Extract fmriprep regressors for SPM model specification

extract_fmriprep_regressors_for_spm.m

% extracts confounds of interest from fmriprep timeseries.tsv
% and saves them for easier ingestion by SPM model specification
%
% the output should have a BIDS like structure too a bit like this
%
% spm12
% ├── CHANGES
% ├── dataset_description.json
% ├── README
% └── sub-01
%     └── stats
%         ├── sub-01_task-facerepetition_confounds.mat
%         └── sub-01_task-facerepetition_confounds.tsv
%
%
% (C) Copyright 2022 Remi Gau
        
path_to_fmriprep = fullfile(pwd, 'fmriprep');
output_folder = fullfile(pwd, 'spm12');

task_label = 'facerepetition';
space_label = 'MNI152NLin2009cAsym';

% set up some regular expression to identify the confounds we want to keep
confounds_of_interest = {'^rot_[xyz]$', '^trans_[xyz]$', '^*outlier*$'};

% index the content of the fmriprep data set and figure out which subjects we
% have
BIDS = bids.layout(path_to_fmriprep, 'use_schema', false); 
subjects = bids.query(BIDS, 'subjects');

% prepare the output folder structure
folders = struct('subjects', {subjects}, 'modalities', {{'stats'}});
bids.init(output_folder, 'folders', folders);

for i_sub = 1:numel(subjects)
  
  % create the filter to 
  filter = struct('sub', subjects{i_sub}, ...
                  'task', task_label, ...
                  'desc', 'confounds', ...
                  'suffix', 'timeseries');
  confound_files = bids.query(BIDS, 'data', filter);

  % loop through all the desc-confounds_timeseries.tsv
  % load it
  % get only the condounds we need 
  % save it in the output folder as a mat file and a TSV
  for i = 1:numel(confound_files)

    % for mat file
    names = {};
    R = [];
    
    % for TSV
    new_content = struct();
    
    % load
    content = bids.util.tsvread(confound_files{i});
    confounds_names = fieldnames(content);
    
    % create a logical vector to identify which confounds to keep
    % and store confounds to keep in new variables
    confounds_to_keep = regexp(confounds_names, ...
                               strjoin(confounds_of_interest, '|'));
    confounds_to_keep = ~cellfun('isempty', confounds_to_keep);
    
    confounds_names = confounds_names(confounds_to_keep);
    
    for j = 1:numel(confounds_names)
      % for mat file
      names{j} = confounds_names{j};
      R(:,j) = content.(confounds_names{j});
      
      % for TSV
      new_content.(confounds_names{j}) = content.(confounds_names{j});
    end
    
    % save to mat and TSV
    output_file_name = bids.File(confound_files{i});
    output_file_name.entities.desc = '';
    output_file_name.suffix = 'confounds';
    
    output_file_name.path = fullfile(output_folder, ['sub-' subjects{i_sub}], 'stats');
    
    bids.util.tsvwrite(fullfile(output_file_name.path, output_file_name.filename), ...
                       new_content);

    output_file_name.extension = '.mat';
    save(fullfile(output_file_name.path, output_file_name.filename), 'names', 'R');
                     
  end
  
end