and Duyn, Jeff H. and Glover, Gary H. and Gonzalez-Castillo, Javier and Handwerker, Daniel A. and Keilholz, Shella and Kiviniemi, Vesa and Leopold, David A. and de Pasquale, Francesco and Sporns, Olaf and Walter, Martin and Chang, Catie},
			doi = {10.1016/j.neuroimage.2013.05.079},
			eprint = {NIHMS150003},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/d72f41d058e3e9777c9ec248c2bf8da74197ff6a.pdf:pdf},
			isbn = {1095-9572 (Electronic)$\backslash$r1053-8119 (Linking)},
			issn = {10538119},
			journal = {NeuroImage},
			keywords = {Dynamics,Fluctuations,Functional MRI (fMRI),Functional connectivity,Resting state,Spontaneous activity},
			pages = {360--378},
			pmid = {23707587},
			publisher = {Elsevier Inc.},
			title = {{Dynamic functional connectivity: Promise, issues, and interpretations}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2013.05.079 https://ac.els-cdn.com/S105381191300579X/1-s2.0-S105381191300579X-main.pdf?{\_}tid=990023e9-57ba-4b4c-a267-705d1760a0bc{\&}acdnat=1543412441{\_}7110fbff4ca8224b974cc43f8f6df132},
			volume = {80},
			year = {2013}
			}

			@article{Miller2016,
			abstract = {Resting-state functional brain imaging studies of network connectivity have long assumed that functional connections are stationary on the timescale of a typical scan. Interest in moving beyond this simplifying assumption has emerged only recently. The great hope is that training the right lens on time-varying properties of whole-brain network connectivity will shed additional light on previously concealed brain activation patterns characteristic of serious neurological or psychiatric disorders. We present evidence that multiple explicitly dynamical properties of time-varying whole-brain network connectivity are strongly associated with schizophrenia, a complex mental illness whose symptomatic presentation can vary enormously across subjects. As with so much brain-imaging research, a central challenge for dynamic network connectivity lies in determining transformations of the data that both reduce its dimensionality and expose features that are strongly predictive of important population characteristics. Our paper introduces an elegant, simple method of reducing and organizing data around which a large constellation of mutually informative and intuitive dynamical analyses can be performed. This framework combines a discrete multidimensional data-driven representation of connectivity space with four core dynamism measures computed from large-scale properties of each subject's trajectory, ie., properties not identifiable with any specific moment in time and therefore reasonable to employ in settings lacking inter-subject time-alignment, such as resting-state functional imaging studies. Our analysis exposes pronounced differences between schizophrenia patients (Nsz = 151) and healthy controls (Nhc = 163). Time-varying whole-brain network connectivity patterns are found to be markedly less dynamically active in schizophrenia patients, an effect that is even more pronounced in patients with high levels of hallucinatory behavior. To the best of our knowledge this is the first demonstration that high-level dynamic properties of whole-brain connectivity, generic enough to be commensurable under many decompositions of time-varying connectivity data, exhibit robust and systematic differences between schizophrenia patients and healthy controls.},
			author = {Miller, Robyn L. and Yaesoubi, Maziar and Turner, Jessica A. and Mathalon, Daniel and Preda, Adrian and Pearlson, Godfrey and Adali, Tulay and Calhoun, Vince D.},
			doi = {10.1371/journal.pone.0149849},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/8e8bb49088a13858e3043a8c7646743f74f2129a.pdf:pdf},
			issn = {19326203},
			journal = {PloS one},
			number = {3},
			pages = {e0149849},
			pmid = {26981625},
			title = {{Higher Dimensional Meta-State Analysis Reveals Reduced Resting fMRI Connectivity Dynamism in Schizophrenia Patients}},
			url = {https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0149849{\&}type=printable},
			volume = {11},
			year = {2016}
			}

			@article{Taylor2011,
			author = {Taylor, M J and Donner, E J and Pang, E W},
			doi = {10.1016/j.neucli.2011.08.002},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/ed3e69fd6c220658022c7cb4cf123e5adc4b9645.pdf:pdf},
			issn = {0987-7053},
			journal = {Clinical Neurophysiology},
			keywords = {Autisme,Children with ASD,Cognitive development,D{\'{e}}veloppement cognitif,Flexibilit{\'{e}} attentionnelle,Fonctions ex{\'{e}}cutives,Frontal lobes,Hippocampe,Hippocampus,Lobes frontaux,M{\'{e}}moire de travail,Preterm children,Pr{\'{e}}maturit{\'{e}},Set-shifting,Working memory},
			number = {1},
			pages = {19--25},
			publisher = {Elsevier Masson SAS},
			title = {{fMRI and MEG in the study of typical and atypical cognitive development}},
			url = {http://dx.doi.org/10.1016/j.neucli.2011.08.002 http://ac.els-cdn.com/S0987705311000645/1-s2.0-S0987705311000645-main.pdf?{\_}tid=08be2326-e3d3-11e6-b8a4-00000aab0f26{\&}acdnat=1485440800{\_}b9f59baee8e622bbc614388328c25b34},
			volume = {42},
			year = {2011}
			}

			@article{Dale1999,
			author = {Dale, Anders M},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/95b8edc3e847aedf2381ba5b7eeadda5ed4a146c.pdf:pdf},
			journal = {Human Brain Mapping},
			keywords = {deconvolution,eeg,erp,linear model,meg,overlap correction,systems identification},
			pages = {109--114},
			title = {{Optimal Experimental Design for Event-Related fMRI}},
			url = {http://gablab.mit.edu/downloads/Dale.HBM.1999.pdf},
			volume = {114},
			year = {1999}
			}

			@article{Lawhern2013,
			author = {Lawhern, Vernon and Hairston, W David and Robbins, Kay},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Lawhern, Hairston, Robbins - 2013 - Optimal Feature Selection for Artifact Classification in EEG Time Series.pdf:pdf},
			journal = {Foundations of Augmented Cognition Lecture Notes in Computer Science},
			keywords = {ar,artifacts,autoregressive,classification,electroencephalography,feature selection,model,multinomial regression,penalized regression},
			pages = {326--334},
			title = {{Optimal Feature Selection for Artifact Classification in EEG Time Series}},
			url = {http://download.springer.com/static/pdf/969/chp:10.1007/978-3-642-39454-6{\_}34.pdf?auth66=1425466545{\_}b4a6708bdd5af6b34d08f9419a758105{\&}ext=.pdf},
			volume = {8027},
			year = {2013}
			}

			@article{Ciric2017,
			abstract = {Since initial reports regarding the impact of motion artifact on measures of functional connectivity, there has been a proliferation of participant-level confound regression methods to limit its impact. However, many of the most commonly used techniques have not been systematically evaluated using a broad range of outcome measures. Here, we provide a systematic evaluation of 14 participant-level confound regression methods in 393 youths. Specifically, we compare methods according to four benchmarks, including the residual relationship between motion and connectivity, distance-dependent effects of motion on connectivity, network identifiability, and additional degrees of freedom lost in confound regression. Our results delineate two clear trade-offs among methods. First, methods that include global signal regression minimize the relationship between connectivity and motion, but result in distance-dependent artifact. In contrast, censoring methods mitigate both motion artifact and distance-dependence, but use additional degrees of freedom. Importantly, less effective de-noising methods are also unable to identify modular network structure in the connectome. Taken together, these results emphasize the heterogeneous efficacy of existing methods, and suggest that different confound regression strategies may be appropriate in the context of specific scientific goals.},
			archivePrefix = {arXiv},
			arxivId = {1608.03616},
			author = {Ciric, Rastko and Wolf, Daniel H. and Power, Jonathan D. and Roalf, David R. and Baum, Graham L. and Ruparel, Kosha and Shinohara, Russell T. and Elliott, Mark A. and Eickhoff, Simon B. and Davatzikos, Christos and Gur, Ruben C. and Gur, Raquel E. and Bassett, Danielle S. and Satterthwaite, Theodore D.},
			doi = {10.1016/j.neuroimage.2017.03.020},
			eprint = {1608.03616},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/fdeefe94a4241b701c5df0d5b2dc3c7d5e8f3071.pdf:pdf},
			isbn = {1053-8119},
			issn = {10959572},
			journal = {NeuroImage},
			keywords = {Artifact,Confound,Functional connectivity,Motion,Noise,fMRI,motion,preprocessing},
			mendeley-tags = {motion,preprocessing},
			number = {March},
			pages = {174--187},
			pmid = {28302591},
			title = {{Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity}},
			url = {https://ac.els-cdn.com/S1053811917302288/1-s2.0-S1053811917302288-main.pdf?{\_}tid=4031e1d6-10bd-11e8-a578-00000aab0f02{\&}acdnat=1518526745{\_}cc06c9eb781f7de222d112ec786e613d},
			volume = {154},
			year = {2017}
			}

			@article{McLaren2012,
			abstract = {Functional MRI (fMRI) allows one to study task-related regional responses and task-dependent connectivity analysis using psychophysiological interaction (PPI) methods. The latter affords the additional opportunity to understand how brain regions interact in a task-dependent manner. The current implementation of PPI in Statistical Parametric Mapping (SPM8) is configured primarily to assess connectivity differences between two task conditions, when in practice fMRI tasks frequently employ more than two conditions. Here we evaluate how a generalized form of context-dependent PPI (gPPI; http://www.nitrc.org/projects/gppi), which is configured to automatically accommodate more than two task conditions in the same PPI model by spanning the entire experimental space, compares to the standard implementation in SPM8. These comparisons are made using both simulations and an empirical dataset. In the simulated dataset, we compare the interaction beta estimates to their expected values and model fit using the Akaike information criterion (AIC). We found that interaction beta estimates in gPPI were robust to different simulated data models, were not different from the expected beta value, and had better model fits than when using standard PPI (sPPI) methods. In the empirical dataset, we compare the model fit of the gPPI approach to sPPI. We found that the gPPI approach improved model fit compared to sPPI. There were several regions that became non-significant with gPPI. These regions all showed significantly better model fits with gPPI. Also, there were several regions where task-dependent connectivity was only detected using gPPI methods, also with improved model fit. Regions that were detected with all methods had more similar model fits. These results suggest that gPPI may have greater sensitivity and specificity than standard implementation in SPM. This notion is tempered slightly as there is no gold standard; however, data simulations with a known outcome support our conclusions about gPPI. In sum, the generalized form of context-dependent PPI approach has increased flexibility of statistical modeling, and potentially improves model fit, specificity to true negative findings, and sensitivity to true positive findings. ?? 2012.},
			archivePrefix = {arXiv},
			arxivId = {NIHMS150003},
			author = {McLaren, Donald G. and Ries, Michele L. and Xu, Guofan and Johnson, Sterling C.},
			doi = {10.1016/j.neuroimage.2012.03.068},
			eprint = {NIHMS150003},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/f94b6ccc39cbd09b8f9a77400ff2c014b4e7f4a0.pdf:pdf},
			isbn = {1095-9572 (Electronic)$\backslash$n1053-8119 (Linking)},
			issn = {10538119},
			journal = {NeuroImage},
			keywords = {Brain mapping,Context-dependent connectivity,Effective connectivity,FMRI,Functional connectivity,PPI,Psychophysiological interactions},
			number = {4},
			pages = {1277--1286},
			pmid = {22484411},
			publisher = {Elsevier B.V.},
			title = {{A generalized form of context-dependent psychophysiological interactions (gPPI): A comparison to standard approaches}},
			url = {http://dx.doi.org/10.1016/j.neuroimage.2012.03.068 http://ac.els-cdn.com/S1053811912003497/1-s2.0-S1053811912003497-main.pdf?{\_}tid=fedec7f6-4477-11e7-a7dd-00000aab0f27{\&}acdnat=1496066962{\_}12d097d18267ce131aab630c47033624},
			volume = {61},
			year = {2012}
			}

			@article{Hemmerling2007,
			abstract = {In the near future: Further down the road: ?? 2007 Elsevier Inc. All rights reserved.},
			author = {Hemmerling, Thomas M.},
			doi = {10.1016/j.aan.2007.07.006},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Hemmerling - 2007 - Automated Anesthesia - Fact or Fantasy.pdf:pdf},
			isbn = {1473-6500 (Electronic) 0952-7907 (Linking)},
			issn = {07376146},
			journal = {Advances in Anesthesia},
			number = {1},
			pages = {17--39},
			pmid = {19812483},
			title = {{Automated Anesthesia - Fact or Fantasy?}},
			volume = {25},
			year = {2007}
			}

