[UK24] Reproduction #161
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| % first-level job ---- | ||
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| % $ narps_description -t UK24 -d preprocessing --json | ||
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| clear matlabbatch; | ||
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| % We consider that anat and func data have been gunzipped first | ||
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| % % gunzip anat | ||
| % matlabbatch{1}.cfg_basicio.file_dir.file_ops.cfg_gunzip_files.files = {'/Users/camaumet/Softs/narps_open_pipelines/data/original/ds001734/sub-001/anat/sub-001_T1w.nii.gz'}; | ||
| % matlabbatch{1}.cfg_basicio.file_dir.file_ops.cfg_gunzip_files.outdir = {''}; | ||
| % matlabbatch{1}.cfg_basicio.file_dir.file_ops.cfg_gunzip_files.keep = true; | ||
| % | ||
| % % gunzip sbref | ||
| % matlabbatch{end+1}.cfg_basicio.file_dir.file_ops.cfg_gunzip_files.files = {'/Users/camaumet/Softs/narps_open_pipelines/data/original/ds001734/sub-001/anat/sub-001_T1w.nii.gz'}; | ||
| % matlabbatch{end}.cfg_basicio.file_dir.file_ops.cfg_gunzip_files.outdir = {''}; | ||
| % matlabbatch{end}.cfg_basicio.file_dir.file_ops.cfg_gunzip_files.keep = true; | ||
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| % 1 - Coregistration of the structural T1w image to the reference | ||
| % functional image (defined as the single volume 'sbref' image acquired | ||
| % before the first functional run) \n | ||
| matlabbatch{1}.spm.spatial.coreg.estimate.ref(1) = {... | ||
| '/ABS_PATH/data/original/ds001734/sub-001/func/sub-001_task-MGT_run-01_sbref.nii,1'}; %SBREF_FILE | ||
| matlabbatch{end}.spm.spatial.coreg.estimate.source(1) = {... | ||
| '/ABS_PATH/data/original/ds001734/sub-001/anat/sub-001_T1w.nii,1'}; %ANAT_FILE | ||
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| % 2 - Segmentation of the coregistered T1w image into grey matter, white | ||
| % matter and cerebrospinal fluid tissue maps.\n | ||
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| % note le fichier ANAT va garder le meme nom mais en pratique il y a une dependance sur la sortie du coreg | ||
| matlabbatch{end+1}.spm.spatial.preproc.channel.vols = {... | ||
| '/ABS_PATH/data/original/ds001734/sub-001/anat/sub-001_T1w.nii,1'}; % this is the coregistered ANAT | ||
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| % --> output ANAT_SEG_PARAM | ||
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| % 3 - Reslicing of coregistered T1w image and segmentations to the same | ||
| % voxel space of the reference (sbref) image | ||
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| % Note Camille: I am not sure how to do this with the matlabbatch (imcalc | ||
| % should work but maybe there is a more direct option) | ||
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| % spm_reslice{{ANAT, c1ANAT, c2ANAT, c3ANAT}, SBREF} | ||
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| % 4 - Functional MRI volume realignment: first realigning all 4 'sbref' | ||
| % images to the one acquired before the first run; followed by realignment | ||
| % of all volumes in a particular run to the first image in that run. | ||
| % This is done implictly with multi-session realignment in SPM12. | ||
| matlabbatch{end+1}.spm.spatial.realign.estwrite.data = { | ||
| { | ||
| %Note: I included only 3 volumes for the sub-001_task-MGT_run-01_bold | ||
| %but in practice there should be as many as volumes in the 4d file | ||
| 'ABS_PATH/narps_open_pipelines/data/original/ds001734/sub-001/func/sub-001_task-MGT_run-01_sbref.nii,1' | ||
| 'ABS_PATH/narps_open_pipelines/data/original/ds001734/sub-001/func/sub-001_task-MGT_run-01_bold.nii,1' | ||
