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Main_CHBM_sens_level.m
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%% Get BC-VARETA Outputs
clc;
clear all;
close all;
addpath(genpath('external'));
addpath('functions');
addpath('templates');
addpath('make_reports');
addpath('test');
properties = jsondecode(fileread(fullfile('app','properties.json')));
%% Printing data information
disp(strcat("-->> Name:",properties.generals.name));
disp(strcat("-->> Version:",properties.generals.version));
disp(strcat("-->> Version date:",properties.generals.version_date));
disp("=================================================================");
root_path_g1 = 'Z:\data3_260T\share_space\jcclab-users\Ariosky\BC-V_Output\CHBM_Template';
root_path_g2 = 'Z:\data3_260T\data\CCLAB_DATASETS\Covid\Corrected\BC-V_Output\Controls_manual_&_auto';
root_path_g3 = 'Z:\data3_260T\data\CCLAB_DATASETS\Covid\Corrected\BC-V_Output\Pathol_manual_&_auto';
output_path = 'D:\Data\CHBM\BC-V_group_stat';
%Getting subject layout
scalp = load(fullfile('Z:\data3_260T\data\CCLAB_DATASETS\Covid\Corrected\BC-V_Structure\Controls_manual_&_auto\CU COVID 003\scalp\scalp.mat'));
load(properties.general_params.colormap);
Channel = scalp.Cdata.Channel;
elec_data = [];
elec_data.pos = zeros(length(Channel),3);
for ii = 1:length(Channel)
elec_data.lbl{ii} = Channel(ii).Name;
temp = Channel(ii).Loc;
elec_data.pos(ii,:) = mean(temp,2);
end
elec_data.label = elec_data.lbl;
elec_data.elecpos = elec_data.pos;
elec_data.unit = 'mm';
cfg.marker = '';
cfg.layout = properties.sensor_params.layout;
cfg.channel = 'eeg';
cfg.markersymbol = '.';
cfg.colormap = cmap_a;
cfg.markersize = 3;
cfg.markercolor = [1 1 1];
topo.elec = elec_data;
topo.label = elec_data.lbl;
topo.dimord = 'chan_freq';
topo.freq = 1;
disp("-->> Starting process");
disp("------------------>> Processing CHBM normative dataset <<-----------------");
disp("----------------------------------------------------------------");
disp("-->> Finding completed files");
%%
%% Group 1
%%
% Checking firt subject for create the tensors
subjects_g1 = dir(fullfile(root_path_g1,'**','BC_V_info.mat'));
disp("----------------------------------------------------------------");
info_g1 = load('CHBM_info');
subject = subjects_g1(1);
load(fullfile(subject.folder,subject.name));
sensor_level = BC_V_info.generals;
sens_file = fullfile(subject.folder,sensor_level(1).Ref_path,sensor_level(1).Name);
load(sens_file,"Svv_channel");
sens3D_g1 = zeros(size(Svv_channel,1),size(Svv_channel,3),length(subjects_g1));
age_g1 = zeros(length(subjects_g1),1);
count_g1 = 1;
for i=1:length(subjects_g1)
subject = subjects_g1(i);
load(fullfile(subject.folder,subject.name));
subID = BC_V_info.subjectID;
disp(strcat("-->> Processing subject: ",subID," Iter: ", num2str(i)));
sensor_level = BC_V_info.generals;
age_g1(i) = info_g1.data_info(contains({info_g1.data_info.SubID},subID)).Age;
sens_file = fullfile(subject.folder,sensor_level(1).Ref_path,sensor_level(1).Name);