			@article{Shirer2012,
			abstract = {Decoding specific cognitive states from brain activity constitutes a major goal of neuroscience. Previous studies of brain-state classification have focused largely on decoding brief, discrete events and have required the timing of these events to be known. To date, methods for decoding more continuous and purely subject-driven cognitive states have not been available. Here, we demonstrate that free-streaming subject-driven cognitive states can be decoded using a novel whole-brain functional connectivity analysis. Ninety functional regions of interest (ROIs) were defined across 14 large-scale resting-state brain networks to generate a 3960 cell matrix reflecting whole-brain connectivity. We trained a classifier to identify specific patterns of whole-brain connectivity as subjects rested quietly, remembered the events of their day, subtracted numbers, or (silently) sang lyrics. In a leave-one-out cross-validation, the classifier identified these 4 cognitive states with 84{\%} accuracy. More critically, the classifier achieved 85{\%} accuracy when identifying these states in a second, independent cohort of subjects. Classification accuracy remained high with imaging runs as short as 30--60 s. At all temporal intervals assessed, the 90 functionally defined ROIs outperformed a set of 112 commonly used structural ROIs in classifying cognitive states. This approach should enable decoding a myriad of subject-driven cognitive states from brief imaging data samples.},
			author = {Shirer, W. R. and Ryali, S. and Rykhlevskaia, E. and Menon, V. and Greicius, M. D.},
			doi = {10.1093/cercor/bhr099},
			file = {:Users/lorenafreitas/Downloads/Articles for Journal Club-20170517/Shirer{\_}Cognitive{\_}States{\_}Decoding.pdf:pdf},
			isbn = {1460-2199 (Electronic)$\backslash$n1047-3211 (Linking)},
			issn = {10473211},
			journal = {Cerebral Cortex},
			keywords = {classification,functional connectivity,resting-state,subject-driven cognition},
			number = {1},
			pages = {158--165},
			pmid = {21616982},
			title = {{Decoding subject-driven cognitive states with whole-brain connectivity patterns}},
			volume = {22},
			year = {2012}
			}

			@article{Liu2018,
			abstract = {The brain is a complex system that integrates and processes information across multiple time scales by dynamically coordinating activities over brain regions and circuits. Correlations in resting-state functional magnetic resonance imaging (rsfMRI) signals have been widely used to infer functional connectivity of the brain, providing a metric of functional associations that reflects a temporal average over an entire scan (typically several minutes or longer). Not until recently was the study of dynamic brain interactions at much shorter time scales (seconds to minutes) considered for inference of functional connectivity. One method proposed for this objective seeks to identify and extract recurring co-activation patterns (CAPs) that represent instantaneous brain configurations at single time points. Here, we review the development and recent advancement of CAP methodology and other closely related approaches, as well as their applications and associated findings. We also discuss the potential neural origins and behavioral relevance of CAPs, along with methodological issues and future research directions in the analysis of fMRI co-activation patterns.},
			author = {Liu, Xiao and Zhang, Nanyin and Chang, Catie and Duyn, Jeff H.},
			doi = {10.1016/j.neuroimage.2018.01.041},
			file = {:Users/lorenafreitas/Downloads/1-s2.0-S1053811918300417-main.pdf:pdf},
			issn = {10959572},
			journal = {NeuroImage},
			keywords = {Co-activation brain patterns,Dynamic brain connectivity,Resting-state fMRI},
			number = {September 2017},
			pages = {485--494},
			pmid = {29355767},
			publisher = {Elsevier Ltd},
			title = {{Co-activation patterns in resting-state fMRI signals}},
			url = {https://doi.org/10.1016/j.neuroimage.2018.01.041},
			volume = {180},
			year = {2018}
			}

			@article{Logothetis2001,
			abstract = {Functional magnetic resonance imaging (fMRI) is widely used to study the operational organization of the human brain, but the exact relationship between the measured fMRI signal and the underlying neural activity is unclear. Here we present simultaneous intracortical recordings of neural signals and fMRI responses. We compared local field potentials (LFPs), single- and multi-unit spiking activity with highly spatio-temporally resolved blood-oxygen-level-dependent (BOLD) fMRI responses from the visual cortex of monkeys. The largest magnitude changes were observed in LFPs, which at recording sites characterized by transient responses were the only signal that significantly correlated with the haemodynamic response. Linear systems analysis on a trial-by-trial basis showed that the impulse response of the neurovascular system is both animal- and site-specific, and that LFPs yield a better estimate of BOLD responses than the multi-unit responses. These findings suggest that the BOLD contrast mechanism reflects the input and intracortical processing of a given area rather than its spiking output.},
			author = {Logothetis, N K and Pauls, J and Augath, M and Trinath, T and Oeltermann, A},
			doi = {10.1038/35084005},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/c962e23f49ff7c16f4fd704d8a5bdb0fcef7f6cd.pdf:pdf},
			isbn = {0028-0836 (Print)$\backslash$r0028-0836 (Linking)},
			issn = {0028-0836},
			journal = {Nature},
			keywords = {Magnetic Resonance Imaging,Action Potentials,Animals,Contrast Sensitivity,Electrodes,Electrophysiology,Hemodynamics,Macaca mulatta,Neurons/physiology,Oxygen/blood,Photic Stimulation,Signal Processing, Computer-Assisted,Synaptic Transmission,Visual Cortex/*physiology},
			number = {6843},
			pages = {150--157},
			pmid = {11449264},
			title = {{Neurophysiological investigation of the basis of the fMRI signal}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/11449264},
			volume = {412},
			year = {2001}
			}

			@article{Power2017,
			abstract = {{\textcopyright} 2017, Public Library of Science. All rights reserved. This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Head motion can be estimated at any point of fMRI image processing. Processing steps involving temporal interpolation (e.g., slice time correction or outlier replacement) often precede motion estimation in the literature. From first principles it can be anticipated that temporal interpolation will alter head motion in a scan. Here we demonstrate this effect and its consequences in five large fMRI datasets. Estimated head motion was reduced by 10â50{\%} or more following temporal interpolation, and reductions were often visible to the naked eye. Such reductions make the data seem to be of improved quality. Such reductions also degrade the sensitivity of analyses aimed at detecting motion-related artifact and can cause a dataset with artifact to falsely appear artifact-free. These reduced motion estimates will be particularly problematic for studies needing estimates of motion in time, such as studies of dynamics. Based on these findings, it is sensible to obtain motion estimates prior to any image processing (regardless of subsequent processing steps and the actual timing of motion correction procedures, which need not be changed). We also find that outlier replacement procedures change signals almost entirely during times of motion and therefore have notable similarities to motion-targeting censoring strategies (which withhold or replace signals entirely during times of motion).},
			author = {Power, Jonathan D. and Plitt, Mark and Kundu, Prantik and Bandettini, Peter A. and Martin, Alex},
			doi = {10.1371/journal.pone.0182939},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/a4867581a029146d8aeb0cb07a7a3df2c1c72cf0.pdf:pdf},
			isbn = {1111111111},
			issn = {19326203},
			journal = {PLoS ONE},
			number = {9},
			pages = {1--20},
			title = {{Temporal interpolation alters motion in fMRI scans: Magnitudes and consequences for artifact detection}},
			url = {http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0182939{\&}type=printable},
			volume = {12},
			year = {2017}
			}

			@article{Gharabaghi2014,
			abstract = {Motor recovery after stroke is an unsolved challenge despite intensive rehabilitation training programs. Brain stimulation techniques have been explored in addition to traditional rehabilitation training to increase the excitability of the stimulated motor cortex. This modulation of cortical excitability augments the response to afferent input during motor exercises, thereby enhancing skilled motor learning by long-term potentiation-like plasticity. Recent approaches examined brain stimulation applied concurrently with voluntary movements to induce more specific use-dependent neural plasticity during motor training for neurorehabilitation. Unfortunately, such approaches are not applicable for the many severely affected stroke patients lacking residual hand function. These patients require novel activity-dependent stimulation paradigms based on intrinsic brain activity. Here, we report on such brain state-dependent stimulation (BSDS) combined with haptic feedback provided by a robotic hand orthosis. Transcranial magnetic stimulation (TMS) of the motor cortex and haptic feedback to the hand were controlled by sensorimotor desynchronization during motor-imagery and applied within a brain-machine interface (BMI) environment in one healthy subject and one patient with severe hand paresis in the chronic phase after stroke. BSDS significantly increased the excitability of the stimulated motor cortex in both healthy and post-stroke conditions, an effect not observed in non-BSDS protocols. This feasibility study suggests that closing the loop between intrinsic brain state, cortical stimulation and haptic feedback provides a novel neurorehabilitation strategy for stroke patients lacking residual hand function, a proposal that warrants further investigation in a larger cohort of stroke patients.},
			author = {Gharabaghi, Alireza and Kraus, Dominic and Le{\~{a}}o, Maria T and Sp{\"{u}}ler, Martin and Walter, Armin and Bogdan, Martin and Rosenstiel, Wolfgang and Naros, Georgios and Ziemann, Ulf},
			doi = {10.3389/fnhum.2014.00122},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Gharabaghi et al. - 2014 - Coupling brain-machine interfaces with cortical stimulation for brain-state dependent stimulation enhancing m.pdf:pdf},
			issn = {1662-5161},
			journal = {Frontiers in human neuroscience},
			keywords = {activity-dependent stimulation,brain state-dependent stimulation,brain state-dependent stimulation, activity-depend,brain-computer interface,brain-machine interface,brain-robot interface,closed-loop stimulation,transcranial magnetic stimulation},
			month = {jan},
			number = {March},
			pages = {122},
			pmid = {24634650},
			title = {{Coupling brain-machine interfaces with cortical stimulation for brain-state dependent stimulation: enhancing motor cortex excitability for neurorehabilitation.}},
			url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3942791{\&}tool=pmcentrez{\&}rendertype=abstract},
			volume = {8},
			year = {2014}
			}

			@article{King2014,
			abstract = {Parsing a cognitive task into a sequence of operations is a central problem in cognitive neuroscience. We argue that a major advance is now possible owing to the application of pattern classifiers to time-resolved recordings of brain activity [electroencephalography (EEG), magnetoencephalography (MEG), or intracranial recordings]. By testing at which moment a specific mental content becomes decodable in brain activity, we can characterize the time course of cognitive codes. Most importantly, the manner in which the trained classifiers generalize across time, and from one experimental condition to another, sheds light on the temporal organization of information-processing stages. A repertoire of canonical dynamical patterns is observed across various experiments and brain regions. This method thus provides a novel way to understand how mental representations are manipulated and transformed.},
			author = {King, J-R and Dehaene, S},
			doi = {10.1016/j.tics.2014.01.002},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/King, Dehaene - 2014 - Characterizing the dynamics of mental representations the temporal generalization method.pdf:pdf},
			issn = {1879-307X},
			journal = {Trends in cognitive sciences},
			keywords = {002,01,10,1016,1364-6613,2014,all rights reserved,decoding,doi,dx,eeg,http,j,meg,multivariate pattern analyses,org,parallel,processing,see front matter,serial processing,temporal generalization,tics,{\ss} 2014 elsevier ltd},
			month = {mar},
			number = {4},
			pages = {203--210},
			pmid = {24593982},
			publisher = {Elsevier Ltd},
			title = {{Characterizing the dynamics of mental representations: the temporal generalization method.}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/24593982},
			volume = {18},
			year = {2014}
			}

