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iCAPs_v2/functions/Run_Thresholding.m
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Run_Thresholding.m
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%% This function runs thresholding on innovations, according to the
% parameters specified by the user
%
% Input:
% param - struct containing all necessary parameters to run TA
% .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
% [.force_Thresholding] - if set to 1, Thresholding will be forced to
% run, even if already has been done
% .alpha - Alpha-level at which to look for significance of
% innovation signal frames (first element is the percentile of
% the lower threshold - negative innovations - and second element
% the upper threshold one - positive innovations)
% .f_voxels - Fraction of voxels from the ones entering total
% activation for a given subject that should show an innovation
% at the same time point, so that the corresponding frame is
% retained for iCAPs clustering
% .thresh_title - Title used to create the folder where thresholding
% data will be saved
% .threshold_minclussize - Number of neighbours that must also show
% an innovation for a voxel to be retained
% .threshold_interconnectivity - Number of neighbours to consider in
% the process
%
% Output:
% Cycles through the subjects and thresholds their data:
% 1) temporal threshold: takes the innovation signals generated from
% the surrogate data, builds a distribution from them, samples the
% X-th percentiles from the distribution at each voxel mask temporal
% frames from the real data with this
% 2) spatial threshold: mark frames where X percent of significant
% voxels
function [] = Run_Thresholding(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
% Will contain the parameters initially entered by the user, prior to any
% change within the loop
param_CI = param;
% 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_Thresholding_',param.title,'.txt']),'a+');
WriteInformation(fid,['\nStarting 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
WriteInformation(fid,'Entering the thresholding process...');
% Loops over all the subjects to analyze
for iS = 1:param.n_subjects
% Path towards the data of the subject of interest
SubjPath = fullfile(param.PathData,param.Subjects{iS});
WriteInformation(fid,['Analyzing subject ',SubjPath,'...']);
if ~exist(SubjPath,'dir')
WriteInformation(fid,['Incorrect subject path ',SubjPath,': ignored subject']);
continue
end
% Creates the folder that will contain total activation results
% for the considered subject if it does not exist yet
resultsPath=fullfile(SubjPath,'TA_results',param.title);
% check whether thresholding was already executed for this subject
[TA_real_done,TA_surrogate_done,Thresholding_done] = Check_TA_Files(resultsPath,param.thresh_title);
if ~TA_real_done || TA_surrogate_done~=1
WriteInformation(fid,['No total activation results, run TA routine first! skipping...\n']);
continue;
end
if isfield(param,'force_Thresholding') && param.force_Thresholding
Thresholding_done=0;
end
if ~Thresholding_done
% loading data
WriteInformation(fid,['Loading total activation data...']);
load(fullfile(resultsPath,'TotalActivation','Innovation'));
load(fullfile(resultsPath,'Surrogate','Innovation_surrogate'));
ptmp=load(fullfile(resultsPath,'TotalActivation','param'));
param.fHeader=ptmp.param.fHeader;
param.mask=ptmp.param.mask;
param.Dimension=ptmp.param.Dimension;
param.TemporalMask=ptmp.param.TemporalMask;
clear ptmp
WriteInformation(fid,['Removing NaNs from innovations...']);
[Innovation_surrogate,param.mask_nonan,param.mask2_nonan] = RemoveNan(Innovation_surrogate,param,fid);
WriteInformation(fid,['Computing percentiles...']);
param.PC = ComputeSurrogatePercentiles(Innovation_surrogate,param,fid);
WriteInformation(fid,['Selecting significant innovation frames...']);
[SignInnov,param] = SelectSignificantFrames(Innovation(:,param.mask2_nonan),param,fid);
WriteInformation(fid,['Saving...']);
save4Dnii(fullfile(resultsPath,'Thresholding'),param.thresh_title,'SignInnov',SignInnov',param.fHeader.fname,param.mask_nonan,param.Dimension);
save4Dnii(fullfile(resultsPath,'Thresholding'),param.thresh_title,'mask_nonan',param.mask_nonan,param.fHeader.fname,param.mask,param.Dimension);
save(fullfile(resultsPath,'Thresholding',param.thresh_title,'SignInnov'),'SignInnov','-v7.3');
save(fullfile(resultsPath,'Thresholding',param.thresh_title,'param'),'param','-v7.3');
WriteInformation(fid,['Finished running thresholding for subject ',SubjPath,'...\n']);
elseif Thresholding_done==1
WriteInformation(fid,'Thresholding already done, skipping...\n');
end
% Resets the parameters to what they were at the start of the loop
% (before any subject-specific change could have been made
clear param
param = param_CI;
end
end
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