load(sens_file,"Svv_channel");
for j=1:size(Svv_channel,3)
sens3D_g1(:,j,count_g1) = abs(diag(Svv_channel(:,:,j)));
end
count_g1 = count_g1 + 1;
end
%%
%% Group 2
%%
disp("------------------>> Processing Covid-control dataset <<-----------------");
disp("----------------------------------------------------------------");
disp("-->> Finding completed files");
subjects_g2 = dir(fullfile(root_path_g2,'**','BC_V_info.mat'));
disp("----------------------------------------------------------------");
info_g2 = load('Controls_info');
subject = subjects_g2(1);
load(fullfile(subject.folder,subject.name));
sensor_level = BC_V_info.generals;
sens_file = fullfile(subject.folder,sensor_level(1).Ref_path,sensor_level(1).Name);
load(sens_file,"Svv_channel");
sens3D_g2 = zeros(size(Svv_channel,1),size(Svv_channel,3),length(subjects_g2));
age_g2 = zeros(length(subjects_g2),1);
count_g2 = 1;
for i=1:length(subjects_g2)
subject = subjects_g2(i);
load(fullfile(subject.folder,subject.name));
subID = BC_V_info.subjectID;
disp(strcat("-->> Processing subject: ",subID," Iter: ", num2str(i)));
sensor_level = BC_V_info.generals;
age_g2(i) = info_g2.data_info(contains({info_g2.data_info.SubID},subID)).Age;
sens_file = fullfile(subject.folder,sensor_level(1).Ref_path,sensor_level(1).Name);
load(sens_file,"Svv_channel");
for j=1:size(Svv_channel,3)
sens3D_g2(:,j,count_g2) = abs(diag(Svv_channel(:,:,j)));
end
count_g2 = count_g2 + 1;
end
%%
%% Group 3
%%
disp("------------------>> Processing Covid dataset <<-----------------");
disp("----------------------------------------------------------------");
disp("-->> Finding completed files");
subjects_g3 = dir(fullfile(root_path_g3,'**','BC_V_info.mat'));
disp("----------------------------------------------------------------");
info_g3 = load('Pathol_info');
subject = subjects_g3(1);
load(fullfile(subject.folder,subject.name));
sensor_level = BC_V_info.generals;
sens_file = fullfile(subject.folder,sensor_level(1).Ref_path,sensor_level(1).Name);
load(sens_file,"Svv_channel");
sens3D_g3 = zeros(size(Svv_channel,1),size(Svv_channel,3),length(subjects_g3));
age_g3 = zeros(length(subjects_g3),1);
count_g3 = 1;
for i=1:length(subjects_g3)
subject = subjects_g3(i);
load(fullfile(subject.folder,subject.name));
subID = BC_V_info.subjectID;
disp(strcat("-->> Processing subject: ",subID," Iter: ", num2str(i)));
sensor_level = BC_V_info.generals;
age_g3(i) = info_g3.data_info(contains({info_g3.data_info.SubID},subID)).Age;
sens_file = fullfile(subject.folder,sensor_level(1).Ref_path,sensor_level(1).Name);
load(sens_file,"Svv_channel");
for j=1:size(Svv_channel,3)
sens3D_g3(:,j,count_g3) = abs(diag(Svv_channel(:,:,j)));
end
count_g3 = count_g3 + 1;
end
%%
%% Group comparison
%%
%% remove subject scale
sens3D_g1t = log(sens3D_g1);
sens3D_g2t = log(sens3D_g2);
sens3D_g3t = log(sens3D_g3);
sens3D_g1t = sens3D_g1t - mean(sens3D_g1t,[1 2]);
sens3D_g2t = sens3D_g2t - mean(sens3D_g2t,[1 2]);
