diff --git a/101915/Functional/Movement_Regressors.txt b/101915/Functional/Movement_Regressors.txt
new file mode 100644
index 0000000..83a39e2
--- /dev/null
+++ b/101915/Functional/Movement_Regressors.txt
@@ -0,0 +1,1200 @@
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+0.25531 0.58204 1.1343 0.001752 0.009243 -0.004798
+0.15344 0.56134 1.1232 0.001653 0.009029 -0.00443
+0.12026 0.5456 1.0752 0.002465 0.009006 -0.00428
+0.17446 0.56313 1.0707 0.002874 0.009064 -0.004336
+0.28385 0.57157 1.2054 0.001068 0.009664 -0.004657
+0.26964 0.57426 1.2103 0.000271 0.009638 -0.004454
+0.1942 0.55934 1.1636 0.001227 0.009176 -0.004274
+0.18195 0.55576 1.0857 0.002459 0.009273 -0.004234
+0.2694 0.57005 1.1619 0.001303 0.009588 -0.004324
+0.32449 0.58314 1.2575 0.00036299 0.009504 -0.004453
+0.25844 0.58627 1.2519 0.000331 0.00962 -0.004661
+0.1911 0.58321 1.2079 0.00135 0.009306 -0.004531
+0.21005 0.56902 1.1849 0.001577 0.009778 -0.004391
+0.27975 0.58399 1.243 0.001134 0.009976 -0.004318
+0.29037 0.59149 1.2856 0.000443 0.010042 -0.00445
+0.20577 0.58501 1.2424 0.001269 0.009672 -0.004585
+0.17896 0.57838 1.1851 0.001803 0.009184 -0.004335
+0.23358 0.57258 1.2166 0.001268 0.009793 -0.004457
+0.32183 0.59445 1.289 0.000429 0.010051 -0.004383
+0.2798 0.59147 1.2994 0.000267 0.009967 -0.004508
+0.2031 0.57996 1.2516 0.001519 0.00978 -0.004469
+0.20141 0.58331 1.1836 0.002036 0.009268 -0.004365
+0.26259 0.58681 1.2441 0.00148 0.008951 -0.004482
+0.31323 0.59662 1.3125 0.000273 0.00971 -0.004629
+0.2541 0.59155 1.3154 0.000402 0.009815 -0.004429
+0.16886 0.59808 1.2391 0.001162 0.009007 -0.004244
+0.17459 0.56677 1.1775 0.002249 0.009127 -0.004161
+0.28951 0.59243 1.2807 0.00124 0.009648 -0.004091
+0.3167 0.5995 1.3036 0.000419 0.009945 -0.004108
+0.23828 0.5932 1.2894 0.001181 0.009656 -0.004109
+0.20894 0.58654 1.2634 0.002076 0.009498 -0.003992
+0.2531 0.59019 1.2618 0.001962 0.009585 -0.003883
+0.33223 0.61046 1.3228 0.00069 0.009957 -0.004066
+0.32659 0.6099 1.3343 0.00021 0.010049 -0.003896
+0.23394 0.60653 1.2962 0.001454 0.009684 -0.004121
+0.22822 0.59044 1.2699 0.001875 0.00985 -0.003943
+0.27827 0.59632 1.2885 0.001531 0.009909 -0.003667
+0.32767 0.61079 1.3521 0.00023501 0.010012 -0.003771
+0.30307 0.60939 1.3695 -0.000111 0.010001 -0.003719
+0.21983 0.61098 1.3192 0.00107 0.009632 -0.004013
+0.24907 0.59824 1.3128 0.001443 0.00981 -0.003779
+0.30171 0.60772 1.3476 0.000605 0.009947 -0.003794
+0.35418 0.61781 1.3849 -0.00013299 0.009988 -0.003727
+0.28209 0.61588 1.3622 0.000172 0.009842 -0.003675
+0.22917 0.61342 1.3054 0.001488 0.009508 -0.003582