			@article{Weiskopf2005,
			abstract = {Functional magnetic resonance imaging (fMRI) measures the blood oxygen level-dependent (BOLD) signal related to neuronal activity. So far, this technique has been limited by time-consuming data analysis impeding on-line analysis. In particular, no brain-computer interface (BCI) was available which provided on-line feedback to learn physiological self-regulation of the BOLD signal. Recently, studies have shown that fMRI feedback is feasible and facilitates voluntary control of brain activity. Here we review these studies to make the fMRI feedback methodology accessible to a broader scientific community such as researchers concerned with functional brain imaging and the neurobiology of learning. Methodological and conceptual limitations were substantially reduced by artefact control, sensitivity improvements, real-time algorithms, and adapted experimental designs. Physiological self-regulation of the local BOLD response is a new paradigm for cognitive neuroscience to study brain plasticity and the functional relevance of regulated brain areas by modification of behaviour. Voluntary control of abnormal activity in circumscribed brain areas may even be applied as psychophysiological treatment.},
			author = {Weiskopf, Nikolaus and Scharnowski, Frank and Veit, Ralf and Goebel, Rainer and Birbaumer, Niels and Mathiak, Klaus},
			doi = {10.1016/j.jphysparis.2005.09.019},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Weiskopf et al. - 2005 - Self-regulation of local brain activity using real-time functional magnetic resonance imaging (fMRI).pdf:pdf},
			issn = {0928-4257},
			journal = {Journal of physiology, Paris},
			keywords = {Algorithms,Behavior,Behavior: physiology,Biofeedback,Brain,Brain Mapping,Brain: physiology,Cognition,Cognition: physiology,Computer-Assisted,Humans,Magnetic Resonance Imaging,Neuronal Plasticity,Neuronal Plasticity: physiology,Neurons,Neurons: physiology,Oxygen,Oxygen: blood,Psychology,Psychology: physiology,Signal Processing,Time Factors,real time},
			mendeley-tags = {real time},
			number = {4-6},
			pages = {357--73},
			pmid = {16289548},
			title = {{Self-regulation of local brain activity using real-time functional magnetic resonance imaging (fMRI).}},
			url = {http://www.ncbi.nlm.nih.gov/pubmed/16289548},
			volume = {98},
			year = {2005}
			}

			@article{Khalidov2011,
			abstract = {We propose a new framework to extract the activity-related component in the BOLD functional magnetic resonance imaging (fMRI) signal. As opposed to traditional fMRI signal analysis techniques, we do not impose any prior knowledge of the event timing. Instead, our basic assumption is that the activation pattern is a sequence of short and sparsely distributed stimuli, as is the case in slow event-related fMRI. We introduce new wavelet bases, termed activelets, which sparsify the activity-related BOLD signal. These wavelets mimic the behavior of the differential operator underlying the hemodynamic system. To recover the sparse representation, we deploy a sparse-solution search algorithm. The feasibility of the method is evaluated using both synthetic and experimental fMRI data. The importance of the activelet basis and the non-linear sparse recovery algorithm is demonstrated by comparison against classical B-spline wavelets and linear regularization, respectively. ?? 2011 Elsevier B.V. All rights reserved.},
			author = {Khalidov, Ildar and Fadili, Jalal and Lazeyras, Fran??ois and {Van De Ville}, Dimitri and Unser, Michael},
			doi = {10.1016/j.sigpro.2011.03.008},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/Khalidov et al. - 2011 - Activelets Wavelets for sparse representation of hemodynamic responses.pdf:pdf},
			issn = {01651684},
			journal = {Signal Processing},
			keywords = {??? 1 minimization,BOLD fMRI,Hemodynamic response,Sparsity,Wavelet design},
			number = {12},
			pages = {2810--2821},
			title = {{Activelets: Wavelets for sparse representation of hemodynamic responses}},
			volume = {91},
			year = {2011}
			}

			@article{Christoff2016,
			abstract = {Most research on mind-wandering has characterized it as a mental state with contents that are task unrelated or stimulus independent. However, the dynamics of mind-wandering â how mental states change over time â have remained largely neglected. Here, we introduce a dynamic framework for understanding mind-wandering and its relationship to the recruitment of large-scale brain networks. We propose that mind-wandering is best understood as a member of a family of spontaneous-thought phenomena that also includes creative thought and dreaming. This dynamic framework can shed new light on mental disorders that are marked by alterations in spontaneous thought, including depression, anxiety and attention deficit hyperactivity disorder},
			author = {Christoff, Kalina and Irving, Zachary C. and Fox, Kieran C. R. and Spreng, R. Nathan and Andrews-Hanna, Jessica R.},
			doi = {10.1038/nrn.2016.113},
			file = {:Users/lorenafreitas/Library/Application Support/Mendeley Desktop/Downloaded/5e2e335493b7a65768d7d4c546ccb373f0892ffd.pdf:pdf},
			isbn = {0894-0282},
			issn = {1471-003X},
			journal = {Nature Reviews Neuroscience},
			number = {11},
			pages = {718--731},
			pmid = {27654862},
			publisher = {Nature Publishing Group},
			title = {{Mind-wandering as spontaneous thought: a dynamic framework}},
			url = {http://www.nature.com/doifinder/10.1038/nrn.2016.113},
			volume = {17},
			year = {2016}
			}

			@article{Rack2012,
			title={Caffeine increases the temporal variability of resting-state {BOLD} connectivity in the motor cortex},
			author={Rack-Gomer, Anna Leigh and Liu, Thomas T},
			journal={Neuroimage},
			volume={59},
			number={3},
			pages={2994--3002},
			year={2012},
			publisher={Elsevier}
			}


			@inproceedings{Huang2017,
			Author = {Huang, Weiyu and Marques, Antonio G and Ribeiro, Alejandro},
			Booktitle = {Eur. Signal Process. Conf.},
			Date-Added = {2017-11-30 04:23:39 +0000},
			Date-Modified = {2017-11-30 04:24:33 +0000},
			Month = {Aug.},
			Pages = {1094--1098},
			Title = {Collaborative filtering via graph signal processing},
			Year = {2017}}

			@article{Chen2015c,
			Author = {Chen, Siheng and Varma, Rohan and Sandryhaila, Aliaksei and Kova{\v{c}}evi{\'c}, Jelena},
			Date-Added = {2017-09-19 05:06:22 +0000},
			Date-Modified = {2017-09-19 05:06:37 +0000},
			Journal = {IEEE Trans. Signal Process.},
			Month = {Dec.},
			Number = {24},
			Pages = {6510--6523},
			Title = {Discrete signal processing on graphs: Sampling theory},
			Volume = {63},
			Year = {2015}}

			@article{VonLuxburg2007,
			Author = {Von Luxburg, Ulrike},
			Date-Added = {2017-09-17 23:13:17 +0000},
			Date-Modified = {2017-09-17 23:13:50 +0000},
			Journal = {Stat. Comput.},
			Number = {4},
			Pages = {395--416},
			Title = {A tutorial on spectral clustering},
			Volume = {17},
			Year = {2007}}

			@article{Sporns2014,
			Author = {Sporns, Olaf},
			Date-Added = {2017-09-16 17:21:39 +0000},
			Date-Modified = {2017-09-16 17:21:54 +0000},
			Journal = {Nature Neurosci.},
			Month = {May},
			Number = {5},
			Pages = {652--660},
			Title = {Contributions and challenges for network models in cognitive neuroscience},
			Volume = {17},
			Year = {2014}}

			@article{Garrett2013,
			Author = {Garrett, Douglas D and Samanez-Larkin, Gregory R and MacDonald, Stuart WS and Lindenberger, Ulman and McIntosh, Anthony R and Grady, Cheryl L},
			Date-Added = {2017-09-16 17:13:58 +0000},
			Date-Modified = {2017-09-16 17:14:34 +0000},
			Journal = {Neurosci. Biobehav. Rev.},
			Month = {May},
			Number = {4},
			Pages = {610--624},
			Title = {Moment-to-moment brain signal variability: A next frontier in human brain mapping?},
			Volume = {37},
			Year = {2013}}

			@article{Preti2017b,
			Author = {Preti, Maria Giulia and Van De Ville, Dimitri},
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			Month = {Oct.},
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			year={2018},
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			@article{Li2018b,
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			journal={Human brain mapping},
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			title={More than just statics: temporal dynamics of intrinsic brain activity predicts the suicidal ideation in depressed patients},
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			title={Nicotine abstinence influences the calculation of salience in discrete insular circuits},
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			journal={Human brain mapping},
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			title={Tensor-based vs. matrix-based rank reduction in dynamic brain connectivity},
			author={Mokhtari, Fatemeh and Mayhugh, Rhiannon E and Hugenschmidt, Christina E and Rejeski, W Jack and Laurienti, Paul J},
			booktitle={Medical Imaging 2018: Image Processing},
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			}

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			title={Sustained versus instantaneous connectivity differentiates cognitive functions of processing speed and episodic memory},
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			title={Transient states of network connectivity are atypical in autism: A dynamic functional connectivity study},
			author={Mash, Lisa E and Linke, Annika C and Olson, Lindsay A and Fishman, Inna and Liu, Thomas T and M{\"u}ller, Ralph-Axel},
			journal={Human brain mapping},
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			title={Dynamic functional connectivity during task performance and rest predicts individual differences in attention across studies},
			author={Fong, Angus Ho Ching and Yoo, Kwangsun and Rosenberg, Monica D and Zhang, Sheng and Li, Chiang-Shan R and Scheinost, Dustin and Constable, R Todd and Chun, Marvin M},
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			author={Cordes, Dietmar and Zhuang, Xiaowei and Kaleem, Muhammad and Sreenivasan, Karthik and Yang, Zhengshi and Mishra, Virendra and Banks, Sarah J and Bluett, Brent and Cummings, Jeffrey L},
			journal={Alzheimer's \& Dementia: Translational Research \& Clinical Interventions},
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			title={Impact of 36 h of total sleep deprivation on resting-state dynamic functional connectivity},
			author={Xu, Huaze and Shen, Hui and Wang, Lubin and Zhong, Qi and Lei, Yu and Yang, Liu and Zeng, Ling-Li and Zhou, Zongtan and Hu, Dewen and Yang, Zheng},
			journal={Brain research},
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			title={Alterations of Resting-State Static and Dynamic Functional Connectivity of the Dorsolateral Prefrontal Cortex in Subjects with Internet Gaming Disorder},
			author={Han, Xu and Wu, Xiaowei and Wang, Yao and Sun, Yawen and Ding, Weina and Cao, Mengqiu and Du, Yasong and Lin, Fuchun and Zhou, Yan},
			journal={Frontiers in human neuroscience},
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			pages={41},
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			publisher={Frontiers}
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			title={The inner fluctuations of the brain in presymptomatic Frontotemporal Dementia: The chronnectome fingerprint},
			author={Premi, Enrico and Calhoun, Vince D and Diano, Matteo and Gazzina, Stefano and Cosseddu, Maura and Alberici, Antonella and Archetti, Silvana and Paternic{\`o}, Donata and Gasparotti, Roberto and van Swieten, John and others},
			journal={NeuroImage},
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			title={Bayesian structure learning for dynamic brain connectivity},
			author={Andersen, Michael and Winther, Ole and Hansen, Lars Kai and Poldrack, Russell and Koyejo, Oluwasanmi},
			booktitle={International Conference on Artificial Intelligence and Statistics},
			pages={1436--1446},
			year={2018}
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			author={Mokhtari, Fatemeh and Akhlaghi, Milad I and Simpson, Sean L and Wu, Guorong and Laurienti, Paul J},
			journal={NeuroImage},
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			title={Reduced network dynamics on functional MRI signals cognitive impairment in multiple sclerosis},
			author={Eijlers, Anand JC and Wink, Alle Meije and Meijer, Kim A and Douw, Linda and Geurts, Jeroen JG and Schoonheim, Menno M},
			journal={Radiology},
			pages={182623},
			year={2019},
			publisher={Radiological Society of North America}
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			title={Amyloid causes intermittent network disruptions in cognitively intact older subjects},
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			year={2019},
			publisher={Springer}
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			title={Atypical flexibility in dynamic functional connectivity quantifies the severity in autism spectrum disorder},
			author={Harlalka, Vatika and Bapi, Raju S and Vinod, PK and Roy, Dipanjan},
			journal={Frontiers in human neuroscience},
			volume={13},
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			year={2019},
			publisher={Frontiers}
			}

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			title={Dynamic reorganization of functional connectivity reveals abnormal temporal efficiency in schizophrenia},
			author={Sun, Yu and Collinson, Simon L and Suckling, John and Sim, Kang},
			journal={Schizophrenia bulletin},
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			number={3},
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			year={2018},
			publisher={Oxford University Press US}
			}

			@article{Li2019c,
			title={Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes},
			author={Li, Lingge and Pluta, Dustin and Shahbaba, Babak and Fortin, Norbert and Ombao, Hernando and Baldi, Pierre},
			journal={arXiv preprint arXiv:1905.10413},
			year={2019}
			}

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			title={Efficient Covariance Estimation from Temporal Data},
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			journal={arXiv preprint arXiv:1905.13276},
			year={2019}
			}

