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cosmo_connectivity_encode_to_main.m
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cosmo_connectivity_encode_to_main.m
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clear all; close all; fclose all;
clc
% addpath /space/voima/1/users/matlab/CoSMoMVPA-master/
% addpath /space/voima/1/users/matlab/CoSMoMVPA-master/mvpa
% addpath /space/voima/1/users/matlab/toolbox/libsvm-master/
% addpath /space/voima/1/users/matlab/libsvm-master/matlab
% addpath /homes/9/jyrki/matlab/toolbox/CoSMoMVPA-master/mvpa
% addpath /autofs/homes/009/jyrki/matlab/toolbox/libsvm-master/
% addpath /autofs/homes/009/jyrki/matlab/toolbox/libsvm-master/matlab
% addpath /homes/9/jyrki/matlab/toolbox/CoSMoMVPA-master/
%%
addpath /space/voima/1/users/matlab/CoSMoMVPA-master/
addpath /space/voima/1/users/matlab/CoSMoMVPA-master/mvpa
%addpath /space/voima/1/users/matlab/toolbox/libsvm-master/
addpath /space/voima/1/users/matlab/libsvm-master/matlab
%%
fid=fopen('/autofs/space/taito_005/users/fahimeh/doc/txt/label_names.txt');
C=textscan(fid,'%s');
label_names=C{1,1};
lnames = {'anteriorcingulate_rh',
'caudalmiddlefrontal_rh',
'lateralorbitofrontal_rh',
'IFG_rh',
'rostralmiddlefrontal_rh',
'precentral_rh',
'superiortemporal_rh',
'lateralorbitofrontal_lh',
'IFG_lh',
'caudalmiddlefrontal_lh',
'rostralmiddlefrontal_lh',
'precentral_lh',
'superiortemporal_lh',
'supramarginal_lh',
'supramarginal_rh'};
ROIs = {'rACC',
'rCMF',
'rOF',
'rIFG',
'rRMF',
'rPrC',
'rST',
'lOF',
'lIFG',
'lCMF',
'lRMF',
'lPrC',
'lST',
'lSM',
'rSM'};
%name_tag='1002to1017';
% name_tag='3002to3017';
% %name_tag='2102to2117';
%
%
% if strcmp(name_tag,'1002to1017')
% t1=0.5;
% t2=2;
% else
% t1=0;
% t2=1;
% end
% indices = {[1:5],[6:10],[11:17],[18:27],[28:40],[41:56],[57:68],[69:72], ...
% [73:79],[80:88],[89:94],[95:106],[107:122],[123:135],[136:145],[146:154]};
indices = {[1:5],[6:10],[11:17],[18:27],[28:40],[41:56],[57:68], ...
[73:79],[80:88],[89:94],[95:106],[107:122],[123:135],[136:145],[146:154]};
%%
dpath='/autofs/space/taito_005/users/fahimeh/resources/coherence/large_subroi/';
freqs={'Theta','Alpha','Beta','Gamma','HighGamma'};
for freqi=1:5
freq=freqs{freqi};
k=1;
for iroi1=2:length(lnames)-1
for iroi2=iroi1+1:length(lnames)
L1=label_names(indices{iroi1});
L2=label_names(indices{iroi2});
check1=strcmp(L1{1}(end-7:end-6),L2{1}(end-7:end-6));
check2= contains(L1{1},'temporal')+contains(L2{1},'temporal');
if check1+check2==2 && check2<2
name_tag='3002to3017';
t1=0;
t2=1;
try
fn1=[dpath 'iCoh_allsubj_' lnames{iroi1} '_' lnames{iroi2} '_' name_tag '_' freq '_split1_time' num2str(t1) 'to' num2str(t2) 's.mat'];