+0.27391 0.59513 1.2917 0.001337 0.009904 -0.003386
+0.34547 0.60544 1.3781 0.000362 0.009814 -0.003714
+0.35079 0.61581 1.3834 -0.00012701 0.009664 -0.003666
+0.28022 0.60841 1.354 0.000738 0.00945 -0.003644
+0.26285 0.60156 1.3213 0.001433 0.009728 -0.003582
+0.29669 0.60513 1.3401 0.001437 0.009751 -0.003553
+0.36463 0.62264 1.4055 8.1001e-05 0.009819 -0.003579
+0.32988 0.62286 1.3996 -7.1e-05 0.009626 -0.003555
+0.26844 0.61718 1.3724 0.00097499 0.009543 -0.003599
+0.28525 0.61285 1.3628 0.001664 0.00967 -0.003556
+0.31133 0.61627 1.3665 0.001372 0.009782 -0.003498
+0.37392 0.62447 1.4066 0.00016699 0.009702 -0.0036
+0.33163 0.62244 1.404 -0.00035599 0.009612 -0.003661
+0.29212 0.62232 1.3671 0.001029 0.009432 -0.003515
+0.27666 0.60829 1.3662 0.001332 0.00948 -0.003533
+0.30628 0.60147 1.3646 0.00082701 0.009574 -0.003422
+0.37626 0.62003 1.4184 0.00015401 0.009733 -0.003351
+0.37851 0.62713 1.4418 -0.000297 0.009691 -0.003262
+0.31629 0.63278 1.4176 -0.00011299 0.009062 -0.003202
+0.28836 0.59923 1.3599 0.00079001 0.00958 -0.003181
+0.34187 0.60837 1.385 0.000342 0.009752 -0.003178
+0.41177 0.6284 1.441 -0.000197 0.00963 -0.003231
+0.33514 0.60765 1.4228 -0.000264 0.009123 -0.003148
+0.2844 0.60205 1.3641 0.000856 0.009465 -0.003114
+0.33699 0.60537 1.3741 0.000521 0.009645 -0.003023
+0.42059 0.62104 1.45 -0.00047499 0.009602 -0.003036
+0.36527 0.62621 1.4487 0 0.009227 -0.003157
+0.281 0.62067 1.4314 0 0.008886 -0.003045
+0.30957 0.60423 1.3908 0.001005 0.009251 -0.003
+0.34716 0.60805 1.4239 0 0.009217 -0.002934
+0.42369 0.62676 1.4846 -0.00018 0.008678 -0.003025
+0.38676 0.61662 1.4811 -0.00023901 0.008897 -0.002366
+0.31084 0.60334 1.4232 0 0.008545 -0.002898
+0.32581 0.59529 1.3969 0 0.009327 -0.002853
diff --git a/CsfMask_07_121x145x121.nii b/CsfMask_07_121x145x121.nii
new file mode 100644
index 0000000..b4c36b7
Binary files /dev/null and b/CsfMask_07_121x145x121.nii differ
diff --git a/Script_Preprocessing_HCP.m b/Script_Preprocessing_HCP.m
index 3325519..0b5149c 100644
--- a/Script_Preprocessing_HCP.m
+++ b/Script_Preprocessing_HCP.m
@@ -1,474 +1,472 @@
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 % Authors: Thomas Bolton and Mert Inan
 % Date: 03/08/2017 -
 % Description:
 % This script will perform classical preprocessing steps on the HCP data in
 % order to analyse it in different ways:
 %
 % - Through regional sparse coupled hidden Markov models
 % - Through co-activation pattern analysis
 % - Through variance-related assessment of motion
 % - Through Total activation on graphs
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 %% 1. Setting up path files
 
