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iCAPs/functions/Run_TimeCourses.m
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Run_TimeCourses.m
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%% This function runs regression to obtain iCAPs time courses
% regression can either be done 'unconstrained' or with 'transient-based'
% constraints
%
% Input:
% param - struct containing all necessary parameters to run TA
%
% * Data reading and saving information:
% .PathData - path to data
% .Subjects - cell array with list of subdirectories where each
% subject's fMRI data is stored; must contain one entry per
% subject to analyze.
% This is where the TA folder will be created (or looked for)
% for each subject.
% .n_subjects - number of subject to analyze
% .title - possibility to define a title for the current
% project (usefull if TA should be run for different
% parameters), default: current date, it should be the title of
% the project for which TA has already been run
% [.force_Regression] - if set to 1, regression will be forced to
% run, even if already has been done
% [.thresh_title] - information to read thresholding data and save
% regression results, if not specified, the fields 'param.alpha'
% and 'param.f_voxels' need to exist
% [.data_title] - information for saving of regression results
% [.iCAPs_title] - string or cellstring with all the subfolders to
% create for saving regression results
%
% * Regression information:
% [.saveClusterReplicateData] - specify if the result of each replicate
% should be saved during clustering, default = 0
% .n_folds - Number of replicates of clustering
% .K - Number of clusters, can be a number or an array of multiple K
% .DistType - Type of distance to use for the k-means clustering
% process (choose between 'sqeuclidean' and 'cosine')
% [.MaxIter] - Maximum number of allowed iterations of the kmeans
% clustering, the Matlab default of 100 is sometimes not enough
% if many frames (i.e., many subjects or long scans) are
% included, default = 100
%
% Output:
function [] = Run_Regression(param)
% Date and time when the routines are called
param.date = strrep(strrep(datestr(datetime('now')),' ','_'),':','_');
if ~isfield(param,'title') || isempty(param.title)
param.title=param.date;
end
% Creates and opens a log-file that will contain all information related to
% what is done to the data
if ~exist(fullfile(param.PathData,'TAlogs'),'dir'); mkdir(fullfile(param.PathData,'TAlogs'));end;
fid=fopen(fullfile(param.PathData,'TAlogs',['log_Regression_',param.title,'.txt']),'a+');
WriteInformation(fid,['Starting the total activation/iCAPs tools for project entitled ',param.title]);
% Checks that the path towards the data is correct
if ~exist(param.PathData,'dir')
WriteInformation(fid,'Incorrect path towards the data: execution stopped');
error('The data folder that you specified does not exist ! Please check and restart running...');
end
% setting data title, if not specified
if ~isfield(param,'data_title') || isempty(param.data_title)
param.data_title=param.title;
end
% setting thresholding title, if not specified
if ~isfield(param,'thresh_title') || isempty(param.thresh_title)
param.thresh_title = ['Alpha_',strrep(num2str(param.alpha(1)),'.','DOT'),'_',...
strrep(num2str(param.alpha(2)),'.','DOT'),'_Fraction_',...
strrep(num2str(param.f_voxels),'.','DOT')];
end
% main folder according to data+thresholding, aggregated frames are saved here
param.outDir_main=fullfile(param.PathData,'iCAPs_results',[param.data_title,'_',param.thresh_title]);
% iCAPs title (subfolder in which clustering results are saved)
if ~isfield(param,'iCAPs_title') || isempty(param.iCAPs_title)
for nK=1:length(param.K)
param.iCAPs_title{nK} = ['K_',num2str(param.K(nK)),'_Dist_',...
param.DistType,'_Folds_',num2str(param.n_folds)];
end
if length(param.K)==1
param.iCAPs_title=param.iCAPs_title{1};
end
end
% adapting k and iCAPs title to make the code compatible with
% multiple K
if iscell(param.iCAPs_title)
param.iCAPs_title_cell=param.iCAPs_title;
else
param.iCAPs_title_cell{1}=param.iCAPs_title;
end
param.K_vect=param.K;
% main procedure for regression:
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if ~isfield(param,'doRegression') || param.doRegression
WriteInformation(fid,'Entering the time course retrieval process...');
% clustering for every K
for iK=1:length(param.iCAPs_title_cell)
param.iCAPs_title=param.iCAPs_title_cell{iK};
param.K=param.K_vect(iK);
WriteInformation(fid,['K = ' num2str(param.K) ', iCAPs title: ' param.iCAPs_title]);
% clustering subfolder to save clustering-specific information
param.outDir_iCAPs=fullfile(param.outDir_main,param.iCAPs_title);
% check if clustering has been done
[Aggregating_done,Clustering_done,~,~] = Check_iCAPs_Files(param.outDir_main,param.outDir_iCAPs);
if ~Aggregating_done
error('Significant innvation frames have not been aggregated yet, run clustering routine first!')