fn2=[dpath 'iCoh_allsubj_' lnames{iroi1} '_' lnames{iroi2} '_' name_tag '_' freq '_split2_time' num2str(t1) 'to' num2str(t2) 's.mat'];
fn3=[dpath 'iCoh_allsubj_' lnames{iroi1} '_' lnames{iroi2} '_' name_tag '_' freq '_split3_time' num2str(t1) 'to' num2str(t2) 's.mat'];
fn4=[dpath 'iCoh_allsubj_' lnames{iroi1} '_' lnames{iroi2} '_' name_tag '_' freq '_split4_time' num2str(t1) 'to' num2str(t2) 's.mat'];
d1=load(fn1);d2=load(fn2);
d3=load(fn3);d4=load(fn4);
[nsubjs, nc, nroi1, nroi2]=size(d1.data);
name_tag='1002to1017';
t1=0.5;
t2=2;
fn1=[dpath 'iCoh_allsubj_' lnames{iroi1} '_' lnames{iroi2} '_' name_tag '_' freq '_split1_time' num2str(t1) 'to' num2str(t2) 's.mat'];
fn2=[dpath 'iCoh_allsubj_' lnames{iroi1} '_' lnames{iroi2} '_' name_tag '_' freq '_split2_time' num2str(t1) 'to' num2str(t2) 's.mat'];
fn3=[dpath 'iCoh_allsubj_' lnames{iroi1} '_' lnames{iroi2} '_' name_tag '_' freq '_split3_time' num2str(t1) 'to' num2str(t2) 's.mat'];
fn4=[dpath 'iCoh_allsubj_' lnames{iroi1} '_' lnames{iroi2} '_' name_tag '_' freq '_split4_time' num2str(t1) 'to' num2str(t2) 's.mat'];
d1m=load(fn1);d2m=load(fn2);
d3m=load(fn3);d4m=load(fn4);
[nsubjsm, ncm, nroi1m, nroi2m]=size(d1m.data);
vols1=[];vols2=[];vols3=[];vols4=[];
vols1m=[];vols2m=[];vols3m=[];vols4m=[];
mvpaRes=[];
if(isempty(vols1)),vols1=zeros(nsubjs,nc,nroi1,nroi2);vols2=vols1; vols3=vols1; vols4=vols1; end
if(isempty(vols1m)),vols1m=zeros(nsubjsm,ncm,nroi1m,nroi2m);vols2m=vols1m; vols3m=vols1m; vols4m=vols1m; end
vols1(:,:,:,:)=abs(d1.data);
vols2(:,:,:,:)=abs(d2.data);
vols3(:,:,:,:)=abs(d3.data);
vols4(:,:,:,:)=abs(d4.data);
vols1m(:,:,:,:)=abs(d1m.data);
vols2m(:,:,:,:)=abs(d2m.data);
vols3m(:,:,:,:)=abs(d3m.data);
vols4m(:,:,:,:)=abs(d4m.data);
HR = zeros(nsubjs,4);
if(isempty(mvpaRes)), mvpaRes=zeros(size(d1.data,1),1);end
for sidx=1:size(d1.data,1)
pred_vect=[]; targ_vect=[];
vols=[vols1(sidx,:,:); vols2(sidx,:,:); vols3(sidx,:,:); vols4(sidx,:,:)];
volsm=[vols1m(sidx,:,:); vols2m(sidx,:,:); vols3m(sidx,:,:); vols4m(sidx,:,:)];
targets_train=[(1:6)';(1:6)'; (1:6)'];
targets_test=(1:6)';
runs=1:size(vols,1);
foldk=1;
for fold=1:4
traini=runs(~ismember(runs,fold));
samples_train= [squeeze(vols(traini(1),:,:));squeeze(vols(traini(2),:,:)); squeeze(vols(traini(3),:,:))];
% samples_train= [squeeze(vols(1,:,:));squeeze(vols(2,:,:)); squeeze(vols(3,:,:));squeeze(vols(4,:,:))];
samples_test = squeeze(volsm(fold,:,:));
% [COEFF, SCORE, LATENT, TSQUARED, EXPLAINED, MU] = pca(samples_train);
samples_train=samples_train(:,:); %samples_train=samples_train-mean(samples_train(:));
samples_test=samples_test(:,:); %samples_test=samples_test-mean(samples_test(:));
% opt=[];
