-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathSPM_auditory_word_frequency_1st_level.m
196 lines (181 loc) · 11 KB
/
SPM_auditory_word_frequency_1st_level.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
%2024 SPM auditory
subject_path = '/media/neel/MOUS/MOUS/MOUS/fmriprep_fresh';
outdir = '/home/neel/Documents/SPM_results/SPM-A_multireg_neg-contrast_test';
sourcedir = '/media/neel/MOUS/MOUS/MOUS/SynologyDrive/source';
mkdir(outdir)
cd(subject_path)
%mkdir SPM-A
subjects = dir('sub-A*');
subjNames = extractfield(subjects, 'name');
cd('/home/neel/Desktop/MOUS_hierarchical-representations')
for m = 1:length(subjNames) %subj index.
currentName = subjNames(m)
%if using new regressors:
regressors = readtable(char(fullfile(sourcedir, currentName, 'func',strcat(currentName,'_word_frequencies.csv'))));
transcription = readtable(char(fullfile(sourcedir,currentName,'func',strcat(currentName,'_transcription.csv'))));
%if using old regressors:
%regressors = readtable(char(fullfile(sourcedir, currentName, 'func',strcat(currentName,'_regressors.xlsx'))));
% %Remove rows with NaN values
% notmissingidx = ~ismissing(regressors);
% removedidx = find(~notmissingidx);
% transcription = transcription(notmissingidx(:,3),:);
% regressors = rmmissing(regressors);
% replace rows with NaN values with 0s
numericVars = varfun(@isnumeric, regressors, 'OutputFormat', 'uniform');
regressors{:, numericVars} = fillmissing(regressors{:, numericVars}, 'constant', 0);
%%0. Coregister. Uncomment and modify the below if this was not done by fmriprep.
% matlabbatch{1}.spm.spatial.coreg.estwrite.ref = {'/media/MOUS/MOUS/SynologyDrive/source/sub-A2124/func/sub-A2124_task-auditory_bold.nii,1'}; %functional. expand.
% matlabbatch{1}.spm.spatial.coreg.estwrite.source = {'/media/MOUS/MOUS/SynologyDrive/source/sub-A2124/anat/sub-A2124_T1w.nii,1'}; %anatomical.
% matlabbatch{1}.spm.spatial.coreg.estwrite.other = {''};
% matlabbatch{1}.spm.spatial.coreg.estwrite.eoptions.cost_fun = 'nmi';
% matlabbatch{1}.spm.spatial.coreg.estwrite.eoptions.sep = [4 2];
% matlabbatch{1}.spm.spatial.coreg.estwrite.eoptions.tol = [0.02 0.02 0.02 0.001 0.001 0.001 0.01 0.01 0.01 0.001 0.001 0.001];
% matlabbatch{1}.spm.spatial.coreg.estwrite.eoptions.fwhm = [7 7];
% matlabbatch{1}.spm.spatial.coreg.estwrite.roptions.interp = 4;
% matlabbatch{1}.spm.spatial.coreg.estwrite.roptions.wrap = [0 0 0];
% matlabbatch{1}.spm.spatial.coreg.estwrite.roptions.mask = 0;
% matlabbatch{1}.spm.spatial.coreg.estwrite.roptions.prefix = 'april_coreg';
%
% %Segmentation (necessary for normalization)
% matlabbatch{1}.spm.spatial.preproc.channel.vols = {'april_coreg'};
% matlabbatch{1}.spm.spatial.preproc.channel.biasreg = 0.001;
% matlabbatch{1}.spm.spatial.preproc.channel.biasfwhm = 60;
% matlabbatch{1}.spm.spatial.preproc.channel.write = [0 1];
% matlabbatch{1}.spm.spatial.preproc.tissue(1).tpm = {'/home/neelbanerjee/Documents/spm12/tpm/TPM.nii,1'};