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			title={An average sliding window correlation method for dynamic functional connectivity},
			author={Vergara, Victor M and Abrol, Anees and Calhoun, Vince D},
			journal={Human brain mapping},
			volume={40},
			number={7},
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			year={2019},
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			title={Classification of cocaine-dependent participants with dynamic functional connectivity from functional magnetic resonance imaging data},
			author={Sakoglu, Unal and Mete, Mutlu and Esquivel, John and Rubia, Katya and Briggs, Richard and Adinoff, Bryon},
			journal={Journal of neuroscience research},
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			year={2019},
			publisher={Wiley Online Library}
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			title={Altered local and large-scale dynamic functional connectivity variability in posttraumatic stress disorder: a resting-state fMRI study},
			author={Fu, Shishun and Ma, Xiaofen and Wu, Yunfan and Bai, Zhigang and Yi, Yin and Liu, Mengchen and Lan, Zhihong and Hua, Kelei and Huang, Shumei and Li, Meng and others},
			journal={Frontiers in Psychiatry},
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			pages={234},
			year={2019},
			publisher={Frontiers}
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			year={2019},
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			title={Dynamic functional connectivity analysis of functional MRI based on copula time-varying correlation},
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			volume={323},
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			year={2019},
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			journal={bioRxiv},
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			year={2019},
			publisher={Cold Spring Harbor Laboratory}
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			journal={Frontiers in Neuroscience},
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			year={2019},
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			title={Dynamical exploration of the repertoire of brain networks at rest is modulated by psilocybin},
			author={Lord, Louis-David and Expert, Paul and Atasoy, Selen and Roseman, Leor and Rapuano, Kristina and Lambiotte, Renaud and Nutt, David J and Deco, Gustavo and Carhart-Harris, Robin L and Kringelbach, Morten L and others},
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			title={Functional parcellation of the hippocampus from resting-state dynamic functional connectivity},
			author={Zhong, Qi and Xu, Huaze and Qin, Jian and Zeng, Ling-Li and Hu, Dewen and Shen, Hui},
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			@article{Zhu2019,
			title={Abnormal Dynamic Functional Connectivity Associated With Subcortical Networks in Parkinson Disease: A Temporal Variability Perspective},
			author={Zhu, Hong and Huang, Juan and Deng, Lifu and He, Naying and Cheng, Lin and Shu, Pin and Yan, Fuhua and Tong, Shanbao and Sun, Junfeng and Ling, Huawei},
			journal={Frontiers in neuroscience},
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			year={2019},
			publisher={Frontiers}
			}

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			title={Eigenvector centrality dynamics from resting-state fMRI: gender and age differences in healthy subjects},
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			journal={Frontiers in Neuroscience},
			volume={13},
			year={2019},
			publisher={Frontiers Media SA}
			}

			@article{Wu2019,
			title={Personality traits are related with dynamic functional connectivity in major depression disorder: A resting-state analysis},
			author={Wu, Xinran and He, Hong and Shi, Liang and Xia, Yunman and Zuang, Kaixiang and Feng, Qiuyang and Zhang, Yao and Ren, Zhiting and Wei, Dongtao and Qiu, Jiang},
			journal={Journal of affective disorders},
			volume={245},
			pages={1032--1042},
			year={2019},
			publisher={Elsevier}
			}

			@phdthesis{Fatemeh2019,
			title={Convolutional Autoencoder for Studying Dynamic Functional Brain Connectivity in Resting-State Functional MRI},
			author={Fatemeh, Mohammadi},
			year={2019},
			school={Concordia University}
			}

			@article{Kung2019,
			title={Instability of brain connectivity during nonrapid eye movement sleep reflects altered properties of information integration},
			author={Kung, Yi-Chia and Li, Chia-Wei and Chen, Shuo and Chen, Sharon Chia-Ju and Lo, Chun-Yi Z and Lane, Timothy J and Biswal, Bharat and Wu, Changwei W and Lin, Ching-Po},
			journal={Human brain mapping},
			year={2019},
			publisher={Wiley Online Library}
			}

			@article{Mennigen2019c,
			title={Reduced higher dimensional temporal dynamism in neurofibromatosis type 1},
			author={Mennigen, Eva and Schuette, Peter and Vajdi, Ariana and Pacheco, Laura and Rosser, Tena and Bearden, Carrie E},
			journal={NeuroImage: Clinical},
			volume={22},
			pages={101692},
			year={2019},
			publisher={Elsevier}
			}

			@article{Song2019,
			title={Maximal flexibility in dynamic functional connectivity with critical dynamics revealed by fMRI data analysis and brain network modelling},
			author={Song, Benshen and Ma, Ningning and Liu, Guangyao and Zhang, Haochuan and Yu, Lianchun and Liu, LiWei and Zhang, Jing},
			journal={Journal of neural engineering},
			year={2019},
			publisher={IOP Publishing}
			}

			@inproceedings{Saha2019,
			title={Classification As a Criterion to Select Model Order For Dynamic Functional Connectivity States in Rest-fMRI Data},
			author={Saha, Debbrata K and Abrol, Anees and Damaraju, Eswar and Rashid, Barnaly and Plis, Sergey M and Calhoun, Vince D},
			booktitle={2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)},
			pages={1602--1605},
			year={2019},
			organization={IEEE}
			}

			@article{Zhai2019,
			title={Predicting Brain Age Based on Spatial and Temporal Features of Human Brain Functional Networks},
			author={Zhai, Jian and Li, Ke},
			journal={Frontiers in human neuroscience},
			volume={13},
			pages={62},
			year={2019},
			publisher={Frontiers}
			}

			@article{Engels2018,
			title={Dynamic functional connectivity and symptoms of Parkinson disease: a resting-state fMRI study},
			author={Engels, Gwenda and Vlaar, Annemarie and McCoy, Br{\'o}nagh and Scherder, Erik and Douw, Linda},
			journal={Frontiers in aging neuroscience},
			volume={10},
			pages={388},
			year={2018},
			publisher={Frontiers}
			}

			@misc{Chiang2018,
			title={Spectral analysis of dynamic functional connectivity between resting-state networks reveals information beyond static connectivity (P3. 281)},
			author={Chiang, Sharon and Vankov, Emilian and Yeh, Hsiang and Guindani, Michele and Vannucci, Marina and Haneef, Zulfi and Stern, John},
			year={2018},
			publisher={AAN Enterprises}
			}

			@article{Fu2018,
			title={Characterizing dynamic amplitude of low-frequency fluctuation and its relationship with dynamic functional connectivity: an application to schizophrenia},
			author={Fu, Zening and Tu, Yiheng and Di, Xin and Du, Yuhui and Pearlson, Godfrey D and Turner, Jessica A and Biswal, Bharat B and Zhang, Zhiguo and Calhoun, Vince D},
			journal={Neuroimage},
			volume={180},
			pages={619--631},
			year={2018},
			publisher={Elsevier}
			}

			@article{Du2018,
			title={Dynamic functional connectivity impairments in early schizophrenia and clinical high-risk for psychosis},
			author={Du, Yuhui and Fryer, Susanna L and Fu, Zening and Lin, Dongdong and Sui, Jing and Chen, Jiayu and Damaraju, Eswar and Mennigen, Eva and Stuart, Barbara and Loewy, Rachel L and others},
			journal={Neuroimage},
			volume={180},
			pages={632--645},
			year={2018},
			publisher={Elsevier}
			}

			@article{Beaty2018,
			title={Brain networks of the imaginative mind: Dynamic functional connectivity of default and cognitive control networks relates to openness to experience},
			author={Beaty, Roger E and Chen, Qunlin and Christensen, Alexander P and Qiu, Jiang and Silvia, Paul J and Schacter, Daniel L},
			journal={Human brain mapping},
			volume={39},
			number={2},
			pages={811--821},
			year={2018},
			publisher={Wiley Online Library}
			}

			@article{Long2019,
			title={F116. Gender Differences in Stability of Brain Functional Connectivity},
			author={Long, Yicheng and Ji, Jie Lisa and Anticevic, Alan},
			journal={Biological Psychiatry},
			volume={85},
			number={10},
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			year={2019},
			publisher={Elsevier}
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			@article{Mokhtari2018b,
			title={Dynamic fMRI networks predict success in a behavioral weight loss program among older adults},
			author={Mokhtari, Fatemeh and Rejeski, W Jack and Zhu, Yingying and Wu, Guorong and Simpson, Sean L and Burdette, Jonathan H and Laurienti, Paul J},
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			volume={173},
			pages={421--433},
			year={2018},
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			}

			@article{Arnone2018,
			title={The effects of serotonin modulation on medial prefrontal connectivity strength and stability: a pharmacological fMRI study with citalopram},
			author={Arnone, Danilo and Wise, Toby and Walker, C and Cowen, Phillip J and Howes, O and Selvaraj, Sudhakar},
			journal={Progress in Neuro-Psychopharmacology and Biological Psychiatry},
			volume={84},
			pages={152--159},
			year={2018},
			publisher={Elsevier}
			}

			@inproceedings{Cai2019,
			title={Improved estimation of dynamic connectivity from resting-state fMRI data},
			author={Cai, Biao and Stephen, Julia M and Wilson, Tony W and Calhoun, Vince D and Wang, Yu-Ping},
			booktitle={Medical Imaging 2019: Image Processing},
			volume={10949},
			pages={109490P},
			year={2019},
			organization={International Society for Optics and Photonics}
			}

			@article{Cai2018,
			title={Capturing Dynamic Connectivity from Resting State fMRI using Time-Varying Graphical Lasso},
			author={Cai, Biao and Zhang, Gemeng and Zhang, Aiying and Stephen, Julia M and Wilson, Tony W and Calhoun, Vince D and Wang, Yuping},
			journal={IEEE Transactions on Biomedical Engineering},
			year={2018},
			publisher={IEEE}
			}

			@article{Lee2019b,
			title={Brain-State Extraction Algorithm Based on the State Transition (BEST): A Dynamic Functional Brain Network Analysis in fMRI Study},
			author={Lee, Young-Beom and Yoo, Kwangsun and Roh, Jee Hoon and Moon, Won-Jin and Jeong, Yong},
			journal={Brain topography},
			pages={1--17},
			year={2019},
			publisher={Springer}
			}

			@inproceedings{Faghiri2019,
			title={Using Gradient as a New Metric for Dynamic Connectivity Estimation from Resting fMRI Data},
			author={Faghiri, Ashkan and Stephen, Julia M and Wang, Yu-Ping and Wilson, Tony W and Calhoun, Vince D},
			booktitle={2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)},
			pages={1805--1808},
			year={2019},
			organization={IEEE}
			}

			@article{Zhi2018,
			title={Aberrant dynamic functional network connectivity and graph properties in major depressive disorder},
			author={Zhi, Dongmei and Calhoun, Vince D and Lv, Luxian and Ma, Xiaohong and Ke, Qing and Yang, Yongfeng and Yang, Xiao and Pan, Miao and Qi, Shile and Jiang, Rongtao and others},
			journal={Frontiers in psychiatry},
			volume={9},
			pages={339},
			year={2018},
			publisher={Frontiers}
			}

			@article{Fu2019b,
			title={Altered static and dynamic functional network connectivity in Alzheimer disease and subcortical ischemic vascular disease: shared and specific brain connectivity abnormalities},
			author={Fu, Zening and Caprihan, Arvind and Chen, Jiayu and Du, Yuhui and Adair, John C and Sui, Jing and Rosenberg, Gary A and Calhoun, Vince D},
			journal={Human Brain Mapping},
			year={2019},
			publisher={Wiley Online Library}
			}

			@article{Schumacher2019,
			title={Dynamic functional connectivity changes in dementia with Lewy bodies and Alzheimer disease},
			author={Schumacher, Julia and Peraza, Luis R and Firbank, Michael and Thomas, Alan J and Kaiser, Marcus and Gallagher, Peter and O'Brien, John T and Blamire, Andrew M and Taylor, John-Paul},
			journal={NeuroImage: Clinical},
			volume={22},
			pages={101812},
			year={2019},
			publisher={Elsevier}
			}

			@article{Bosma2018,
			title={Dynamic pain connectome functional connectivity and oscillations reflect multiple sclerosis pain},
			author={Bosma, Rachael L and Kim, Junseok A and Cheng, Joshua C and Rogachov, Anton and Hemington, Kasey S and Osborne, Natalie R and Oh, Jiwon and Davis, Karen D},
			journal={Pain},
			volume={159},
			number={11},
			pages={2267--2276},
			year={2018},
			publisher={LWW}
			}