sens3D_g3t = sens3D_g3t - mean(sens3D_g3t,[1 2]);
fig_scattergram = plot_age_sens(sens3D_g1t,age_g1);
%% linea regression
[sens3D_g1t,sens3D_g2t,sens3D_g3t] = linear_regression(sens3D_g1t,age_g1,sens3D_g2t,age_g2,sens3D_g3t,age_g3);
sens3D_g1t = reshape(sens3D_g1t,size(sens3D_g1t,1)*size(sens3D_g1t,2),size(sens3D_g1t,3));
sens3D_g2t = reshape(sens3D_g2t,size(sens3D_g2t,1)*size(sens3D_g2t,2),size(sens3D_g2t,3));
sens3D_g3t = reshape(sens3D_g3t,size(sens3D_g3t,1)*size(sens3D_g3t,2),size(sens3D_g3t,3));
sens3D_g1t = permute(sens3D_g1t,[2 1]);
sens3D_g2t = permute(sens3D_g2t,[2 1]);
sens3D_g3t = permute(sens3D_g3t,[2 1]);
%% statistical test
min_nsample = min([size(sens3D_g1t,1) size(sens3D_g2t,1) size(sens3D_g3t,1)]);
sens3D_g1t(min_nsample+1:end,:) = [];
sens3D_g2t(min_nsample+1:end,:) = [];
sens3D_g3t(min_nsample+1:end,:) = [];
nperm = 1000;
psignif = 0.05;
%% diff g1_g2
[h_g1g2,p_g2g2,~,stats_g1g2] = ttest2(sens3D_g1t,sens3D_g2t,'Tail','both');
[pID_g1g2,pN_g1g2] = FDR(p_g1g2,psignif);
ind_p_g1g2 = find(p_g1g2 > pN_g1g2);
orig_t_g1g2 = stats_g1g2.tstat';
orig_t_g1g2(ind_p_g1g2) = 0;
orig_t_g1g2 = reshape(orig_t_g1g2,size(sens3D_g2,1),size(sens3D_g2,2));
%% diff g2_g3
[h_g2g3,p_g2g3,~,stats_g2g3] = ttest2(sens3D_g2t,sens3D_g3t,'Tail','both');
[pID_g2g3,pN_g2g3] = FDR(p_g2g3,psignif);
ind_p_g2g3 = find(p_g2g3 > pN_g2g3);
orig_t_g2g3 = stats_g2g3.tstat';
orig_t_g2g3(ind_p_g2g3) = 0;
orig_t_g2g3 = reshape(orig_t_g2g3,size(sens3D_g2,1),size(sens3D_g2,2));
%% figures
% Creating a report
report_name = 'Covid dataset_sens_analysis';
f_report('New');
% Add a title to the report
f_report('Title','Group difference normativeVScontrol, controlVScovid');
% Add a title to the report
f_report('Header','Cortical topography');
% Add a info to the report
f_report('Index','Linear trend of the data with age'); % a line information
bands = {'delta','theta','alpha','beta','gamma'};
for band = 1:length(bands)
fig_out = fig_scattergram{band};
fig_name = ['age-scattergram ',bands{band}];
f_report('Snapshot', fig_out, fig_name, [] , [200,200,875,350]);
close(fig_out);
end
% Add topic to the report
f_report('Index','Student T-test of the groups');
% Add a info to the report
f_report('Info','Transformations applied to the data'); % a line information
% Add multielements info to the report
line_1 = 'collect normative data_g1(gen,band,nsubj_g1)';
line_2 = 'collect control data_g2(gen,band,nsubj_g2)';
line_3 = 'collect covid data_g3(gen,band,nsubj_g3)';
line_4 = 'log transformation data <- log(data)';
line_5 = 'remove individual subject scale data <- mean(data,[1 2])';
line_6 = 'obtain age regression coefficients with normative data_g1';
line_7 = 'remove age effect in data_g1, data_g2, data_g3 with regression coeffcients';
line_8 = 'obtain Tstudent(data_g1,data_g2) between normative and control data';
line_9 = 'obtain Tstudent(data_g2,data_g3) between control and covid data';
f_report('Info',{line_1,line_2,line_3,line_4,line_5,line_6,line_7,line_8,line_9}); % multiline information cell array