 % Adds the shared folder to do all we want: contains SPM8
 PathSPM = '/media/miplab-nas3/Code-NAS1/shared/static_FC_pipeline/spm8';
 PathPrepro = '/media/miplab-nas3/Code-NAS1/projects/ABIDE/preprocessing_Thomas/MyPreprocessing';
 PathDPARSFA = '/media/miplab-nas3/Code-NAS1/shared/static_FC_pipeline/DPARSF_V2.3_130615';
 
 % If the 'server link' exists, we add the path. Else, we add the computer
 % path (with Volumes)
 try
     addpath(genpath(PathSPM));
 catch
     addpath(genpath('/Volumes/Code-NAS1/shared/static_FC_pipeline/spm8'));
 end
 
 try
     addpath(genpath(PathPrepro));
 catch
     addpath(genpath('/Volumes/Code-NAS1/projects/ABIDE/preprocessing_Thomas/MyPreprocessing'));
 end
 
 try
     addpath(genpath(PathDPARSFA));
 catch
     addpath(genpath('/Volumes/Code-NAS1/shared/static_FC_pipeline/DPARSF_V2.3_130615'));
 end
 
 % ------------- DATA PATH TO READ FROM -------------
 % **** Changed from /media/miplab-nas2/data/HCP_Data_TA' ****
 % Need to check that writing to this path is not done!
 readPath = '/media/miplab-nas3/HCP-Data/HCP_Giulia';
 
 % ------------- DATA PATH TO WRITE TO -------------
 writePath = '/media/miplab-nas3/Data/Regional_SCHMM';
 
 
 %% 2. Setting up options
 
 % We change directory towards where we store the RS data
 cd(readPath); 
 
 % Choose the wave of the subjects
 wave = '1st_wave_90subjs'; %Change loop cycle count when you change this
 %wave ='2nd_wave_158subjs';
 
 % Indices of the subjects to preprocess
 pathToSubjectsFunc = fullfile(readPath, 'Preproc_sFC', wave);
 pathToSubjectsStruct = fullfile(readPath,'Preproc_sFC',wave);
 d = dir(pathToSubjectsFunc); 
 d = d(4:end,:);
 
 % Type of acquisition for the subjects (RL or LR): for this data, we only
 % have the LR session available for now
 d2 = 'rfMRI_REST1_LR';
 %d2 = 'rfMRI_REST1_RL';
 
 % Number of scans to remove
 removeFirstNscans = 10;
 
 %% 3. Preprocessing itself
 
 % I iterate through all the HCP folders and create respective folders in
 % the write directory
 for i = 1:158
     try
         disp(['Subject ',d(i).name,'...']);
 
         %Creating a folder in the write directory
         if isequal(d2,'rfMRI_REST1_RL')
             writeFolder = [d(i).name,'_RL'];  %Change according to d2
         else
             writeFolder = d(i).name; 
         end
         cd(writePath);
         mkdir(writeFolder);
         cd(writeFolder);
-        mkdir('Structural');
+        %mkdir('Structural');
         mkdir('Functional');
+        mkdir('CAPs');
+        mkdir('VAR');
+        mkdir('SCHMM');
         cd(readPath);
 
         % READ Structural and functional paths
-        structPath = fullfile(pathToSubjectsStruct,d(i).name,'T1');
+        %structPath = fullfile(pathToSubjectsStruct,d(i).name,'T1');
         functPath = fullfile(pathToSubjectsFunc,d(i).name,'fMRI');
         
         % WRITE Structural and functional paths
-        structWritePath = fullfile(writePath,writeFolder,'Structural');
+        %structWritePath = fullfile(writePath,writeFolder,'Structural');
         functWritePath = fullfile(writePath,writeFolder,'Functional');
 
         % Reads and modifies the text file to have 594 rows and to have proper
         % units, then copies it from read location to write location.
         movText = textread(fullfile(functPath,'rpMovement_Regressors.txt'));
 
         %Changes degrees to radians
-        movText = movText(1:594,1:6);
+        movText = movText(:,1:6);
         for l = 4:6
             movText(:,l) = movText(:,l)*pi/180;
         end
 
         dlmwrite(fullfile(functWritePath,'Movement_Regressors.txt'),movText,' ');
 
 
 
     %% Fine-Tuning WM and CSF Masks for Each Volume
 
         % Gets one volume only
         [fMeanFN_noPath, foosubdirs] = spm_select('List',functPath,['^' 'frf' '.*\.nii$']);
 
         % Gets the path and the volume itself
-        fMean_fn=fullfile(functWritePath,fMeanFN_noPath(1,:));
+        fMean_fn=fullfile(functPath,fMeanFN_noPath(1,:));
 
         % Transforms the WM and CSF masks from the toolbox into native
         % resolution
         Masks={'WhiteMask_09_121x145x121.nii','CsfMask_07_121x145x121.nii'};
 
         % I must convert the masks to the right resolution
         for j=1:length(Masks)
             tmp=which(Masks{j});
 
             if j == 1
                 WMmaskLidx = mapVolumeToVolume_TB(tmp,fMean_fn);
             else
                 CSFmaskLidx = mapVolumeToVolume_TB(tmp,fMean_fn);
             end
         end
 
 
         %% 5. EXTRACT FMRI TIMECOURSES
         disp(['Subject ',d(i).name,'...']);
         disp('Extracting time courses...');
 
