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cosmo_connectivity_allroi_plot.m
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cosmo_connectivity_allroi_plot.m
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clear all; fclose all;
clc
%close all;
% 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=5:5
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
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);
vols1=[];vols2=[];vols3=[];vols4=[];
mvpaRes=[];
if(isempty(vols1)),vols1=zeros(nsubjs,nc,nroi1,nroi2);vols2=vols1; vols3=vols1; vols4=vols1; end
vols1(:,:,:,:)=abs(d1.data);
vols2(:,:,:,:)=abs(d2.data);
vols3(:,:,:,:)=abs(d3.data);
vols4(:,:,:,:)=abs(d4.data);
HR = zeros(nsubjs,4);
if(isempty(mvpaRes)), mvpaRes=zeros(size(d1.data,1),1);end
for sidx=7:size(d1.data,1)
pred_vect=[]; targ_vect=[];
vols=[vols1(sidx,:,:); vols2(sidx,:,:); vols3(sidx,:,:); vols4(sidx,:,:)];
v1=squeeze(mean(vols));
figure;
for i=1:6
v2=v1(i,:);
subplot(3,2,i);hist(v2(:));
ylim([0 20])
end
% targets_train=[(1:6)';(1:6)'; (1:6)'];
% targets_test=(1:6)';
%
% runs=1:size(vols,1);
%
% for fold=1:length(runs)
% traini=runs(~ismember(runs,fold));
%
% samples_train= [squeeze(vols(traini(1),:,:));squeeze(vols(traini(2),:,:)); squeeze(vols(traini(3),:,:))];
% samples_test = squeeze(vols(fold,:,:));
%
%
%
% samples_train=samples_train(:,:); %samples_train=samples_train-mean(samples_train(:));
% samples_test=samples_test(:,:); %samples_test=samples_test-mean(samples_test(:));
%
% predicted = cosmo_classify_libsvm(samples_train, targets_train, samples_test);
% HR(sidx,fold)=sum(targets_test==predicted)/6;
%
% 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
end
end
end
end
%save([dpath, name_tag, '_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 , name_tag,'_connectivity_5freq_uncorrected_pvalues.mat'],'p_value')
%%
save_p = [dpath , name_tag,'_connectivity_5freq_uncorrected_pvalues.csv'];
csvwrite(save_p,p_value)
accuracy_mean(:,indx)=[];
accuracy_st_error(:,indx)=[];
save([dpath, name_tag, '_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)
%%
clc
fid=fopen([dpath , name_tag,'_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(indx)=[];
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<0.05)
accuracy_mean(corrected_p<0.05)
%%
close all
dpath='/autofs/space/taito_005/users/fahimeh/resources/coherence/large_subroi/';
load([dpath, name_tag, '_connectivity_5freq_accuracy_' num2str(t1) 'to' num2str(t2) 's.mat'])
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')