| 'ABS_PATH/narps_open_pipelines/data/original/ds001734/sub-001/func/sub-001_task-MGT_run-01_bold.nii,2' | ||
| 'ABS_PATH/narps_open_pipelines/data/original/ds001734/sub-001/func/sub-001_task-MGT_run-01_bold.nii,3' | ||
| }, | ||
| { | ||
| %Note: I included only 3 volumes for the sub-001_task-MGT_run-02_bold | ||
| %but in practice there should be as many as volumes in the 4d file | ||
| 'ABS_PATH/narps_open_pipelines/data/original/ds001734/sub-001/func/sub-001_task-MGT_run-02_sbref.nii,1' | ||
| 'ABS_PATH/narps_open_pipelines/data/original/ds001734/sub-001/func/sub-001_task-MGT_run-02_bold.nii,1' | ||
| 'ABS_PATH/narps_open_pipelines/data/original/ds001734/sub-001/func/sub-001_task-MGT_run-02_bold.nii,2' | ||
| 'ABS_PATH/narps_open_pipelines/data/original/ds001734/sub-001/func/sub-001_task-MGT_run-02_bold.nii,3' | ||
| }, | ||
| { | ||
| %Note: I included only 3 volumes for the sub-001_task-MGT_run-03_bold | ||
| %but in practice there should be as many as volumes in the 4d file | ||
| 'ABS_PATH/narps_open_pipelines/data/original/ds001734/sub-001/func/sub-001_task-MGT_run-03_sbref.nii,1' | ||
| 'ABS_PATH/narps_open_pipelines/data/original/ds001734/sub-001/func/sub-001_task-MGT_run-03_bold.nii,1' | ||
| 'ABS_PATH/narps_open_pipelines/data/original/ds001734/sub-001/func/sub-001_task-MGT_run-03_bold.nii,2' | ||
| 'ABS_PATH/narps_open_pipelines/data/original/ds001734/sub-001/func/sub-001_task-MGT_run-03_bold.nii,3' | ||
| }, | ||
| { | ||
| %Note: I included only 3 volumes for the sub-001_task-MGT_run-04_bold | ||
| %but in practice there should be as many as volumes in the 4d file | ||
| 'ABS_PATH/narps_open_pipelines/data/original/ds001734/sub-001/func/sub-001_task-MGT_run-04_sbref.nii,1' | ||
| 'ABS_PATH/narps_open_pipelines/data/original/ds001734/sub-001/func/sub-001_task-MGT_run-04_bold.nii,1' | ||
| 'ABS_PATH/narps_open_pipelines/data/original/ds001734/sub-001/func/sub-001_task-MGT_run-04_bold.nii,2' | ||
| 'ABS_PATH/narps_open_pipelines/data/original/ds001734/sub-001/func/sub-001_task-MGT_run-04_bold.nii,3' | ||
| } | ||
| }'; | ||
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| % 5 - Spatial smoothing of fMRI data. | ||
| % "spatial_smoothing": "Spatial smoothing was conducted on realigned | ||
| % functional data using SPM12's standard smoothing function, which used a | ||
| % fixed Gaussian smoothing kernel with FWHM of 4mm (twice the voxel size). | ||
| % Smoothing was performed in the native functional space.", | ||
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| matlabbatch{end+1}.spm.spatial.smooth.data = { | ||
| 'ABS_PATH/narps_open_pipelines/data/original/ds001734/sub-001/func/rsub-001_task-MGT_run-01_sbref.nii' | ||
| 'ABS_PATH/narps_open_pipelines/data/original/ds001734/sub-001/func/rsub-001_task-MGT_run-02_bold.nii' | ||
| 'ABS_PATH/narps_open_pipelines/data/original/ds001734/sub-001/func/rsub-001_task-MGT_run-03_bold.nii' | ||
| 'ABS_PATH/narps_open_pipelines/data/original/ds001734/sub-001/func/rsub-001_task-MGT_run-04_bold.nii' | ||
| }; | ||
| matlabbatch{end}.spm.spatial.smooth.fwhm = [4 4 4]; | ||
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| % 6 - Calculation of several quality metrics based on preprocessed data, | ||
| % including framewise displacement derived from motion correction | ||
| % (realignment) parameters. | ||
| % The method was adapted from Power et al. (2012), | ||
| % using 50mm as the assumed radius for translating rotation parameters | ||
| % into displacements, assuming small angle approximations. A threshold of | ||
| % 0.5 mm was set to define so-called 'motion outlier' volumes, which after | ||
| % application to the FD timeseries yielded a binary 'scrubbing' regressor | ||
| % per run. | ||
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| % Note: here we can use FramewiseDisplacement from nipype that to compute | ||