         % Names of all the functional files
         fVolsFNlist_VX=getImageFNinAcqOrder_TB(functPath,'s5frf','nii',[]);
         fVolsFNlist_fullpath_VX=cellfun(@(x) fullfile(functPath,x), fVolsFNlist_VX,'UniformOutput',false);
 
         % Same for unsmoothed data (needed because we want to average WM and
         % CSF signals on unsmoothed data)
         fVolsFNlist_VX_UNSMOOTHED=getImageFNinAcqOrder_TB(functPath,'frf','nii',[]);
         fVolsFNlist_fullpath_VX_UNSMOOTHED=cellfun(@(x) fullfile(functPath,x), fVolsFNlist_VX_UNSMOOTHED,'UniformOutput',false);
 
         % read all headers and files in temporal order into a 4-D array
         V0i_VX=spm_vol(fVolsFNlist_fullpath_VX);
     
         if removeFirstNscans~=0
             V0i_VX=V0i_VX((removeFirstNscans+1):end);
         end
 
         % Same for unsmoothed
         V0i_VX_UNSMOOTHED=spm_vol(fVolsFNlist_fullpath_VX_UNSMOOTHED);
 
         if removeFirstNscans~=0
             V0i_VX_UNSMOOTHED=V0i_VX_UNSMOOTHED((removeFirstNscans+1):end);
         end
 
         % Indices of the frames of interest (common to smoothed and unsmoothed
         % cases
         V0idx=1:length(V0i_VX_UNSMOOTHED);
 
         % Creates the 4D volumes
         V0_VX=zeros(V0i_VX{1}.dim(1),V0i_VX{1}.dim(2),V0i_VX{1}.dim(3),length(V0i_VX),'single');
     
         for iter=1:length(V0i_VX)
             V0_VX(:,:,:,V0idx(iter))=spm_read_vols(V0i_VX{iter});
         end
 
         V0_VX_UNSMOOTHED=zeros(V0i_VX_UNSMOOTHED{1}.dim(1),V0i_VX_UNSMOOTHED{1}.dim(2),V0i_VX_UNSMOOTHED{1}.dim(3),length(V0i_VX_UNSMOOTHED),'single');
 
         for iter=1:length(V0i_VX_UNSMOOTHED)
             V0_VX_UNSMOOTHED(:,:,:,V0idx(iter))=spm_read_vols(V0i_VX_UNSMOOTHED{iter});
         end
 
         %% 6. DETRENDING
         disp(['Subject ',d(i).name,'...']);
         disp('Detrending...');
 
         AllVolume_VX=reshape(V0_VX,[],size(V0_VX,4))'; % time x space
 
         AllVolume_VX_UNSMOOTHED=reshape(V0_VX_UNSMOOTHED,[],size(V0_VX_UNSMOOTHED,4))'; % time x space
 
         CUTNUMBER=10; % cut space in parts
 
-        SegmentLength = ceil(size(AllVolume_VX_UNSMOOTHED,2) / CUTNUMBER);
+        SegmentLength = ceil(size(AllVolume_VX,2) / CUTNUMBER);
 
         for iCut=1:CUTNUMBER
             if iCut~=CUTNUMBER
                 Segment = (iCut-1)*SegmentLength+1 : iCut*SegmentLength;
             else
                 Segment = (iCut-1)*SegmentLength+1 : size(AllVolume_VX_UNSMOOTHED,2);
             end
 
             AllVolume_VX(:,Segment) = detrend(AllVolume_VX(:,Segment)); 
             AllVolume_VX_UNSMOOTHED(:,Segment) = detrend(AllVolume_VX_UNSMOOTHED(:,Segment)); 
         end
 
         V0_VX=reshape(AllVolume_VX',size(V0_VX,1),size(V0_VX,2),size(V0_VX,3),size(V0_VX,4));
         V0_VX_UNSMOOTHED=reshape(AllVolume_VX_UNSMOOTHED',size(V0_VX_UNSMOOTHED,1),size(V0_VX_UNSMOOTHED,2),size(V0_VX_UNSMOOTHED,3),size(V0_VX_UNSMOOTHED,4));
 
 
 
         %% 7. COMPUTE AVG CSF TIMECOURSE   
         disp(' Computing average CSF signal...');
 
         % Returns the locations in X, Y and Z of the elements from the mask
         [Ci,Cj,Ck]=ind2sub(size(V0_VX_UNSMOOTHED(:,:,:,1)),find(CSFmaskLidx));
 