%%
%% EEG topography
for band = 1:length(bands)
% diff g1_g2
topo.powspctrm = data_diff_g1g2(:,band);
figure_diff_g1_g2 = figure;
ft_topoplotTFR(cfg,topo);
fig_name = strcat(['sensor-tstat-normativeVScontrol' ' ' band.name ' ' 'topography']);
title(fig_name)
f_report('Snapshot', figure_diff_g1_g2, fig_name, [] , [200,200,875,350]);
close(figure_diff_g1_g2);
% diff g2_g3
topo.powspctrm = data_diff_g2g3(:,band);
figure_diff_g2_g3 = figure;
ft_topoplotTFR(cfg,topo);
fig_name = strcat(['sensor-tstat-normativeVScontrol' ' ' band.name ' ' 'topography']);
title(fig_name)
f_report('Snapshot', figure_diff_g2_g3, fig_name, [] , [200,200,875,350]);
close(figure_diff_g2_g3);
end
% Add the footer info to the report
footer_title = 'Organization';
text = 'All rights reserved';
copyright = '@copy CC-Lab';
contact = '[email protected]';
references = {'https://github.com/CCC-members', 'https://www.neuroinformatics-collaboratory.org/'};
f_report('Footer', footer_title, text, 'ref', references, 'copyright', copyright, 'contactus', contact);
disp('-->> Saving report.');
% Export the report
disp('-->> Exporting report.');
% report_name = 'CHBM_by_events';
FileFormat = 'html';
f_report('Export',output_path, report_name, FileFormat);
%%
% [activ3D_g1t,~] = get_roi_data(activ3D_g1,Sc);
% [activ3D_g2t,~] = get_roi_data(activ3D_g2,Sc);
% activ3D_g1t = log(activ3D_g1t);
% activ3D_g1t = activ3D_g1t - mean(activ3D_g1t,[1 2]);
% activ3D_g2t = log(activ3D_g2t);
% activ3D_g2t = activ3D_g2t - mean(activ3D_g2t,[1 2]);
% nperm = 1000;
% psignif = 0.05;
% activ3D_g1t = reshape(activ3D_g1t,size(activ3D_g1t,1)*size(activ3D_g1t,2),size(activ3D_g1t,3));
% activ3D_g2t = reshape(activ3D_g2t,size(activ3D_g2t,1)*size(activ3D_g2t,2),size(activ3D_g2t,3));
% activ3D_g1t = permute(activ3D_g1t,[2 1]);
% activ3D_g2t = permute(activ3D_g2t,[2 1]);
%
%
% %% mean data1
% figure
% patch('Faces',Sc.Faces,'Vertices',Sc.Vertices,'FaceVertexCData',...
% squeeze(mean(activ3D_g1(:,1,:),3)),'FaceColor','interp','EdgeColor','none','FaceAlpha',.99);
% set(gca,'xcolor','k','ycolor','k','zcolor','k');
% az = 90; el = 270;
% view(az,el);
% colormap(gca,cmap);
% % title(['cortex-tstat ',bands{band}]);
% %% mean data2
% figure
% patch('Faces',Sc.Faces,'Vertices',Sc.Vertices,'FaceVertexCData',...
% squeeze(mean(activ3D_g2(:,1,:),3)),'FaceColor','interp','EdgeColor','none','FaceAlpha',.99);
% set(gca,'xcolor','k','ycolor','k','zcolor','k');
% az = 90; el = 270;
% view(az,el);
% colormap(gca,cmap);
% % title(['cortex-tstat ',bands{band}]);
% %
%
%
% group_difference(activ3D_g1t,activ3D_g2t,Sc);
%
%
%
% activ3D_g1t = log(activ3D_g1t);
% activ3D_g1t = activ3D_g1t - mean(activ3D_g1t,[1 2]);
% activ3D_g2t = log(activ3D_g2t);
% activ3D_g2t = activ3D_g2t - mean(activ3D_g2t,[1 2]);
%
%
% group_difference(activ3D_g1t_regressed,activ3D_g2t_regressed,Sc)
%
%
% plot_differ_by_roi(activ3D_g1t,activ3D_g2t,Sc);
% avrg_cortex(activ3D_g1t,Sc,'g1');
% avrg_cortex(activ3D_g2t,Sc,'g2');