			@article{Li2019d,
			title={Predicting the Post-therapy Severity Level (UPDRS-III) of Patients With Parkinson Disease After Drug Therapy by Using the Dynamic Connectivity Efficiency of fMRI},
			author={Li, Xuesong and Xiong, Yuhui and Liu, Simin and Zhou, Rongsong and Hu, Zhangxuan and Tong, Yan and He, Le and Niu, Zhendong and Ma, Yu and Guo, Hua},
			journal={Frontiers in neurology},
			volume={10},
			year={2019},
			publisher={Frontiers Media SA}
			}

			@article{Marusak2018,
			title={Mindfulness and dynamic functional neural connectivity in children and adolescents},
			author={Marusak, Hilary A and Elrahal, Farrah and Peters, Craig A and Kundu, Prantik and Lombardo, Michael V and Calhoun, Vince D and Goldberg, Elimelech K and Cohen, Cindy and Taub, Jeffrey W and Rabinak, Christine A},
			journal={Behavioural brain research},
			volume={336},
			pages={211--218},
			year={2018},
			publisher={Elsevier}
			}

			@article{Lim2018,
			title={Dynamic functional connectivity markers of objective trait mindfulness},
			author={Lim, Julian and Teng, James and Patanaik, Amiya and Tandi, Jesisca and Massar, Stijn AA},
			journal={Neuroimage},
			volume={176},
			pages={193--202},
			year={2018},
			publisher={Elsevier}
			}

			@article{Zheng2018,
			title={The dynamic characteristics of the anterior cingulate cortex in resting-state fMRI of patients with depression},
			author={Zheng, Hongna and Li, Feng and Bo, Qijing and Li, Xianbin and Yao, Li and Yao, Zhijun and Wang, Chuanyue and Wu, Xia},
			journal={Journal of affective disorders},
			volume={227},
			pages={391--397},
			year={2018},
			publisher={Elsevier}
			}

			@article{Guo2018,
			title={The instability of functional connectivity in patients with schizophrenia and their siblings: A dynamic connectivity study},
			author={Guo, Shuixia and Zhao, Wei and Tao, Haojuan and Liu, Zhening and Palaniyappan, Lena},
			journal={Schizophrenia research},
			volume={195},
			pages={183--189},
			year={2018},
			publisher={Elsevier}
			}

			@article{Espinoza2019,
			title={Dynamic functional network connectivity in Huntington disease and its associations with motor and cognitive measures},
			author={Espinoza, Flor A and Liu, Jingyu and Ciarochi, Jennifer and Turner, Jessica A and Vergara, Victor M and Caprihan, Arvind and Misiura, Maria and Johnson, Hans J and Long, Jeffrey D and Bockholt, Jeremy H and others},
			journal={Human brain mapping},
			volume={40},
			number={6},
			pages={1955--1968},
			year={2019},
			publisher={Wiley Online Library}
			}

			@article{Qiu2018,
			title={Abnormal dynamic functional connectivity of amygdalar subregions in untreated patients with first-episode major depressive disorder},
			author={Qiu, Lihua and Xia, Mingrui and Cheng, Bochao and Yuan, Lin and Kuang, Weihong and Bi, Feng and Ai, Hua and Gu, Zhongwei and Lui, Su and Huang, Xiaoqi and others},
			journal={Journal of psychiatry \& neuroscience: JPN},
			volume={43},
			number={4},
			pages={262},
			year={2018},
			publisher={Canadian Medical Association}
			}

			@article{Tian2018,
			title={Changes in dynamic functional connections with aging},
			author={Tian, Lixia and Li, Qizhuo and Wang, Chaomurilige and Yu, Jian},
			journal={Neuroimage},
			volume={172},
			pages={31--39},
			year={2018},
			publisher={Elsevier}
			}

			@article{Wang2019,
			title={Abnormal dynamic functional network connectivity in unmedicated bipolar and major depressive disorders based on the triple-network model},
			author={Wang, Junjing and Wang, Ying and Huang, Huiyuan and Jia, Yanbin and Zheng, Senning and Zhong, Shuming and Chen, Guanmao and Huang, Li and Huang, Ruiwang},
			journal={Psychological medicine},
			pages={1--10},
			year={2019},
			publisher={Cambridge University Press}
			}

			@article{Mennigen2018,
			title={Reduced higher-dimensional resting state fMRI dynamism in clinical high-risk individuals for schizophrenia identified by meta-state analysis},
			author={Mennigen, Eva and Miller, Robyn L and Rashid, Barnaly and Fryer, Susanna L and Loewy, Rachel L and Stuart, Barbara K and Mathalon, Daniel H and Calhoun, Vince D},
			journal={Schizophrenia research},
			volume={201},
			pages={217--223},
			year={2018},
			publisher={Elsevier}
			}

			@inproceedings{Bhinge2018,
			title={IVA-Based Spatio-Temporal Dynamic Connectivity Analysis in Large-Scale FMRI Data},
			author={Bhinge, Suchita and Calhoun, Vince D and Adal{\i}, T{\"u}lay},
			booktitle={2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
			pages={965--969},
			year={2018},
			organization={IEEE}
			}

			@misc{Dambrosio2018,
			title={Reduced Dynamism of Functional Connectivity Is Associated with Cognitive Impairment in Multiple Sclerosis Patients: A Dynamic Functional Connectivity Study in a Multi-Center Setting (P3. 379)},
			author={DâAmbrosio, Alessandro and Rocca, Maria A and Valsasina, Paola and Gallo, Antonio and De Stefano, Nicola and Pareto, Debora and Barkhof, Frederik and Ciccarelli, Olga and Enzinger, Christian and Tedeschi, Gioacchino and others},
			year={2018},
			publisher={AAN Enterprises}
			}

			@article{Dambrosio2019,
			title={Reduced dynamics of functional connectivity and cognitive impairment in multiple sclerosis},
			author={dâAmbrosio, Alessandro and Valsasina, Paola and Gallo, Antonio and De Stefano, Nicola and Pareto, Deborah and Barkhof, Frederik and Ciccarelli, Olga and Enzinger, Christian and Tedeschi, Gioacchino and Stromillo, M Laura and others},
			journal={Multiple Sclerosis Journal},
			pages={1352458519837707},
			year={2019},
			publisher={SAGE Publications Sage UK: London, England}
			}

			@article{Klugah2019,
			title={Altered dynamic functional network connectivity in frontal lobe epilepsy},
			author={Klugah-Brown, Benjamin and Luo, Cheng and He, Hui and Jiang, Sisi and Armah, Gabriel Kofi and Wu, Yu and Li, Jianfu and Yin, Wenjie and Yao, Dezhong},
			journal={Brain topography},
			volume={32},
			number={3},
			pages={394--404},
			year={2019},
			publisher={Springer}
			}

			@article{Xia2019,
			title={Tracking the dynamic functional connectivity structure of the human brain across the adult lifespan},
			author={Xia, Yunman and Chen, Qunlin and Shi, Liang and Li, MengZe and Gong, Weikang and Chen, Hong and Qiu, Jiang},
			journal={Human brain mapping},
			volume={40},
			number={3},
			pages={717--728},
			year={2019},
			publisher={Wiley Online Library}
			}
			@article{Hu2018,
			title={Dynamic network analysis reveals altered temporal variability in brain regions after stroke: a longitudinal resting-state fMRI study},
			author={Hu, Jianping and Du, Juan and Xu, Qiang and Yang, Fang and Zeng, Fanyong and Weng, Yifei and Dai, Xi-jian and Qi, Rongfeng and Liu, Xiaoxue and Lu, Guangming and others},
			journal={Neural plasticity},
			volume={2018},
			year={2018},
			publisher={Hindawi}
			}

			@inproceedings{Salman2018,
			title={Weak Mutual Information Between Functional Domains in Schizophrenia},
			author={Salman, Mustafa S and Vergara, Victor M and Damaraju, Eswar and Calhoun, Vince D},
			booktitle={2018 52nd Asilomar Conference on Signals, Systems, and Computers},
			pages={1362--1366},
			year={2018},
			organization={IEEE}
			}

			@article{Dong2018,
			title={Reconfiguration of dynamic functional connectivity in sensory and perceptual system in schizophrenia},
			author={Dong, Debo and Duan, Mingjun and Wang, Yulin and Zhang, Xingxing and Jia, Xiaoyan and Li, Yingjia and Xin, Fei and Yao, Dezhong and Luo, Cheng},
			journal={Cerebral Cortex},
			year={2018}
			}

			@article{Zhang2018b,
			title={Abnormal dynamic functional connectivity between speech and auditory areas in schizophrenia patients with auditory hallucinations},
			author={Zhang, Wenjing and Li, Siyi and Wang, Xiuli and Gong, Yao and Yao, Li and Xiao, Yuan and Liu, Jieke and Keedy, Sarah K and Gong, Qiyong and Sweeney, John A and others},
			journal={NeuroImage: Clinical},
			volume={19},
			pages={918--924},
			year={2018},
			publisher={Elsevier}
			}

			@article{Malhi2019,
			title={Resting-state neural network disturbances that underpin the emergence of emotional symptoms in adolescent girls: resting-state fMRI study},
			author={Malhi, Gin S and Das, Pritha and Outhred, Tim and Bryant, Richard A and Calhoun, Vince},
			journal={The British Journal of Psychiatry},
			pages={1--7},
			year={2019},
			publisher={Cambridge University Press}
			}
			@article{Oneill2018,
			title={Dynamics of large-scale electrophysiological networks: A technical review},
			author={O'Neill, George C and Tewarie, Prejaas and Vidaurre, Diego and Liuzzi, Lucrezia and Woolrich, Mark W and Brookes, Matthew J},
			journal={Neuroimage},
			volume={180},
			pages={559--576},
			year={2018},
			publisher={Elsevier}
			}

			@article{Cohen2018,
			title={The behavioral and cognitive relevance of time-varying, dynamic changes in functional connectivity},
			author={Cohen, Jessica R},
			journal={NeuroImage},
			volume={180},
			pages={515--525},
			year={2018},
			publisher={Elsevier}
			}

			@article{DePasquale2018,
			title={Cortical cores in network dynamics},
			author={de Pasquale, Francesco and Corbetta, Maurizio and Betti, Viviana and Della Penna, Stefania},
			journal={Neuroimage},
			volume={180},
			pages={370--382},
			year={2018},
			publisher={Elsevier}
			}

			@article{Mashour2018,
			title={Neural correlates of unconsciousness in large-scale brain networks},
			author={Mashour, George A and Hudetz, Anthony G},
			journal={Trends in neurosciences},
			volume={41},
			number={3},
			pages={150--160},
			year={2018},
			publisher={Elsevier}
			}

			@article{Du2018b,
			title={Classification and prediction of brain disorders using functional connectivity: promising but challenging},
			author={Du, Yuhui and Fu, Zening and Calhoun, Vince D},
			journal={Frontiers in neuroscience},
			volume={12},
			year={2018},
			publisher={Frontiers Media SA}
			}

			@article{Uddin2018,
			title={Future directions for examination of brain networks in neurodevelopmental disorders},
			author={Uddin, Lucina Q and Karlsgodt, Katherine H},
			journal={Journal of Clinical Child \& Adolescent Psychology},
			volume={47},
			number={3},
			pages={483--497},
			year={2018},
			publisher={Taylor \& Francis}
			}

			@article{Valsasina2019,
			title={Characterizing Rapid Fluctuations of Resting State Functional Connectivity in Demyelinating, Neurodegenerative, and Psychiatric Conditions: From Static to Time-Varying Analysis},
			author={Valsasina, Paola and de la Cruz, Milagros Hidalgo and Filippi, Massimo and Rocca, Maria A},
			journal={Frontiers in neuroscience},
			volume={13},
			year={2019},
			publisher={Frontiers Media SA}
			}

			@article{Girn2019,
			title={Linking brain network reconfiguration and intelligence: Are we there yet?},
			author={Girn, Manesh and Mills, Caitlin and Christoff, Kalina},
			journal={Trends in neuroscience and education},
			year={2019},
			publisher={Elsevier}
			}

			@article{Filippi2019,
			title={Resting state dynamic functional connectivity in neurodegenerative conditions: a review of magnetic resonance imaging findings},
			author={Filippi, Massimo and Spinelli, Edoardo G and Cividini, Camilla and Agosta, Federica},
			journal={Frontiers in neuroscience},
			volume={13},
			year={2019},
			publisher={Frontiers Media SA}
			}