predicted = cosmo_classify_libsvm(samples_train, targets_train, samples_test);
HR(sidx,foldk)=sum(targets_test==predicted)/6;
foldk=foldk+1;
clear predicted
end
clear vols
end
save_hr{k,1}=HR;
mvpaRes=mean(HR,2);
% removing left handed subjects
mvpaRes([6,14])=[];
[h p ci stat]=ttest(mvpaRes,1/6,'Tail','right')
roi_to_roi{k,1}=[ROIs{iroi1} '-' ROIs{iroi2}];
roi_p(freqi,k)=p;
accuracy_mean(freqi,k)=mean(mvpaRes);
accuracy_st_error(freqi,k)=std(mvpaRes)/sqrt(length(mvpaRes));
accuracy(freqi,k,:)=mvpaRes;
k=k+1;
clear p
catch
end
end
end
end
end
%save([dpath, 'encodetomain_connectivity_5freq_subjects_accuracy_' num2str(t1) 'to' num2str(t2) 's.mat'],'accuracy','roi_to_roi')
%%
% removing lst-rST connection
indx=6;
pp = roi_p;
pp(:,indx)=[];
p_value = pp(:);
save([dpath , 'EtoM_4fold_connectivity_5freq_uncorrected_pvalues.mat'],'p_value');
save_p = [dpath , 'EtoM_connectivity_5freq_uncorrected_pvalues.csv'];
csvwrite(save_p,p_value)
accuracy_mean(:,indx)=[];
accuracy_st_error(:,indx)=[];
save([dpath, 'encodetomain_connectivity_5freq_accuracy_' num2str(t1) 'to' num2str(t2) 's.mat'],'accuracy_mean','accuracy_st_error')
%
% %% run the R commands:
% % > mydata = read.csv(file="/autofs/space/taito_005/users/fahimeh/resources/coherence/large_subroi/1002to1017_connectivity_5freq_uncorrected_pvalues.csv", header=FALSE, sep=",")
% % > p = p.adjust(as.numeric(mydata[[1]]),'fdr')
% % > write.table(p,file="/autofs/space/taito_005/users/fahimeh/resources/coherence/large_subroi/1002to1017_connectivity_5freq_corrected_pvalues.txt",sep="",row.names=FALSE)
%
%%
close all
clc
freqs={'Theta','Alpha','Beta','Gamma','HighGamma'};
dpath='/autofs/space/taito_005/users/fahimeh/resources/coherence/large_subroi/';
load([dpath, 'encodetomain_connectivity_5freq_subjects_accuracy_' num2str(0.5) 'to' num2str(2) 's.mat'])
fid=fopen([dpath , 'ALL_4fold_connectivity_5freq_corrected_pvalues.txt']);
C=textscan(fid,'%s');
corr_p=C{1,1};
for i=2:length(corr_p)
corrected_p(i-1) = str2double(corr_p{i});
end
roi_to_roi_copy = roi_to_roi;
roi_to_roi_copy(6)=[];
roi_freq = cell(1);
kk=1;
for ii=1:12
for jj=1:5
roi_freq{kk,1}=[roi_to_roi_copy{ii} '-' freqs{jj}];
kk=kk+1;
end
end
roi_freq(corrected_p(1:60)<0.06)
accuracy_mean(corrected_p(1:60)<0.05)
%
% %%
%
%
% save_fig_dir='/autofs/space/taito_005/users/fahimeh/doc/Figures/coherence/results/';
%
% figure;
% for i=1:5
% plot(accuracy_mean(i,:),'LineWidth',2)
% hold on
% end
% ylim([0.15 .45])
% legend('Theta','Alpha','Beta','LowGamma','HighGamma')
% xticks([1:12])
% xticklabels(roi_to_roi_copy)
% print([save_fig_dir name_tag '_accuracy_mean'],'-dpdf','-bestfit')
%
% print([save_fig_dir name_tag '_accuracy_mean'],'-dpng')