         % How many voxels
         nCSFvoxels=numel(Ci);
 
         WriteInformation_TB(fid,['Voxels CSF: ',num2str(nCSFvoxels),'\t']);
 
         % Creates the time course itself
         tcCSF=zeros(1,1,1,size(V0_VX_UNSMOOTHED,4));
         for idx=1:numel(Ci)
             tcCSF=tcCSF+V0_VX_UNSMOOTHED(Ci(idx),Cj(idx),Ck(idx),:);
         end
 
         CSFavg=squeeze(tcCSF/nCSFvoxels)';
         CSFavg_demean=CSFavg-mean(CSFavg);
         clear tcCSF;
 
 
         %% 8. COMPUTE AVG WM TIMECOURSE   
         disp(' Computing average WM signal...');
 
         [Ci,Cj,Ck]=ind2sub(size(V0_VX_UNSMOOTHED(:,:,:,1)),find(WMmaskLidx));
 
         nWMvoxels=numel(Ci); % also, sum(CSFmaskLidx)
 
         WriteInformation_TB(fid,['WM: ',num2str(nWMvoxels),'\t']);
 
         tcWM=zeros(1,1,1,size(V0_VX_UNSMOOTHED,4));
         for idx=1:numel(Ci)
             tcWM=tcWM+V0_VX_UNSMOOTHED(Ci(idx),Cj(idx),Ck(idx),:);
         end
 
         WMavg=squeeze(tcWM/nWMvoxels)';
         WMavg_demean=WMavg-mean(WMavg);
         clear tcWM;
 
 
         %% 9. Construction of the matrix of regressors
 
         fprintf('Regressing out nuisance covariates...');
 
         % Constant, linear and quadratic regressors
         X=[ones(numel(CSFavg),1) [1:numel(CSFavg)]'/numel(CSFavg) ...
             [1:numel(CSFavg)].^2'/(numel(CSFavg)^2)];
 
         % Adds the WM regressor
         X=[X,WMavg_demean'];
 
         % Adds the CSF regressor
         X=[X,CSFavg_demean'];
 
 
         %% 10. Regression itself
 
         disp('Regressing out covariates...');
 
         % Regression for both the data to atlas and the voxelwise data
         V0Cov_VX_UNSMOOTHED =zeros(size(V0_VX_UNSMOOTHED));
         V0Cov_VX =zeros(size(V0_VX_UNSMOOTHED));
 
         for I=1:size(V0_VX_UNSMOOTHED,1)
             fprintf('.');
             for j=1:size(V0_VX_UNSMOOTHED,2)
                 for k=1:size(V0_VX_UNSMOOTHED,3)
 
                     % We keep the residual from the regression process (i.e.
                     % what is left after we remove the impact from the
                     % covariates)
                     [~,res_VX] = y_regress_ss(squeeze(V0_VX(I,j,k,V0idx)),X);
                     [~,res_VX_UNSMOOTHED] = y_regress_ss(squeeze(V0_VX_UNSMOOTHED(I,j,k,V0idx)),X);
 
                     % The volumes following regression of covariates are created
                     V0Cov_VX(I,j,k,V0idx)=res_VX+mean(squeeze(V0_VX(I,j,k,V0idx)));
                     V0Cov_VX_UNSMOOTHED(I,j,k,V0idx)=res_VX_UNSMOOTHED+mean(squeeze(V0_VX_UNSMOOTHED(I,j,k,V0idx)));                   
                 end
             end
         end
 
         V0Cov_VX(isnan(V0Cov_VX))=0;
         V0Cov_VX_UNSMOOTHED(isnan(V0Cov_VX_UNSMOOTHED))=0;
 
 
         %%  12. ATLASING (on unsmoothed data)
 
         % 1. Get the proper atlas file(s): read content of atlas NIFTI file
         % (spm_vol and spm_read_vols) and then convert it into the functional data
         % resolution (mapVolumeToVolume)
         
         if isequal(d2,'rfMRI_REST1_RL')
             chosenDataFile = 'frfMRI_REST1_RL001.nii';
         else
             chosenDataFile = 'frfMRI_REST1_LR001.nii';
         end
 
         % Path of the atlas is going to be used by the mapVolumeToVolume
         % function.
-        atlasDir = '/media/miplab-nas2/Data/HCP_Data_TA/HCP_for_SCHMM/Atlases';
+        atlasDir = '/Users/TiBiUan/Desktop/Github/Regional_SCHMM/Atlases';
         