% matlabbatch{1}.spm.spatial.preproc.tissue(1).ngaus = 1;
% matlabbatch{1}.spm.spatial.preproc.tissue(1).native = [1 0];
% matlabbatch{1}.spm.spatial.preproc.tissue(1).warped = [0 0];
% matlabbatch{1}.spm.spatial.preproc.tissue(2).tpm = {'/home/neelbanerjee/Documents/spm12/tpm/TPM.nii,2'};
% matlabbatch{1}.spm.spatial.preproc.tissue(2).ngaus = 1;
% matlabbatch{1}.spm.spatial.preproc.tissue(2).native = [1 0];
% matlabbatch{1}.spm.spatial.preproc.tissue(2).warped = [0 0];
% matlabbatch{1}.spm.spatial.preproc.tissue(3).tpm = {'/home/neelbanerjee/Documents/spm12/tpm/TPM.nii,3'};
% matlabbatch{1}.spm.spatial.preproc.tissue(3).ngaus = 2;
% matlabbatch{1}.spm.spatial.preproc.tissue(3).native = [1 0];
% matlabbatch{1}.spm.spatial.preproc.tissue(3).warped = [0 0];
% matlabbatch{1}.spm.spatial.preproc.tissue(4).tpm = {'/home/neelbanerjee/Documents/spm12/tpm/TPM.nii,4'};
% matlabbatch{1}.spm.spatial.preproc.tissue(4).ngaus = 3;
% matlabbatch{1}.spm.spatial.preproc.tissue(4).native = [1 0];
% matlabbatch{1}.spm.spatial.preproc.tissue(4).warped = [0 0];
% matlabbatch{1}.spm.spatial.preproc.tissue(5).tpm = {'/home/neelbanerjee/Documents/spm12/tpm/TPM.nii,5'};
% matlabbatch{1}.spm.spatial.preproc.tissue(5).ngaus = 4;
% matlabbatch{1}.spm.spatial.preproc.tissue(5).native = [1 0];
% matlabbatch{1}.spm.spatial.preproc.tissue(5).warped = [0 0];
% matlabbatch{1}.spm.spatial.preproc.tissue(6).tpm = {'/home/neelbanerjee/Documents/spm12/tpm/TPM.nii,6'};
% matlabbatch{1}.spm.spatial.preproc.tissue(6).ngaus = 2;
% matlabbatch{1}.spm.spatial.preproc.tissue(6).native = [0 0];
% matlabbatch{1}.spm.spatial.preproc.tissue(6).warped = [0 0];
% matlabbatch{1}.spm.spatial.preproc.warp.mrf = 1;
% matlabbatch{1}.spm.spatial.preproc.warp.cleanup = 1;
% matlabbatch{1}.spm.spatial.preproc.warp.reg = [0 0.001 0.5 0.05 0.2];
% matlabbatch{1}.spm.spatial.preproc.warp.affreg = 'mni';
% matlabbatch{1}.spm.spatial.preproc.warp.fwhm = 0;
% matlabbatch{1}.spm.spatial.preproc.warp.samp = 3;
% matlabbatch{1}.spm.spatial.preproc.warp.write = [0 1];
% matlabbatch{1}.spm.spatial.preproc.warp.vox = NaN;
% matlabbatch{1}.spm.spatial.preproc.warp.bb = [NaN NaN NaN
% NaN NaN NaN];
%1. FILE FINDING
%gunzip
if isfile(char(fullfile(subject_path,currentName,'func',strcat(currentName,'_task-auditory_space-MNI152NLin6Asym_res-2_desc-preproc_bold.nii.gz'))))
gunzip(char(fullfile(subject_path,currentName,'func',strcat(currentName,'_task-auditory_space-MNI152NLin6Asym_res-2_desc-preproc_bold.nii.gz'))))
delete(char(fullfile(subject_path,currentName,'func',strcat(currentName,'_task-auditory_space-MNI152NLin6Asym_res-2_desc-preproc_bold.nii.gz'))))
end
try
ims = cellstr(spm_select('expand',[fullfile(subject_path, currentName, '/func/', strcat(currentName, '_task-auditory_space-MNI152NLin6Asym_res-2_desc-preproc_bold.nii'))]));
disp('Scans located')
catch
continue
end
%2. SMOOTHING
%%uncomment if running for the first time!