			@article{White2019,
			title={Dissecting Static and Dynamic Functional Connectivity: Example From the Autism Spectrum},
			author={White, Tonya and Calhoun, Vince D},
			journal={Journal of experimental neuroscience},
			volume={13},
			pages={1179069519851809},
			year={2019},
			publisher={SAGE Publications Sage UK: London, England}
			}

			@article{Lurie2018,
			title={On the nature of resting fMRI and time-varying functional connectivity},
			author={Lurie, Daniel and Kessler, Daniel and Bassett, Danielle and Betzel, Richard F and Breakspear, Michael and Keilholz, Shella and Kucyi, Aaron and Li{\'e}geois, Raphael and Lindquist, Martin A and McIntosh, Anthony R},
			journal={PsyArXiv Preprints},
			year={2018}
			}

			@article{Cavanna2018,
			title={Dynamic functional connectivity and brain metastability during altered states of consciousness},
			author={Cavanna, Federico and Vilas, Martina G and Palmucci, Matias and Tagliazucchi, Enzo},
			journal={Neuroimage},
			volume={180},
			pages={383--395},
			year={2018},
			publisher={Elsevier}
			}

			@article{Thompson2018,
			title={Neural and metabolic basis of dynamic resting state fMRI},
			author={Thompson, Garth J},
			journal={Neuroimage},
			volume={180},
			pages={448--462},
			year={2018},
			publisher={Elsevier}
			}

			@article{Liegeois2017,
			title={Interpreting temporal fluctuations in resting-state functional connectivity MRI},
			author={Liegeois, Raphael and Laumann, Timothy O and Snyder, Abraham Z and Zhou, Juan and Yeo, BT Thomas},
			journal={Neuroimage},
			volume={163},
			pages={437--455},
			year={2017},
			publisher={Elsevier}
			}

			@article{Abrol2017,
			title={Replicability of time-varying connectivity patterns in large resting state fMRI samples},
			author={Abrol, Anees and Damaraju, Eswar and Miller, Robyn L and Stephen, Julia M and Claus, Eric D and Mayer, Andrew R and Calhoun, Vince D},
			journal={Neuroimage},
			volume={163},
			pages={160--176},
			year={2017},
			publisher={Elsevier}
			}

			@article{Kucyi2018,
			title={Just a thought: How mind-wandering is represented in dynamic brain connectivity},
			author={Kucyi, Aaron},
			journal={Neuroimage},
			volume={180},
			pages={505--514},
			year={2018},
			publisher={Elsevier}
			}

			@article{Choe2017,
			title={Comparing test-retest reliability of dynamic functional connectivity methods},
			author={Choe, Ann S and Nebel, Mary Beth and Barber, Anita D and Cohen, Jessica R and Xu, Yuting and Pekar, James J and Caffo, Brian and Lindquist, Martin A},
			journal={Neuroimage},
			volume={158},
			pages={155--175},
			year={2017},
			publisher={Elsevier}
			}

			@article{Saggar2018,
			title={Towards a new approach to reveal dynamical organization of the brain using topological data analysis},
			author={Saggar, Manish and Sporns, Olaf and Gonzalez-Castillo, Javier and Bandettini, Peter A and Carlsson, Gunnar and Glover, Gary and Reiss, Allan L},
			journal={Nature communications},
			volume={9},
			number={1},
			pages={1399},
			year={2018},
			publisher={Nature Publishing Group}
			}

			@article{Matsui2018,
			title={Neuronal origin of the temporal dynamics of spontaneous BOLD activity correlation},
			author={Matsui, Teppei and Murakami, Tomonari and Ohki, Kenichi},
			journal={Cerebral Cortex},
			volume={29},
			number={4},
			pages={1496--1508},
			year={2018},
			publisher={Oxford University Press}
			}

			@article{Matsui2018b,
			title={Mouse optical imaging for understanding resting-state functional connectivity in human fMRI},
			author={Matsui, Teppei and Murakami, Tomonari and Ohki, Kenichi},
			journal={Communicative \& integrative biology},
			volume={11},
			number={4},
			pages={e1528821},
			year={2018},
			publisher={Taylor \& Francis}
			}

			@article{Griffa2017,
			title={Transient networks of spatio-temporal connectivity map communication pathways in brain functional systems},
			author={Griffa, Alessandra and Ricaud, Benjamin and Benzi, Kirell and Bresson, Xavier and Daducci, Alessandro and Vandergheynst, Pierre and Thiran, Jean-Philippe and Hagmann, Patric},
			journal={NeuroImage},
			volume={155},
			pages={490--502},
			year={2017},
			publisher={Elsevier}
			}

			@article{Miller2018,
			title={Resting-state fMRI dynamics and null models: Perspectives, sampling variability, and simulations},
			author={Miller, Robyn L and Abrol, Anees and Adali, Tulay and Levin-Schwartz, Yuri and Calhoun, Vince D},
			journal={Frontiers in neuroscience},
			volume={12},
			pages={551},
			year={2018},
			publisher={Frontiers}
			}

			@article{Zhang2017,
			title={Test-retest reliability of âhigh-orderâ functional connectivity in young healthy adults},
			author={Zhang, Han and Chen, Xiaobo and Zhang, Yu and Shen, Dinggang},
			journal={Frontiers in neuroscience},
			volume={11},
			pages={439},
			year={2017},
			publisher={Frontiers}
			}

			@article{Zhou2018,
			title={Simultaneous estimation of low-and high-order functional connectivity for identifying mild cognitive impairment},
			author={Zhou, Yueying and Qiao, Lishan and Li, Weikai and Zhang, Limei and Shen, Dinggang},
			journal={Frontiers in Neuroinformatics},
			volume={12},
			pages={3},
			year={2018},
			publisher={Frontiers}
			}

			@article{Guo2017,
			title={Alzheimer classification using a minimum spanning tree of high-order functional network on fMRI dataset},
			author={Guo, Hao and Liu, Lei and Chen, Junjie and Xu, Yong and Jie, Xiang},
			journal={Frontiers in neuroscience},
			volume={11},
			pages={639},
			year={2017},
			publisher={Frontiers}
			}

			@article{Duff2018,
			title={Disambiguating brain functional connectivity},
			author={Duff, Eugene P and Makin, Tamar and Cottaar, Michiel and Smith, Stephen M and Woolrich, Mark W},
			journal={NeuroImage},
			volume={173},
			pages={540--550},
			year={2018},
			publisher={Elsevier}
			}

			@article{Yousefi2018,
			title={Quasi-periodic patterns of intrinsic brain activity in individuals and their relationship to global signal},
			author={Yousefi, Behnaz and Shin, Jaemin and Schumacher, Eric H and Keilholz, Shella D},
			journal={NeuroImage},
			volume={167},
			pages={297--308},
			year={2018},
			publisher={Elsevier}
			}

			@article{Abbas2019,
			title={Quasi-periodic patterns contribute to functional connectivity in the brain},
			author={Abbas, Anzar and Belloy, Michael and Kashyap, Amrit and Billings, Jacob and Nezafati, Maysam and Schumacher, Eric H and Keilholz, Shella},
			journal={Neuroimage},
			volume={191},
			pages={193--204},
			year={2019},
			publisher={Elsevier}
			}

			@article{Fukushima2018,
			title={Structure--function relationships during segregated and integrated network states of human brain functional connectivity},
			author={Fukushima, Makoto and Betzel, Richard F and He, Ye and van den Heuvel, Martijn P and Zuo, Xi-Nian and Sporns, Olaf},
			journal={Brain Structure and Function},
			volume={223},
			number={3},
			pages={1091--1106},
			year={2018},
			publisher={Springer}
			}

			@article{Fukushima2018b,
			title={Fluctuations between high-and low-modularity topology in time-resolved functional connectivity},
			author={Fukushima, Makoto and Betzel, Richard F and He, Ye and de Reus, Marcel A and van den Heuvel, Martijn P and Zuo, Xi-Nian and Sporns, Olaf},
			journal={NeuroImage},
			volume={180},
			pages={406--416},
			year={2018},
			publisher={Elsevier}
			}

			@article{Xu2018,
			title={Synchronization transition in neuronal networks composed of chaotic or non-chaotic oscillators},
			author={Xu, Kesheng and Maidana, Jean Paul and Castro, Samy and Orio, Patricio},
			journal={Scientific reports},
			volume={8},
			number={1},
			pages={8370},
			year={2018},
			publisher={Nature Publishing Group}
			}

			@article{Pedersen2018,
			title={On the relationship between instantaneous phase synchrony and correlation-based sliding windows for time-resolved fMRI connectivity analysis},
			author={Pedersen, Mangor and Omidvarnia, Amir and Zalesky, Andrew and Jackson, Graeme D},
			journal={Neuroimage},
			volume={181},
			pages={85--94},
			year={2018},
			publisher={Elsevier}
			}

			@article{Ma2018,
			title={Temporal transitions of spontaneous brain activity},
			author={Ma, Zhiwei and Zhang, Nanyin},
			journal={Elife},
			volume={7},
			pages={e33562},
			year={2018},
			publisher={eLife Sciences Publications Limited}
			}

			@article{Pallares2018,
			title={Extracting orthogonal subject-and condition-specific signatures from fMRI data using whole-brain effective connectivity},
			author={Pallar{\'e}s, Vicente and Insabato, Andrea and Sanju{\'a}n, Ana and K{\"u}hn, Simone and Mantini, Dante and Deco, Gustavo and Gilson, Matthieu},
			journal={Neuroimage},
			volume={178},
			pages={238--254},
			year={2018},
			publisher={Elsevier}
			}

			@article{Thompson2018b,
			title={A common framework for the problem of deriving estimates of dynamic functional brain connectivity},
			author={Thompson, William Hedley and Fransson, Peter},
			journal={Neuroimage},
			volume={172},
			pages={896--902},
			year={2018},
			publisher={Elsevier}
			}

			@article{Thompson2018c,
			title={Simulations to benchmark time-varying connectivity methods for fMRI},
			author={Thompson, William Hedley and Richter, Craig Geoffrey and Plav{\'e}n-Sigray, Pontus and Fransson, Peter},
			journal={PLoS computational biology},
			volume={14},
			number={5},
			pages={e1006196},
			year={2018},
			publisher={Public Library of Science}
			}

			@article{Glomb2018,
			title={Stereotypical modulations in dynamic functional connectivity explained by changes in BOLD variance},
			author={Glomb, Katharina and Ponce-Alvarez, Adri{\'a}n and Gilson, Matthieu and Ritter, Petra and Deco, Gustavo},
			journal={NeuroImage},
			volume={171},
			pages={40--54},
			year={2018},
			publisher={Elsevier}
			}

			@article{Abrol2017b,
			title={Schizophrenia shows disrupted links between brain volume and dynamic functional connectivity},
			author={Abrol, Anees and Rashid, Barnaly and Rachakonda, Srinivas and Damaraju, Eswar and Calhoun, Vince D},
			journal={Frontiers in neuroscience},
			volume={11},
			pages={624},
			year={2017},
			publisher={Frontiers}
			}

			@article{Zhuang2018,
			title={Incorporating spatial constraint in co-activation pattern analysis to explore the dynamics of resting-state networks: An application to Parkinsons disease},
			author={Zhuang, Xiaowei and Walsh, Ryan R and Sreenivasan, Karthik and Yang, Zhengshi and Mishra, Virendra and Cordes, Dietmar},
			journal={NeuroImage},
			volume={172},
			pages={64--84},
			year={2018},
			publisher={Elsevier}
			}

			@article{Besseling2018,
			title={Functional network abnormalities consistent with behavioral profile in Autism Spectrum Disorder},
			author={Besseling, Ren{\'e} and Lamerichs, Rolf and Michels, Britt and Heunis, Stephan and de Louw, Anton and Tijhuis, Anton and Bergmans, Jan and Aldenkamp, Bert},
			journal={Psychiatry Research: Neuroimaging},
			volume={275},
			pages={43--48},
			year={2018},
			publisher={Elsevier}
			}