-        for m = 1:2
+        n_atlas = 4;
+        
+        atlas_names = {'Craddock_349.nii','Craddock_950.nii','AAL_90.nii','HCP-MMP1_MNI152_2mm_360_fnirt2diff.nii'};
+        
+        AllVolume_V0Cov_VX = reshape(V0Cov_VX,[],size(V0Cov_VX,4));
+            
+        AllVolume_V0Cov_VX_UNSMOOTHED = reshape(V0Cov_VX_UNSMOOTHED,[],size(V0Cov_VX_UNSMOOTHED,4));
+        
+        for m = 1:n_atlas
             
             %Selection of the number of regions in the atlas.
-            if m == 1
-                regionCnt = '300'; 
-            elseif m==2
-                regionCnt = '950'; 
+            switch m
+                case 1
+                    regionCnt = 349;
+                case 2
+                    regionCnt = 950;
+                case 3
+                    regionCnt = 90;
+                case 4
+                    regionCnt = 360;
             end
 
             %Feeding into the mapVolumeToVolume function with a chosen 3D volume 
-            mappedAtlas = mapVolumeToVolume(fullfile(atlasDir,['Craddock_',regionCnt,'.nii']),fullfile(functWritePath,chosenDataFile));  
+            mappedAtlas = mapVolumeToVolume(fullfile(atlasDir,atlas_names{m}),fullfile(functPath,chosenDataFile));  
 
             % 2. Atlasing itself with reshaped data and atlas
             AllVolume_mappedAtlas = reshape(mappedAtlas,[],1); 
-            AllVolume_V0Cov_VX = reshape(V0Cov_VX,[],size(V0Cov_VX,4));
-            [atlasedData, n_nc, n_TC] = Make_Atlasing(AllVolume_V0Cov_VX, AllVolume_mappedAtlas);    
-
-            %% 13. SCRUBBING
-            % Converts the rotational components into [mm]
-            disp('Scrubbing...');
-            Mot = movText;
-            Mot(:,4:6) = 50*Mot(:,4:6);
-
-            % Discard the first n scans if necessary
-            if removeFirstNscans~=0
-                Mot = Mot((removeFirstNscans+1):end,:);
-            end
+            [atlasedData{m}, n_nc, n_TC] = Make_Atlasing(AllVolume_V0Cov_VX_UNSMOOTHED, AllVolume_mappedAtlas);   
+        end
+            
+
+        %% 13. SCRUBBING (of voxelwise and of atlased time courses)
+
+        % Converts the rotational components into [mm]
+        disp('Scrubbing...');
+        Mot = movText;
+        Mot(:,4:6) = 50*Mot(:,4:6);
 
-            % Computes FD
-            FD = sum(abs([0 0 0 0 0 0; diff(Mot)]),2);
+        % Discard the first n scans if necessary
+        if removeFirstNscans~=0
+            Mot = Mot((removeFirstNscans+1):end,:);
+        end
 
-            % Locates the time points to scrub (set to 1)
-            Scrub_mask1 = logical(FD > 0.5);
+        % Computes FD
+        FD = sum(abs([0 0 0 0 0 0; diff(Mot)]),2);
 
-            % Scrubbing is done for 6 frames after
-            disp('Scrubbing Masks...');
-            Scrub_mask2 = circshift(Scrub_mask1,[1,0]);
-            Scrub_mask2(1) = 0;
+        % Locates the time points to scrub (set to 1)
+        Scrub_mask1 = logical(FD > 0.5);
 
-            Scrub_mask3 = circshift(Scrub_mask1,[2,0]);
-            Scrub_mask3(1) = 0;
+        % Scrubbing is done for 6 frames after
+        disp('Scrubbing Masks...');
+        Scrub_mask2 = circshift(Scrub_mask1,[1,0]);
+        Scrub_mask2(1) = 0;
 
-            Scrub_mask4 = circshift(Scrub_mask1,[3,0]);
-            Scrub_mask4(1) = 0;
+        Scrub_mask3 = circshift(Scrub_mask1,[2,0]);
+        Scrub_mask3(1) = 0;
 
-            Scrub_mask5 = circshift(Scrub_mask1,[4,0]);
-            Scrub_mask5(1) = 0;
+        Scrub_mask4 = circshift(Scrub_mask1,[3,0]);
+        Scrub_mask4(1) = 0;
 
