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ISFCClustering/Matlab_Scripts/Script_HBM_Results_Herewegoagain.m
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Script_HBM_Results_Herewegoagain.m
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% Miscellaneous
addpath(genpath(fullfile(pwd,'Utils')));
addpath(genpath(fullfile(pwd,'Utils_DL')));
addpath(genpath(fullfile(pwd,'Utils_KMeans')));
addpath(genpath(fullfile(pwd,'Utils_Graphs')));
addpath(genpath(fullfile(pwd,'gspbox')));
% Scripts required to visualize 3D brains with nodes and edges
addpath(genpath('/Volumes/Code/shared/static_FC_pipeline/depMat'));
addpath(genpath('/Volumes/Code-1/shared/static_FC_pipeline/depMat'));
addpath(genpath('/media/miplab-nas1/Code/shared/static_FC_pipeline/depMat'));
addpath(genpath('/Volumes/Code/shared/static_FC_pipeline/graph_vis'));
addpath(genpath('/Volumes/Code-1/shared/static_FC_pipeline/graph_vis'));
addpath(genpath('/media/miplab-nas1/Code/shared/static_FC_pipeline/graph_vis'));
% Atlas used in this study
addpath(genpath(fullfile(pwd,'Craddock_Atlas')));
FD_path = 'FD';
n_ROI = 270;
n_subj = 59;
Data_path = 'HBM_PREPRO777_BOOT_SEP_THECHOSENONE';
Title = 'HBM_PREPRO888_BOOTISFC_NOTSEPGROUPS_THECHOSENONE';
load(Data_path,'ISFC_RS','ISFC_MW','ISFC_PureRS','Group_Label','Group_Label_pureRS','Con_vec');
% First, I want to determine when in time, and for what connections, I
% observe a significant excursion above from the mean connectivity
% SignMat has size n_conn x n_TP x n_sessions, and contains:
% -1 if there is a negative excursion
% 0 if there is no excursion
% +1 if there is a positive excursion
SignMat = FindExcursions_v2(ISFC_RS,ISFC_MW,99.98,'Subject-Marginalized-Added-RS',ISFC_PureRS,Group_Label,Group_Label_pureRS);
% Next, I want to perform a feature selection, because not all my
% connections are going to be informative. To keep a connection, it must
% show at least one excursion either in all the CTR subjects, or in all the
% ASD subjects, or in both cases
SignMat2 = SignMat;
Con_vec2 = Con_vec;
idx_consistent = logical(ones(1,length(Con_vec)));
% New number of connections, following feature extraction
n_conn2 = size(SignMat2,1);
% From now on, C1 and C2 contain the reduced pool of connections
clear C1
clear C2
[C1,C2] = ParseConVec(Con_vec2);
% AT THE 0.02% LEVEL (separate subject groups
SignMat_sep = FindExcursions_v2(ISFC_RS,ISFC_MW,99.98,'Subject-Marginalized-Added-RS-Separated-Groups',ISFC_PureRS,Group_Label,Group_Label_pureRS);
%disp(['There are ',num2str(sum(sum(sum(SignMat_2==1)))/numel(SignMat_2)*100),' [%] positive elements retained...']);
%disp(['There are ',num2str(sum(sum(sum(SignMat_2==-1)))/numel(SignMat_2)*100),' [%] negative elements retained...']);
SignMat2_sep = SignMat_sep;
disp('Computation of metrics for number of excursions...');
% ALPHA = 1%
% Number of times that each connection is showing significance
% I separate between the positive, and the negative, parts, at each level
% of significance
n_excursions = squeeze(sum(abs(SignMat2),2));
n_excursions_CTR = n_excursions(:,Group_Label==1);
n_excursions_ASD = n_excursions(:,Group_Label==2);
[Summary_PREPRO888_BOOTISFC_THECHOSENONE_NOTSEP,ISFC,ISFC_mot,n_excursions,...
EXC_mot,idx_AQ,COG,H_ISC,P_ISC] = HBM_Analysis_Herewegoagain(Data_path,FD_path,n_ROI,n_subj,Title,'01');
save('Summary_FINAL_CHOICE','Summary',...
'n_excursions','EXC_mot',...
'idx_AQ','H_ISC','P_ISC','AQ','IQ','Motion_regressor','-v7.3');
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