			@article{DiPerri2018,
			title={Multifaceted brain networks reconfiguration in disorders of consciousness uncovered by co-activation patterns},
			author={Di Perri, Carol and Amico, Enrico and Heine, Lizette and Annen, Jitka and Martial, Charlotte and Larroque, Stephen Karl and Soddu, Andrea and Marinazzo, Daniele and Laureys, Steven},
			journal={Human brain mapping},
			volume={39},
			number={1},
			pages={89--103},
			year={2018},
			publisher={Wiley Online Library}
			}

			@article{Kaiser2019,
			title={Abnormal frontoinsular-default network dynamics in adolescent depression and rumination: a preliminary resting-state co-activation pattern analysis},
			author={Kaiser, Roselinde H and Kang, Min Su and Lew, Yechan and Van Der Feen, Julie and Aguirre, Blaise and Clegg, Rachel and Goer, Franziska and Esposito, Erika and Auerbach, Randy P and Hutchison, R Matthew and others},
			journal={Neuropsychopharmacology},
			pages={1},
			year={2019},
			publisher={Nature Publishing Group}
			}

			@article{Lennartz2018,
			title={Sparse estimation of resting-state effective connectivity from fMRI cross-spectra},
			author={Lennartz, Carolin and Schiefer, Jonathan and Rotter, Stefan and Hennig, J{\"u}rgen and LeVan, Pierre},
			journal={Frontiers in neuroscience},
			volume={12},
			pages={287},
			year={2018},
			publisher={Frontiers}
			}

			@article{Nalci2019,
			title={Nuisance effects and the limitations of nuisance regression in dynamic functional connectivity fMRI},
			author={Nalci, Alican and Rao, Bhaskar D and Liu, Thomas T},
			journal={NeuroImage},
			volume={184},
			pages={1005--1031},
			year={2019},
			publisher={Elsevier}
			}

			@article{Kottaram2018,
			title={Spatio-temporal dynamics of resting-state brain networks improve single-subject prediction of schizophrenia diagnosis},
			author={Kottaram, Akhil and Johnston, Leigh and Ganella, Eleni and Pantelis, Christos and Kotagiri, Ramamohanarao and Zalesky, Andrew},
			journal={Human brain mapping},
			volume={39},
			number={9},
			pages={3663--3681},
			year={2018},
			publisher={Wiley Online Library}
			}

			@article{Pedersen2018b,
			title={Multilayer network switching rate predicts brain performance},
			author={Pedersen, Mangor and Zalesky, Andrew and Omidvarnia, Amir and Jackson, Graeme D},
			journal={Proceedings of the National Academy of Sciences},
			volume={115},
			number={52},
			pages={13376--13381},
			year={2018},
			publisher={National Acad Sciences}
			}

			@article{Liegeois2019,
			title={Resting brain dynamics at different timescales capture distinct aspects of human behavior},
			author={Li{\'e}geois, Rapha{\"e}l and Li, Jingwei and Kong, Ru and Orban, Csaba and Van De Ville, Dimitri and Ge, Tian and Sabuncu, Mert R and Yeo, BT Thomas},
			journal={Nature communications},
			volume={10},
			number={1},
			pages={2317},
			year={2019},
			publisher={Nature Publishing Group}
			}

			@article{Lydon2019,
			title={Evaluation of confound regression strategies for the mitigation of micromovement artifact in studies of dynamic resting-state functional connectivity and multilayer network modularity},
			author={Lydon-Staley, David M and Ciric, Rastko and Satterthwaite, Theodore D and Bassett, Danielle S},
			journal={Network Neuroscience},
			volume={3},
			number={2},
			pages={427--454},
			year={2019},
			publisher={MIT Press}
			}

			@article{Zhu2018,
			title={Altered spatial and temporal concordance among intrinsic brain activity measures in schizophrenia},
			author={Zhu, Jiajia and Zhu, Dao-min and Qian, Yinfeng and Li, Xiaohu and Yu, Yongqiang},
			journal={Journal of psychiatric research},
			volume={106},
			pages={91--98},
			year={2018},
			publisher={Elsevier}
			}

			@article{Stevner2019,
			title={Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep},
			author={Stevner, ABA and Vidaurre, DIEGO and Cabral, Joana and Rapuano, K and Nielsen, Soren Fons Vind and Tagliazucchi, Enzo and Laufs, Helmut and Vuust, Peter and Deco, Gustavo and Woolrich, MW and others},
			journal={Nature communications},
			volume={10},
			number={1},
			pages={1035},
			year={2019},
			publisher={Nature Publishing Group}
			}

			@article{Orio2018,
			title={Chaos versus noise as drivers of multistability in neural networks},
			author={Orio, Patricio and Gatica, Marilyn and Herzog, Rub{\'e}n and Maidana, Jean Paul and Castro, Samy and Xu, Kesheng},
			journal={Chaos: An Interdisciplinary Journal of Nonlinear Science},
			volume={28},
			number={10},
			pages={106321},
			year={2018},
			publisher={AIP Publishing}
			}

			@article{Schwab2018,
			title={Directed functional connectivity using dynamic graphical models},
			author={Schwab, Simon and Harbord, Ruth and Zerbi, Valerio and Elliott, Lloyd and Afyouni, Soroosh and Smith, Jim Q and Woolrich, Mark W and Smith, Stephen M and Nichols, Thomas E},
			journal={NeuroImage},
			volume={175},
			pages={340--353},
			year={2018},
			publisher={Elsevier}
			}

			@article{Zoller2019,
			title={Large-scale brain network dynamics provide a measure of psychosis and anxiety in 22q11. 2 deletion syndrome},
			author={Z{\"o}ller, Daniela and Sandini, Corrado and Karahano{\u{g}}lu, Fikret I{\c{s}}ik and Padula, Maria Carmela and Schaer, Marie and Eliez, Stephan and Van De Ville, Dimitri},
			journal={Biological Psychiatry: Cognitive Neuroscience and Neuroimaging},
			year={2019},
			publisher={Elsevier}
			}

			@article{Tuleasca2019,
			title={Normalization of aberrant pretherapeutic dynamic functional connectivity of extrastriate visual system in patients who underwent thalamotomy with stereotactic radiosurgery for essential tremor: a resting-state functional MRI study},
			author={Tuleasca, Constantin and Bolton, Thomas AW and R{\'e}gis, Jean and Najdenovska, Elena and Witjas, Tatiana and Girard, Nadine and Delaire, Francois and Vincent, Marion and Faouzi, Mohamed and Thiran, Jean-Philippe and others},
			journal={Journal of neurosurgery},
			volume={1},
			number={aop},
			pages={1--10},
			year={2019},
			publisher={American Association of Neurological Surgeons}
			}

			@article{Zoller2019b,
			title={Structural control energy of resting-state functional brain states reveals inefficient brain dynamics in psychosis vulnerability.},
			author={Zoeller, Daniela and Sandini, Corrado and Schaer, Marie and Eliez, Stephan and Bassett, Danielle and Van De Ville, Dimitri},
			journal={bioRxiv},
			pages={703561},
			year={2019},
			publisher={Cold Spring Harbor Laboratory}
			}

			@article{Zoller2018,
			title={Robust recovery of temporal overlap between network activity using transient-informed spatio-temporal regression},
			author={Z{\"o}ller, Daniela M and Bolton, Thomas AW and Karahano{\u{g}}lu, Fikret I{\c{s}}ik and Eliez, Stephan and Schaer, Marie and Van De Ville, Dimitri},
			journal={IEEE transactions on medical imaging},
			volume={38},
			number={1},
			pages={291--302},
			year={2018},
			publisher={IEEE}
			}

			@article{Vidaurre2018,
			title={Discovering dynamic brain networks from big data in rest and task},
			author={Vidaurre, Diego and Abeysuriya, Romesh and Becker, Robert and Quinn, Andrew J and Alfaro-Almagro, Fidel and Smith, Stephen M and Woolrich, Mark W},
			journal={Neuroimage},
			volume={180},
			pages={646--656},
			year={2018},
			publisher={Elsevier}
			}

			@article{Geniesse2019,
			title={Generating dynamical neuroimaging spatiotemporal representations (DyNeuSR) using topological data analysis},
			author={Geniesse, Caleb and Sporns, Olaf and Petri, Giovanni and Saggar, Manish},
			journal={Network Neuroscience},
			pages={1--16},
			year={2019},
			publisher={MIT Press}
			}

			@inproceedings{Li2018,
			title={Estimating Interactions of Functional Brain Connectivity by Hidden Markov Models},
			author={Li, Xingjuan and Li, Yu and Cui, Jiangtao},
			booktitle={International Conference on Advanced Data Mining and Applications},
			pages={403--412},
			year={2018},
			organization={Springer}
			}

			@article{Casorso2019,
			title={Dynamic mode decomposition of resting-state and task fMRI},
			author={Casorso, Jeremy and Kong, Xiaolu and Chi, Wang and Van De Ville, Dimitri and Yeo, BT Thomas and Liegeois, Raphael},
			journal={Neuroimage},
			volume={194},
			pages={42--54},
			year={2019},
			publisher={Elsevier}
			}

			@article{Fukushima2018c,
			title={Comparison of fluctuations in global network topology of modeled and empirical brain functional connectivity},
			author={Fukushima, Makoto and Sporns, Olaf},
			journal={PLoS computational biology},
			volume={14},
			number={9},
			pages={e1006497},
			year={2018},
			publisher={Public Library of Science}
			}

			@article{Iraji2019,
			title={Spatial dynamics within and between brain functional domains: A hierarchical approach to study time-varying brain function},
			author={Iraji, Armin and Fu, Zening and Damaraju, Eswar and DeRamus, Thomas P and Lewis, Noah and Bustillo, Juan R and Lenroot, Rhoshel K and Belger, Aysneil and Ford, Judith M and McEwen, Sarah and others},
			journal={Human brain mapping},
			volume={40},
			number={6},
			pages={1969--1986},
			year={2019},
			publisher={Wiley Online Library}
			}

			@article{Kottaram2019,
			author = {Kottaram, Akhil and Johnston, Leigh A. and Cocchi, Luca and Ganella, Eleni P. and Everall, Ian and Pantelis, Christos and Kotagiri, Ramamohanarao and Zalesky, Andrew},
			title = {Brain network dynamics in schizophrenia: Reduced dynamism of the default mode network},
			journal = {Human Brain Mapping},
			volume = {40},
			number = {7},
			pages = {2212-2228},
			keywords = {default mode network, hidden Markov model, neural dynamics, resting state fMRI, resting state networks, schizophrenia},
			doi = {10.1002/hbm.24519},
			url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/hbm.24519},
			eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/hbm.24519},
			abstract = {Abstract Complex human behavior emerges from dynamic patterns of neural activity that transiently synchronize between distributed brain networks. This study aims to model the dynamics of neural activity in individuals with schizophrenia and to investigate whether the attributes of these dynamics associate with the disorder's behavioral and cognitive deficits. A hidden Markov model (HMM) was inferred from resting-state functional magnetic resonance imaging (fMRI) data that was temporally concatenated across individuals with schizophrenia (nâ=â41) and healthy comparison individuals (nâ=â41). Under the HMM, fluctuations in fMRI activity within 14 canonical resting-state networks were described using a repertoire of 12 brain states. The proportion of time spent in each state and the mean length of visits to each state were compared between groups, and canonical correlation analysis was used to test for associations between these state descriptors and symptom severity. Individuals with schizophrenia activated default mode and executive networks for a significantly shorter proportion of the 8-min acquisition than healthy comparison individuals. While the default mode was activated less frequently in schizophrenia, the duration of each activation was on average 4â5âs longer than the comparison group. Severity of positive symptoms was associated with a longer proportion of time spent in states characterized by inactive default mode and executive networks, together with heightened activity in sensory networks. Furthermore, classifiers trained on the state descriptors predicted individual diagnostic status with an accuracy of 76â85\%.},
			year = {2019}
			}

			@article{Damaraju2018b,
			title={Connectivity dynamics from wakefulness to sleep},
			author={Damaraju, Eswar and Tagliazucchi, Enzo and Laufs, Helmut and Calhoun, Vince},
			journal={bioRxiv},
			pages={380741},
			year={2018},
			publisher={Cold Spring Harbor Laboratory}
			}

			@article{Ryyppo2018,
			title={Regions of Interest as nodes of dynamic functional brain networks},
			author={Ryypp{\"o}, Elisa and Glerean, Enrico and Brattico, Elvira and Saram{\"a}ki, Jari and Korhonen, Onerva},
			journal={Network Neuroscience},
			volume={2},
			number={4},
			pages={513--535},
			year={2018},
			publisher={MIT Press}
			}