end
if ~Clustering_done
error('Clustering has not been done yet, run clustering routine first!')
end
switch param.regType
case 'unconstrained'
param.outDir_reg=fullfile(param.outDir_iCAPs,'TCs_unconstrained');
if ~exist(param.outDir_reg,'dir');mkdir(param.outDir_reg);end
[~,~,~,~,Regression_unc_done] = Check_iCAPs_Files([],[],[],param.outDir_reg);
if isfield(param,'force_Regression') && param.force_Regression
Regression_unc_done=0;
end
if ~Regression_unc_done
WriteInformation(fid,'Loading aggregated data and iCAPs...');
load(fullfile(param.outDir_main,'AI.mat'));
load(fullfile(param.outDir_main,'AI_subject_labels.mat'));
load(fullfile(param.outDir_iCAPs,'iCAPs'));
WriteInformation(fid,'Generating iCAPs Time Courses...');
[TC_unc,TC_unc_stats] = GenerateTimeCourses(AI',AI_subject_labels,iCAPs,param);
save(fullfile(param.outDir_reg,'TC_unc.mat'),'TC_unc');
save(fullfile(param.outDir_reg,'TC_unc_stats.mat'),'TC_unc_stats');
save(fullfile(param.outDir_reg,'param.mat'),'param');
load(fullfile(param.outDir_main,'AI_subject_labels'));
load(fullfile(param.outDir_main,'subject_labels'));
load(fullfile(param.outDir_iCAPs,'IDX'));
clusteringResults.AI_subject_labels=AI_subject_labels;
clusteringResults.subject_labels=subject_labels;
clusteringResults.IDX=IDX;
tempChar_unc =computeTemporalCharacteristics(TC_unc,clusteringResults,param);
save(fullfile(param.outDir_reg,'tempChar_unc.mat'),'tempChar_unc');
else
WriteInformation(fid,'Unconstrained regression has already been done, skipping...');
end
case 'transient-informed'
param.outDir_reg=fullfile(param.outDir_iCAPs,...
strrep(['TCs_' num2str(param.softClusterThres(1)) '_' ...
num2str(mean(diff(param.softClusterThres))) '_' ...
num2str(param.softClusterThres(end))],'.','DOT'));
if ~exist(param.outDir_reg,'dir');mkdir(param.outDir_reg);end
[~,~,~,Regression_ti_done,~] = Check_iCAPs_Files([],[],[],param.outDir_reg);
if isfield(param,'force_Regression') && param.force_Regression
Regression_ti_done=0;
end
if ~Regression_ti_done
WriteInformation(fid,'Loading aggregated data and iCAPs...');
clusteringResults=Load_ClusteringResults(param);% the first input variable is not used anymore, since I compute the path from param
for iT=1:length(param.softClusterThres)
WriteInformation(fid,['Soft assignment factor: ' num2str(param.softClusterThres(iT))]);
param_tmp=param;
param_tmp.softClusterThres=param.softClusterThres(iT);
[TC{iT},TC_stats{iT}]=GenerateTimeCoursesWeighted(clusteringResults,param_tmp);
tempChar{iT} =computeTemporalCharacteristics(TC{iT},clusteringResults,param_tmp);
end
save(fullfile(param.outDir_reg,'TC.mat'),'TC');
save(fullfile(param.outDir_reg,'TC_stats.mat'),'TC_stats');
save(fullfile(param.outDir_reg,'param.mat'),'param');
save(fullfile(param.outDir_reg,'tempChar.mat'),'tempChar');
if isfield(param,'evalAmplitudeCorrs') && param.evalAmplitudeCorrs
evaluateSoftClusterThres_corrs(clusteringResults,TC,param,fid)
end
% add a function to plot BIC and AIC (and maybe the
% correlation with measured amplitudes?)
best_id_bic=evaluateSoftClusterThres(TC_stats,param,fid);
TC=TC{best_id_bic};
TC_stats=TC_stats{best_id_bic};
tempChar=tempChar{best_id_bic};
best_val=strrep(num2str(param.softClusterThres(best_id_bic)),'.','DOT');
save(fullfile(param.outDir_iCAPs,['TC_' best_val '.mat']),'TC');
save(fullfile(param.outDir_iCAPs,['TC_stats_' best_val '.mat']),'TC_stats');
save(fullfile(param.outDir_iCAPs,['tempChar_' best_val '.mat']),'tempChar');
else
WriteInformation(fid,'Transient-informed regression has already been done, skipping...');
end
end
end
end
end
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