-            Scrub_mask6 = circshift(Scrub_mask1,[5,0]);
-            Scrub_mask6(1) = 0;
+        Scrub_mask5 = circshift(Scrub_mask1,[4,0]);
+        Scrub_mask5(1) = 0;
 
-            Scrub_mask7 = circshift(Scrub_mask1,[6,0]);
-            Scrub_mask7(1) = 0;
+        Scrub_mask6 = circshift(Scrub_mask1,[5,0]);
+        Scrub_mask6(1) = 0;
 
-            %Scrubbing done for 3 frames before.
-            Scrub_mask8 = circshift(Scrub_mask1,[-1,0]);
-            Scrub_mask8(end) = 0;
+        Scrub_mask7 = circshift(Scrub_mask1,[6,0]);
+        Scrub_mask7(1) = 0;
 
-            Scrub_mask9 = circshift(Scrub_mask1,[-2,0]);
-            Scrub_mask9(end) = 0;
+        %Scrubbing done for 3 frames before.
+        Scrub_mask8 = circshift(Scrub_mask1,[-1,0]);
+        Scrub_mask8(end) = 0;
 
-            Scrub_mask10 = circshift(Scrub_mask1,[-3,0]);
-            Scrub_mask10(end) = 0;
+        Scrub_mask9 = circshift(Scrub_mask1,[-2,0]);
+        Scrub_mask9(end) = 0;
 
-            Scrub_mask = Scrub_mask1 | Scrub_mask2 | Scrub_mask3 | Scrub_mask4 | Scrub_mask5 | Scrub_mask6 | Scrub_mask7 | Scrub_mask8 | Scrub_mask9 | Scrub_mask10;
+        Scrub_mask10 = circshift(Scrub_mask1,[-3,0]);
+        Scrub_mask10(end) = 0;
+
+        Scrub_mask = Scrub_mask1 | Scrub_mask2 | Scrub_mask3 | Scrub_mask4 | Scrub_mask5 | Scrub_mask6 | Scrub_mask7 | Scrub_mask8 | Scrub_mask9 | Scrub_mask10;
+
+        % SCRUBBING ATLASED TIME COURSES
+
+        for m = 1:n_atlas
 
-            scrubPrcntg = sum(Scrub_mask)/length(Scrub_mask)*100;
-            save('Scrubbed_Percentage','scrubPrcntg');
-            
             % Finds the time points that we know
-            TCon = atlasedData(:,find(~Scrub_mask));
-            TCon(isnan(TCon)) = 0;
+            TCon{m} = atlasedData{m}(:,find(~Scrub_mask));
+            TCon{m}(isnan(TCon{m})) = 0;
 
             % tinter is all the time points
             tinter = 1:length(Scrub_mask);
 
             % torig is the time points that we know
             torig = tinter(find(~Scrub_mask));
 
             % We interpolate all time point values (tinter) using the info that we
             % know (torig,TCon)
             % atlasedData has size n_vox x n_TP
             disp('Before interpolation...');
-            TC2 = interp1(torig,TCon',tinter,'spline')';
+            TC2{m} = interp1(torig,TCon{m}',tinter,'spline')';
 
             % We want to keep the 'NaN's that we had because they are used to
             % remove bad regional time courses later on
-            TC2(isnan(atlasedData)) = NaN;
+            TC2{m}(isnan(atlasedData{m})) = NaN;
             disp('Reviving all the NaNs...');
+        end
+
+
+        % Finds the time points that we know
+        TCon_VX = AllVolume_V0Cov_VX(:,find(~Scrub_mask));
+        TCon_VX(isnan(TCon_VX)) = 0;
 
+        % We interpolate all time point values (tinter) using the info that we
+        % know (torig,TCon)
+        % atlasedData has size n_vox x n_TP
+        disp('Before interpolation...');
+        TC2_VX = interp1(torig,TCon_VX',tinter,'spline')';
+
+        % We want to keep the 'NaN's that we had because they are used to
+        % remove bad regional time courses later on
+        TC2_VX(isnan(TCon_VX)) = NaN;
+        disp('Reviving all the NaNs...');
+
+
+
+        %% 14. FILTERING
+
+        for m = 1:n_atlas
+
+
+            Atlas_data{m} = y_IdealFilter(TC2{m}', 0.72, [0.01, 0]);
 