			@article{Gaviria2019,
			title={Brain functional connectivity dynamics in the aftermaths of affective and cognitive events},
			author={Gaviria, Julian and Rey, Gwladys and Bolton, Thomas and Delgado, Jaime and Van de Ville, Dimitri and Vuilleumier, Patrik},
			journal={bioRxiv},
			pages={685396},
			year={2019},
			publisher={Cold Spring Harbor Laboratory}
			}

			@article{Honari2019,
			title={Investigating the impact of autocorrelation on time-varying connectivity},
			author={Honari, Hamed and Choe, Ann S and Pekar, James J and Lindquist, Martin A},
			journal={NeuroImage},
			volume={197},
			pages={37--48},
			year={2019},
			publisher={Elsevier}
			}

			@article{Mokhtari2019,
			title={Dynamic Functional Magnetic Resonance Imaging Connectivity Tensor Decomposition: A New Approach to Analyze and Interpret Dynamic Brain Connectivity},
			author={Mokhtari, Fatemeh and Laurienti, Paul J and Rejeski, W Jack and Ballard, Grey},
			journal={Brain connectivity},
			volume={9},
			number={1},
			pages={95--112},
			year={2019},
			}

			@article{Singh2019,
			title={Individualized Dynamic Brain Models: Estimation and Validation with Resting-State fMRI},
			author={Singh, Matthew and Braver, Todd and Cole, Michael and Ching, ShiNung},
			journal={bioRxiv},
			pages={678243},
			year={2019},
			publisher={Cold Spring Harbor Laboratory}
			}

			@article{Miller2019,
			title={Whole Brain Polarity Regime Dynamics are Significantly Disrupted in Schizophrenia and Correlate Strongly with Network Connectivity Measures},
			author={Miller, Robyn and Pearlson, Godfrey and Calhoun, Vince},
			journal={bioRxiv},
			pages={543751},
			year={2019},
			publisher={Cold Spring Harbor Laboratory}
			}

			@article{Gilson2019,
			title={MOU-EC: model-based whole-brain effective connectivity to extract biomarkers for brain dynamics from fMRI data and study distributed cognition},
			author={Gilson, Matthieu and Zamora-Lopez, Gorka and Pallares, Vicente and Adhikari, Mohit H and Senden, Mario and Campo, Adria Tauste and Mantini, Dante and Corbetta, Maurizio and Deco, Gustavo and Insabato, Andrea},
			journal={bioRxiv},
			pages={531830},
			year={2019},
			publisher={Cold Spring Harbor Laboratory}
			}

			@article{Menon2019,
			title={A Comparison of Static and Dynamic Functional Connectivities for Identifying Subjects and Biological Sex Using Intrinsic Individual Brain Connectivity},
			author={Menon, Sreevalsan S and Krishnamurthy, K},
			journal={Scientific reports},
			volume={9},
			number={1},
			pages={5729},
			year={2019},
			publisher={Nature Publishing Group}
			}

			@article{Cao2019,
			title={Time-dependent Canonical Correlation Analysis for Multilevel Time Series},
			author={Cao, Xuefei and Ke, Jun and Sandstede, Bj{\"o}rn and Luo, Xi},
			journal={bioRxiv},
			pages={650101},
			year={2019},
			publisher={Cold Spring Harbor Laboratory}
			}

			@article{Ochab2019,
			title={On the pros and cons of using temporal derivatives to assess brain functional connectivity},
			author={Ochab, Jeremi K and Tarnowski, Wojciech and Nowak, Maciej A and Chialvo, Dante R},
			journal={NeuroImage},
			volume={184},
			pages={577--585},
			year={2019},
			publisher={Elsevier}
			}

			@article{Zou2019b,
			title={Multi-frequency Dynamic Weighted Functional Connectivity Networks for Schizophrenia Diagnosis},
			author={Zou, Hongliang and Yang, Jian},
			journal={Applied Magnetic Resonance},
			volume={50},
			number={7},
			pages={847--859},
			year={2019},
			publisher={Springer}
			}

			@article{Iraji2019b,
			title={The spatial chronnectome reveals a dynamic interplay between functional segregation and integration},
			author={Iraji, Armin and Deramus, Thomas P and Lewis, Noah and Yaesoubi, Maziar and Stephen, Julia M and Erhardt, Erik and Belger, Aysneil and Ford, Judith M and McEwen, Sarah and Mathalon, Daniel H and others},
			journal={Human brain mapping},
			year={2019},
			publisher={Wiley Online Library}
			}

			@article{Savva2019,
			title={Assessment of dynamic functional connectivity in resting-state fMRI using the sliding window technique},
			author={Savva, Antonis D and Mitsis, Georgios D and Matsopoulos, George K},
			journal={Brain and behavior},
			volume={9},
			number={4},
			pages={e01255},
			year={2019},
			publisher={Wiley Online Library}
			}

			@article{Surampudi2019,
			title={Resting state dynamics meets anatomical structure: Temporal multiple kernel learning (tMKL) model},
			author={Surampudi, Sriniwas Govinda and Misra, Joyneel and Deco, Gustavo and Bapi, Raju Surampudi and Sharma, Avinash and Roy, Dipanjan},
			journal={NeuroImage},
			volume={184},
			pages={609--620},
			year={2019},
			publisher={Elsevier}
			}

			@article{Zhang2018,
			title={Test-retest reliability of dynamic functional connectivity in resting state fMRI},
			author={Zhang, Chao and Baum, Stefi A and Adduru, Viraj R and Biswal, Bharat B and Michael, Andrew M},
			journal={NeuroImage},
			volume={183},
			pages={907--918},
			year={2018},
			publisher={Elsevier}
			}

			@article{Park2018,
			title={Dynamic effective connectivity in resting state fMRI},
			author={Park, Hae-Jeong and Friston, Karl J and Pae, Chongwon and Park, Bumhee and Razi, Adeel},
			journal={Neuroimage},
			volume={180},
			pages={594--608},
			year={2018},
			publisher={Elsevier}
			}

			@inproceedings{Nikolau2018,
			title={Investigation Of Interaction Between Physiological Signals And fMRI Dynamic Functional Connectivity Using Independent Component Analysis},
			author={Nikolaou, Foivia and Orphanidou, Christina and Murphy, Kevin and Wise, Richard G and Mitsis, Georgios D},
			booktitle={2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
			pages={1019--1023},
			year={2018},
			organization={IEEE}
			}

			@article{Patanaik2018,
			title={Dynamic functional connectivity and its behavioral correlates beyond vigilance},
			author={Patanaik, Amiya and Tandi, Jesisca and Ong, Ju Lynn and Wang, Chenhao and Zhou, Juan and Chee, Michael WL},
			journal={NeuroImage},
			volume={177},
			pages={1--10},
			year={2018},
			publisher={Elsevier}
			}

			@article{Lewis2019,
			title={Static and Dynamic Functional Connectivity Analysis of Cerebrovascular Reactivity: An fMRI Study},
			author={Lewis, Noah and Lu, Hanzhang and Liu, Peiying and Hou, Xirui and Damaraju, Eswar and Iraji, Armin and Calhoun, Vince},
			journal={BioRxiv},
			pages={580621},
			year={2019},
			publisher={Cold Spring Harbor Laboratory}
			}

			@article{Mash2018,
			title={Multimodal approaches to functional connectivity in autism spectrum disorders: an integrative perspective},
			author={Mash, Lisa E and Reiter, Maya A and Linke, Annika C and Townsend, Jeanne and M{\"u}ller, Ralph-Axel},
			journal={Developmental neurobiology},
			volume={78},
			number={5},
			pages={456--473},
			year={2018},
			publisher={Wiley Online Library}
			}

			@article{Nielsen2018,
			title={Predictive assessment of models for dynamic functional connectivity},
			author={Nielsen, S{\o}ren FV and Schmidt, Mikkel N and Madsen, Kristoffer H and M{\o}rup, Morten},
			journal={Neuroimage},
			volume={171},
			pages={116--134},
			year={2018},
			publisher={Elsevier}
			}

			@inproceedings{Nielsen2018b,
			title={Evaluating models of dynamic functional connectivity using predictive classification accuracy},
			author={Nielsen, S{\o}ren F{\o}ns Vind and Levin-Schwartz, Yuri and Vidaurre, Diego and Adali, Tulay and Calhoun, Vince D and Madsen, Kristoffer H and Hansen, Lars Kai and M{\o}rup, Morten},
			booktitle={2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
			pages={2566--2570},
			year={2018},
			organization={IEEE}
			}

			@article{Diez2018,
			title={Dynamic functional connectivity in Parkinsons disease patients with mild cognitive impairment and normal cognition},
			author={D{\'\i}ez-Cirarda, Mar{\'\i}a and Strafella, Antonio P and Kim, Jinhee and Pe{\~n}a, Javier and Ojeda, Natalia and Cabrera-Zubizarreta, Alberto and Ibarretxe-Bilbao, Naroa},
			journal={NeuroImage: Clinical},
			volume={17},
			pages={847--855},
			year={2018},
			publisher={Elsevier}
			}

			@article{Kafashan2018,
			title={Dimensionality reduction impedes the extraction of dynamic functional connectivity states from fmri recordings of resting wakefulness},
			author={Kafashan, MohammadMehdi and Palanca, Ben Julian A and Ching, ShiNung},
			journal={Journal of neuroscience methods},
			volume={293},
			pages={151--161},
			year={2018},
			publisher={Elsevier}
			}

			@article{Shappell2019,
			title={Improved state change estimation in dynamic functional connectivity using hidden semi-Markov models},
			author={Shappell, Heather and Caffo, Brian S and Pekar, James J and Lindquist, Martin A},
			journal={NeuroImage},
			volume={191},
			pages={243--257},
			year={2019},
			publisher={Elsevier}
			}

			@inproceedings{Yang2018,
			title={Independency of Functional Connectivity States on Spatiotemporal Resolution of fMRI Data},
			author={Yang, Yuyuan and Shen, Hui and Luo, Zhiguo and Li, Fuquan and Hu, Dewen},
			booktitle={International Conference on Cognitive Systems and Signal Processing},
			pages={370--380},
			year={2018},
			organization={Springer}
			}

			@article{Osborn2018,
			title={Decreased Dynamic Flexibility of Brain Functional Connectivity in Prodromal Alzheimers Disease},
			author={Osborn, Zachary G and Risacher, Shannon L and West, John D and Tallman, Eileen and Apostolova, Liana and Sporns, Olaf and Saykin, Andrew J},
			journal={Proceedings of IMPRS},
			volume={1},
			number={1},
			year={2018}
			}

			@inproceedings{Nielsen2018c,
			title={Testing group differences in state transition structure of dynamic functional connectivity models},
			author={Nielsen, S{\o}ren FV and Vidaurre, Diego and Madsen, Kristoffer H and Schmidt, Mikkel N and M{\o}rup, Morten},
			booktitle={2018 International Workshop on Pattern Recognition in Neuroimaging (PRNI)},
			pages={1--4},
			year={2018},
			organization={IEEE}
			}

			@article{Park2018b,
			title={Dynamic functional connectivity analysis reveals improved association between brain networks and eating behaviors compared to static analysis},
			author={Park, Bo-yong and Moon, Taesup and Park, Hyunjin},
			journal={Behavioural brain research},
			volume={337},
			pages={114--121},
			year={2018},
			publisher={Elsevier}
			}

			@article{Gilson2019b,
			title={Network analysis of whole-brain fMRI dynamics: A new framework based on dynamic communicability},
			author={Gilson, Matthieu and Kouvaris, Nikos E and Deco, Gustavo and Mangin, Jean-Francois and Poupon, Cyril and Lefranc, Sandrine and Riviere, Denis and Zamora-Lopez, Gorka},
			journal={bioRxiv},
			pages={421883},
			year={2019},
			publisher={Cold Spring Harbor Laboratory}
			}

			@article{Gilson2016,
			title={Estimation of directed effective connectivity from fMRI functional connectivity hints at asymmetries of cortical connectome},
			author={Gilson, Matthieu and Moreno-Bote, Ruben and Ponce-Alvarez, Adri{\'a}n and Ritter, Petra and Deco, Gustavo},
			journal={PLoS computational biology},
			volume={12},
			number={3},
			pages={e1004762},
			year={2016},
			publisher={Public Library of Science}
			}

			@article{Gonzalez2019,
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