| % the framewise displacement from the motion parameters that are available | ||
| % in the rp_ files generated after realignment. | ||
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| % 7 - Generation of nuisance regressors for | ||
| % 1st level analysis, including motion scrubbing regressor defined using | ||
| % framewise displacement, and two tissue signal regressors (white matter | ||
| % and cerebrospinal fluid) derived from averaging the voxel values found | ||
| % within the segmentaed tissue maps. | ||
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| % Scrubbing nuisance regressor: any functional volume that has a | ||
| % framewise displacement greater than 0.5mm is set to one in the scrubbing | ||
| % nuisance covariate (all other volumes are set to zero) | ||
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| % --> Save as reg_scrubbing.txt | ||
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| % white matter signal nuisance: average value of voxels in th functional | ||
| % volume that are part of white matter (as computed using the segmentation, | ||
| % threshold>0.5?) | ||
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| % --> Save as reg_wm.txt | ||
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| % CSF signal nuisance: average value of voxels in th functional | ||
| % volume that are part of white matter (as computed using the segmentation, | ||
| % threshold>0.5?) | ||
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| % --> Save as reg_csf.txt | ||
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| % "independent_vars_first_level": "The GLM design matrix for the 1st level | ||
| % analysis consited of 17 explicitly specified regressors per run. Of these | ||
| % regressors, the first 8 related to experimental conditions, whereas the | ||
| % remaining 9 were nuisance regressors (6 head motion parameter regressors, | ||
| % 2 tissue signal regressors, and 1 scrubbing regressor), none of which | ||
| % were entered as interactions. | ||
| % For the experimental conditions, we are | ||
| % interested in the parametric effect of gain and of loss as related to a | ||
| % mixed gambles task. Trial data, indicating the amount of possible gain | ||
| % and the amount of possible loss, as well as trial timing, trial durations | ||
| % and reaction times were supplied for each run per subject. From this data | ||
| % a block-task regressor was derived respectively for all trials where the | ||
| % gamble could result in a possible gain, in a possible loss, and in a | ||
| % no-loss-nor-gain situation (i.e. 3 block-task regressors). In each case | ||
| % of the possible gain/loss regressors, 2 parametric modulation regressors | ||
| % were added: 1 to model the parametric size of the possible gain/loss, and | ||
| % 1 to model the parametric size of the reaction time. In the case of the | ||
| % no-loss-nor-gain block-task regressor a single extra parametric | ||
| % modulation regressor was added to model the parametric size of the | ||
| % reaction time. | ||
| % All parametric modulation regressors were mean centered | ||
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There was a problem hiding this comment. Is it the mean value computed for each run, or one for all runs ? There was a problem hiding this comment. @bclenet 'mean centering' should be done automatically as part of the design (this is an option in the model). I can't open Matlab (no more licenses) but I'll check as soon as I can :) |
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| % before adding them to the design matrix for the GLM analysis, which | ||
| % implicitly (in SPM12) orthogonalises all parametric modulators in a | ||