% 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 = 'J';
% spm_jobman('run',matlabbatch)
% disp('Smoothing Complete')
% clear matlabbatch
%3. FIRST LEVEL ANALYSIS
%directory setup and scanning parameters
mkdir(char(fullfile(outdir, currentName)))
disp('Directory Created')
AnalysisDirectory = 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(char(fullfile(subject_path,currentName, 'func')))
SmoothedScan = dir('J*.nii');
if isempty(SmoothedScan)
disp(['No smoothed scans found for subject: ', currentName]);
continue;
end
cd(subject_path)
%regressor 1, onset
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.name = 'Onset';
matlabbatch{1}.spm.stats.fmri_spec.sess.cond.onset= regressors.AlignOnset;
matlabbatch{1}.spm.stats.fmri_spec.sess.cond.duration = 0;
matlabbatch{1}.spm.stats.fmri_spec.sess.cond.tmod = 0;
%length control. used to test effects of word length/duration, but not part of final analysis.
matlabbatch{1}.spm.stats.fmri_spec.sess.cond.pmod(1).name = 'Word Length (seconds)';
matlabbatch{1}.spm.stats.fmri_spec.sess.cond.pmod(1).param = transcription.Duration; %transcription.Duration - mean(transcription.Duration)%demean
matlabbatch{1}.spm.stats.fmri_spec.sess.cond.pmod(1).poly = 1;
%regressor 2, frequency
matlabbatch{1}.spm.stats.fmri_spec.sess.cond.pmod(2).name = 'Frequency';
matlabbatch{1}.spm.stats.fmri_spec.sess.cond.pmod(2).param = 0 - regressors.Zipf; %Lg10WF and Zipf represent two alternate logarithmic measures of word frequency.
matlabbatch{1}.spm.stats.fmri_spec.sess.cond.pmod(2).poly = 1;
matlabbatch{1}.spm.stats.fmri_spec.sess.cond.orth = 0;
matlabbatch{1}.spm.stats.fmri_spec.sess.multi = {''};
matlabbatch{1}.spm.stats.fmri_spec.sess.regress = struct('name', {}, 'val', {});
%Motion regressors. These should be produced by fmriprep.
ConfoundsRegressors = tdfread(char(fullfile(subject_path, currentName, '/func/', strcat(currentName, '_task-auditory_desc-confounds_regressors.tsv'))));
%rp_name = [ConfoundsRegressors.trans_x, ConfoundsRegressors.rot_x, ConfoundsRegressors.trans_y, ConfoundsRegressors.rot_y, ConfoundsRegressors.trans_z, ConfoundsRegressors.rot_z];
% Define the subject path and file name
% Extract the relevant columns
motion_regressors = [ConfoundsRegressors.trans_x, ConfoundsRegressors.rot_x, ...
ConfoundsRegressors.trans_y, ConfoundsRegressors.rot_y, ...
ConfoundsRegressors.trans_z, ConfoundsRegressors.rot_z];
% Define the output file name
output_file = fullfile(subject_path, currentName, '/func/', strcat(currentName, '_motion_regressors.txt'));
% Write the motion regressors to a text file
if ~exist(char(output_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')))};
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 = -Inf; %default is 0.8
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 = {char(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) = {char(fullfile(AnalysisDirectory, 'SPM.mat'))};
matlabbatch{1}.spm.stats.con.consess{1}.tcon.name = 'Frequency';
matlabbatch{1}.spm.stats.con.consess{1}.tcon.weights = [0 0 1 0]; %edit if including duration control.
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