-            %% 14. FILTERING
-            disp(['Subject ',d(i).name,'...']);
-            disp('Filtering...');
-            % In this step, two different filtering strategies are used on the
-            % atlased data:
-            %   1) 0.01 Hz - 0.1 Hz Bandpass Filtering (B) 
-            %   2) 0.01 Hz Highpass filtering (H)
-
-            B_Filt_Atl_Data = y_IdealFilter(TC2', 0.72, [0.01, 0.1]);
-            H_Filt_Atl_Data = y_IdealFilter(TC2', 0.72, [0.01, 0]);
-
-            %Following code generates the same thing, but using a for loop:
-%             B2_Filt_Atl_Data = zeros(size(TC2,1),size(TC2,2));
-%             for m=1:size(TC2,1)
-%                 B2_Filt_Atl_Data(m,:)=y_IdealFilter(TC2(m,:)',0.72,[0.01, 0.1]);
-%             end
-% 
-%             H2_Filt_Atl_Data = zeros(size(TC2,1),size(TC2,2));
-%             for m=1:size(TC2,1)
-%                 H2_Filt_Atl_Data(m,:)=y_IdealFilter(TC2(m,:)',0.72,[0.01, 0]);
-%             end
-
-            %% 15. Saving All Files
-            disp(['Subject ',d(i).name,'...']);
-            disp(['Saving All Files for ', regionCnt, '...']);
-            cd(fullfile(writePath,writeFolder));
-            save([regionCnt,'B_Filt_Atl_Data',int2str(i),'.mat'], 'B_Filt_Atl_Data');
-%             save([int2str(regionCnt),'B2_Filt_Atl_Data',int2str(i),'.mat'], 'B2_Filt_Atl_Data');
-            save([regionCnt,'H_Filt_Atl_Data',int2str(i),'.mat'], 'H_Filt_Atl_Data');
-%             save([int2str(regionCnt),'H2_Filt_Atl_Data',int2str(i),'.mat'], 'H2_Filt_Atl_Data');
-
-            colormap('jet');
-            caxis([-1 1]);
-
-            %Plotting the functional connectivity matrix 
-            %Pearson correlation matrix is found by the following:
-            B_pCorr = corr(real(B_Filt_Atl_Data));
-            imagesc(B_pCorr);
-            print([regionCnt,'B_Filt_Atl_Data_plot'],'-dpng'); 
-% 
-%             B2_pCorr = corr(real(B2_Filt_Atl_Data)');
-%             imagesc(B2_pCorr);
-%             print([int2str(regionCnt),'B2_Filt_Atl_Data_plot'],'-dpng'); 
-
-            H_pCorr = corr(real(H_Filt_Atl_Data));
-            imagesc(H_pCorr);
-            print([regionCnt,'H_Filt_Atl_Data_plot'],'-dpng'); 
-
-%             H2_pCorr = corr(real(H2_Filt_Atl_Data)');
-%             imagesc(H2_pCorr);
-%             print([int2str(regionCnt),'H2_Filt_Atl_Data_plot'],'-dpng'); 
-
-            %Some statistical work on the Pearson Correlation
-            %First vectorize the pCorr
-            %vpCorr = reshape(pCorr,[],1);
-            %rng_vpCorr = range(pCorr);
-            %min_vpCorr = min(pCorr);
         end
 
+        H_Filt_VX_Data = y_IdealFilter(TC2_VX', 0.72, [0.01, 0]);
+
+
+        % Z-scoring the CAP data
+        CAP_Data = zscore(H_Filt_VX_Data);
+
+        VAR_Data = var(H_Filt_VX_Data);
+
+
+        %% 15. Saving All Files
+
+        save(fullfile(functWritePath,'CAP','CAP_Data'),'CAP_Data','-v7.3');
+        save(fullfile(functWritePath,'VAR','VAR_Data'),'VAR_Data','-v7.3');
+        save(fullfile(functWritePath,'SCHMM','Atlas_Data'),'Atlas_Data','-v7.3');
+
         disp(['Finished for Subject ',d(i).name,'...']);
 
     catch
         warning(['Could not do it for the ',int2str(i), '. iteration.']);
         continue;
     end
-end
-
-fclose(fid);
-
-% ** End of Script_Preprocessing_HCP_Mert.m **
\ No newline at end of file
+end
\ No newline at end of file
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