| % series with reference to the first one. No time-modulation of conditions | ||
| % was included.\n\nThe canonical HRF basis (default SPM12 option) was used | ||
| % for convolution of the 8 conditions. SPM12 automatically also included a | ||
| % constant regressor in the design matrix, and filtered the data and design | ||
| % matrix using a discrete cosine basis set with a specified cutoff | ||
| % frequency of 128 Hz (default). This accounted for slow drifts in the data | ||
| % , and no explicit drift regressors were included in the design matrix." | ||
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| % Note: the covariates for tissue signal regressors and scrubbing regressor | ||
| % not implemented yet | ||
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| % 8 regresseurs : gain, gain_value, gain_RT, loss, loss_value, loss_RT, no gain no loss, RT no gain no loss | ||
| % 9 nuisance : 6 head motion parameter regressors, 2 tissue signal regressors, and 1 scrubbing regressor | ||
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| matlabbatch{end+1}.spm.stats.fmri_spec.dir = '<UNDEFINED>'; | ||
| matlabbatch{end}.spm.stats.fmri_spec.timing.units = 'secs'; | ||
| matlabbatch{end}.spm.stats.fmri_spec.timing.RT = '<UNDEFINED>'; | ||
| matlabbatch{end}.spm.stats.fmri_spec.timing.fmri_t = 16; | ||
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There was a problem hiding this comment. What is the purpose of these parameters ( There was a problem hiding this comment. This is something I should have removed from the matlabbatch as this is just the default value. For info (as this might be useful for other parameters), each parameter of fMRI specification is described in this file: https://github.com/spm/spm12/blob/main/config/spm_cfg_fmri_spec.m (and in correspondant |
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| matlabbatch{end}.spm.stats.fmri_spec.timing.fmri_t0 = 8; | ||
| matlabbatch{end}.spm.stats.fmri_spec.sess.scans = '<UNDEFINED>'; | ||
| matlabbatch{end}.spm.stats.fmri_spec.sess.cond(1).name = 'gain'; | ||
| % Below we should include the 'onset' values from sub-001_task-MGT_run-01_events | ||
| % for which we have participant_response=accept (either weakly or strongly) | ||
| matlabbatch{end}.spm.stats.fmri_spec.sess.cond(1).onset = '<UNDEFINED>'; | ||
| matlabbatch{end}.spm.stats.fmri_spec.sess.cond(1).duration = 4; | ||
| matlabbatch{end}.spm.stats.fmri_spec.sess.cond(1).pmod(1).name = 'gain_value'; | ||
| % Below we should include the 'gain' values from sub-001_task-MGT_run-01_events | ||
| % for which we have participant_response=accept (either weakly or strongly) | ||
| matlabbatch{end}.spm.stats.fmri_spec.sess.cond(1).pmod(1).param = '<UNDEFINED>'; | ||
| matlabbatch{end}.spm.stats.fmri_spec.sess.cond(1).pmod(1).poly = 1; | ||
| matlabbatch{end}.spm.stats.fmri_spec.sess.cond(1).pmod(2).name = 'gain_RT'; | ||
| % Below we should include the 'RT' values from sub-001_task-MGT_run-01_events | ||
| % for which we have participant_response=accept (either weakly or strongly) | ||
| matlabbatch{end}.spm.stats.fmri_spec.sess.cond(1).pmod(2).param = '<UNDEFINED>'; | ||
| matlabbatch{end}.spm.stats.fmri_spec.sess.cond(1).pmod(2).poly = 1; | ||
| matlabbatch{end}.spm.stats.fmri_spec.sess.cond(1).orth = 1; | ||
| matlabbatch{end}.spm.stats.fmri_spec.sess.cond(2).name = 'loss'; | ||
| % Below we should include the 'onset' values from sub-001_task-MGT_run-01_events | ||
| % for which we have participant_response=accept (either weakly or strongly) | ||
| matlabbatch{end}.spm.stats.fmri_spec.sess.cond(2).onset = '<UNDEFINED>'; | ||
| matlabbatch{end}.spm.stats.fmri_spec.sess.cond(2).duration = 2; | ||
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There was a problem hiding this comment. Here the duration should be 4, right ? There was a problem hiding this comment. Yes I think you are right! |
