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Copy pathSPM_visual_max_bigram_frequency_1st_level.m
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SPM_visual_max_bigram_frequency_1st_level.m
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%1st Level Visual Word Frequency Script
%Replace with directory containing preprocessed subject data.
subject_path = '/media/neel/MOUS/MOUS/MOUS/fmriprep_fresh';
%replace with directory containing source data.
source = '/media/neel/MOUS/MOUS/MOUS/SynologyDrive/source'
%replace with directory for output.
outdir = '/home/neel/Documents/SPM_results/alternate_parameter_contrasts/SPM-V_Lg10_max_bigram'
if ~exist(outdir,'dir')
mkdir(outdir)
end
cd(subject_path)
subjects = dir('sub-V*');
subjNames = extractfield(subjects, 'name');
for v=1:length(subjNames)
currentName = char(subjNames(v))
regressors = readtable(char(fullfile(source, currentName, 'func', strcat(currentName,'_bigrams_processed_max_mean.csv'))),'Delimiter',',');
% Remove rows with NaN values
%regressors = rmmissing(regressors);
%1. File Selection
try
ims = cellstr(spm_select('expand',[fullfile(subject_path, currentName, '/func/', strcat(currentName, '_task-visual_space-MNI152NLin6Asym_res-2_desc-preproc_bold.nii'))]));
catch
disp(strcat(currentName + " does not have task scans"))
continue
end
disp('Scans located')
%2. Smoothing
% matlabbatch{1}.spm.spatial.smooth.data = ims;
% matlabbatch{1}.spm.spatial.smooth.fwhm = [6 6 6];
% matlabbatch{1}.spm.spatial.smooth.dtype = 0;
% matlabbatch{1}.spm.spatial.smooth.im = 0;
% matlabbatch{1}.spm.spatial.smooth.prefix = 'z';
% spm_jobman('run',matlabbatch)
% disp('Smoothing Complete')
clear matlabbatch
%3. 1stLevel Analysis
mkdir(fullfile(outdir, currentName))
disp('Directory Created')
AnalysisDirectory = char(fullfile(outdir, currentName));
if exist(char(fullfile(AnalysisDirectory, 'SPM.mat')))
continue
end
matlabbatch{1}.spm.stats.fmri_spec.dir = {AnalysisDirectory};
matlabbatch{1}.spm.stats.fmri_spec.timing.units = 'secs';
matlabbatch{1}.spm.stats.fmri_spec.timing.RT = 2;
matlabbatch{1}.spm.stats.fmri_spec.timing.fmri_t = 16;
matlabbatch{1}.spm.stats.fmri_spec.timing.fmri_t0 = 8;
%Load Smoothed Scans
cd(strcat(subject_path, '/', currentName, '/func/'))
SmoothedScan = dir('G*.nii') %either this or 'ssub-*'
if isempty(SmoothedScan)
disp(['No smoothed scans found for subject: ', currentName]);
continue;
end
cd(subject_path)
%Return to Batch
matlabbatch{1}.spm.stats.fmri_spec.sess.scans = cellstr(spm_select('expand', [fullfile(SmoothedScan.folder, SmoothedScan.name)]));
matlabbatch{1}.spm.stats.fmri_spec.sess.cond(1).name = 'Word';
%Get master document of word data
matlabbatch{1}.spm.stats.fmri_spec.sess.cond(1).onset = regressors.Onset
matlabbatch{1}.spm.stats.fmri_spec.sess.cond(1).duration = 0;
matlabbatch{1}.spm.stats.fmri_spec.sess.cond(1).tmod = 0;
%should length control be applied here?
% matlabbatch{1}.spm.stats.fmri_spec.sess.cond(1).pmod(1).name = 'Word Length';
% matlabbatch{1}.spm.stats.fmri_spec.sess.cond(1).pmod(1).param = regressors.WordLength %- mean(regressors.log10_Min_Bigram);
% matlabbatch{1}.spm.stats.fmri_spec.sess.cond(1).pmod(1).poly = 1;
matlabbatch{1}.spm.stats.fmri_spec.sess.cond(1).pmod(1).name = 'Max Bigram Frequency';
matlabbatch{1}.spm.stats.fmri_spec.sess.cond(1).pmod(1).param = 0 - regressors.log10_Max_Bigram %- mean(regressors.log10_Min_Bigram);
matlabbatch{1}.spm.stats.fmri_spec.sess.cond(1).pmod(1).poly = 1;
matlabbatch{1}.spm.stats.fmri_spec.sess.cond(1).orth = 0;
matlabbatch{1}.spm.stats.fmri_spec.sess.multi = {''};
matlabbatch{1}.spm.stats.fmri_spec.sess.regress = struct('name', {}, 'val', {});
%Motion regressors (produced by fmriprep)
ConfoundsRegressors = tdfread(fullfile(subject_path, currentName, '/func/', strcat(currentName, '_task-visual_desc-confounds_regressors.tsv')));
% Extract the relevant columns
motion_regressors = [ConfoundsRegressors.trans_x, ConfoundsRegressors.rot_x, ...
ConfoundsRegressors.trans_y, ConfoundsRegressors.rot_y, ...
ConfoundsRegressors.trans_z, ConfoundsRegressors.rot_z];
output_file = fullfile(subject_path, currentName, '/func/', strcat(currentName, '_motion_regressors.txt'));
% Write the motion regressors to a text file
if ~exist(output_file, 'file')
dlmwrite(char(output_file), motion_regressors, 'delimiter', '\t', 'precision', 6);
disp(['Motion regressors written to: ', output_file]);
end
matlabbatch{1}.spm.stats.fmri_spec.sess.multi_reg = {char(fullfile(subject_path, currentName, '/func/', strcat(currentName, '_motion_regressors.txt')))};
%end of motion regressor lines
matlabbatch{1}.spm.stats.fmri_spec.sess.hpf = 128;
matlabbatch{1}.spm.stats.fmri_spec.fact = struct('name', {}, 'levels', {});
matlabbatch{1}.spm.stats.fmri_spec.bases.hrf.derivs = [0 0];
matlabbatch{1}.spm.stats.fmri_spec.volt = 1;
matlabbatch{1}.spm.stats.fmri_spec.global = 'None';
matlabbatch{1}.spm.stats.fmri_spec.mthresh = 0.8; %could change this.
matlabbatch{1}.spm.stats.fmri_spec.mask = {''};
matlabbatch{1}.spm.stats.fmri_spec.cvi = 'AR(1)';
spm_jobman('run',matlabbatch)
disp('Model Specified')
clear matlabbatch
%4. Estimation
matlabbatch{1}.spm.stats.fmri_est.spmmat = {fullfile(AnalysisDirectory, 'SPM.mat')};
matlabbatch{1}.spm.stats.fmri_est.write_residuals = 0;
matlabbatch{1}.spm.stats.fmri_est.method.Classical = 1;
spm_jobman('run',matlabbatch)
disp('Model Estimated')
clear matlabbatch
%5. Contrast
matlabbatch{1}.spm.stats.con.spmmat(1) = {fullfile(AnalysisDirectory, 'SPM.mat')};
matlabbatch{1}.spm.stats.con.consess{1}.tcon.name = char(strcat("Max Bigram Frequency Correlation"));
matlabbatch{1}.spm.stats.con.consess{1}.tcon.weights = [0 1 0];
matlabbatch{1}.spm.stats.con.consess{1}.tcon.sessrep = 'none';
matlabbatch{1}.spm.stats.con.delete = 0;
spm_jobman('run',matlabbatch)
disp('Contrast tested!')
clear matlabbatch
end