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| matlabbatch{end}.spm.stats.fmri_spec.sess.cond(2).tmod = 0; | ||
| matlabbatch{end}.spm.stats.fmri_spec.sess.cond(2).pmod(1).name = 'loss_value'; | ||
| % Below we should include the 'loss' values from sub-001_task-MGT_run-01_events | ||
| % for which we have participant_response=accept (either weakly or strongly) | ||
| matlabbatch{end}.spm.stats.fmri_spec.sess.cond(2).pmod(1).param = '<UNDEFINED>'; | ||
| matlabbatch{end}.spm.stats.fmri_spec.sess.cond(2).pmod(1).poly = 1; | ||
| matlabbatch{end}.spm.stats.fmri_spec.sess.cond(2).pmod(2).name = 'loss_RT'; | ||
| % Below we should include the 'RT' values from sub-001_task-MGT_run-01_events | ||
| % for which we have participant_response=accept (either weakly or strongly) | ||
| matlabbatch{end}.spm.stats.fmri_spec.sess.cond(2).pmod(2).param = '<UNDEFINED>'; | ||
| matlabbatch{end}.spm.stats.fmri_spec.sess.cond(2).pmod(2).poly = 1; | ||
| matlabbatch{end}.spm.stats.fmri_spec.sess.cond(2).orth = 1; | ||
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There was a problem hiding this comment. I could not find the corresponding parameter in nipype There was a problem hiding this comment. Thanks for finding this thread @bclenet! As far as I can tell here we are trying to keep the orthogonolisation which seems to be the default (it looks like it's the switching off that is not implemented yet). If that's true then we should be able to proceed. |
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| matlabbatch{end}.spm.stats.fmri_spec.sess.cond(3).name = 'no_gain_no_loss'; | ||
| % Below we should include the 'RT' values from sub-001_task-MGT_run-01_events | ||
| % for which we have participant_response=reject (either weakly or strongly) | ||
| matlabbatch{end}.spm.stats.fmri_spec.sess.cond(3).onset = '<UNDEFINED>'; | ||
| matlabbatch{end}.spm.stats.fmri_spec.sess.cond(3).duration = 2; | ||
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There was a problem hiding this comment. Here the duration should be 4, right ? |
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| matlabbatch{end}.spm.stats.fmri_spec.sess.cond(3).tmod = 0; | ||
| matlabbatch{end}.spm.stats.fmri_spec.sess.cond(3).pmod.name = 'reaction_time'; | ||
| % Below we should include the 'RT' values from sub-001_task-MGT_run-01_events | ||
| % for which we have participant_response=reject (either weakly or strongly) | ||
| matlabbatch{end}.spm.stats.fmri_spec.sess.cond(3).pmod.param = '<UNDEFINED>'; | ||
| matlabbatch{end}.spm.stats.fmri_spec.sess.cond(3).pmod.poly = 1; | ||
| matlabbatch{end}.spm.stats.fmri_spec.sess.cond(3).orth = 1; | ||
| matlabbatch{end}.spm.stats.fmri_spec.sess.multi_reg = { | ||
| 'ABS_PATH/rp_MOTION_REG_FILE.txt' | ||
| 'ABS_PATH/reg_scrubbing.txt' | ||
| 'ABS_PATH/reg_wm.txt' | ||
| 'ABS_PATH/reg_csf.txt' | ||
| }; | ||
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| % "analysis.inference_contrast_effect": "For the experimental conditions, | ||
| % we are interested in the parametric effect of gain and of loss as related | ||
| % to a mixed gambles task. Trial data, indicating the amount of possible | ||
| % gain and the amount of possible loss, as well as trial timing, trial | ||
| % durations and reaction times were supplied for each run per subject. From | ||
| % this data a block-task regressor was derived respectively for all trials | ||
| % where the gamble could result in a possible gain, in a possible loss, and | ||
| % in a no-loss-nor-gain situation (i.e. 3 block-task regressors). In each | ||
| % case of the possible gain/loss regressors, 2 parametric modulation | ||
| % regressors were added: 1 to model the parametric size of the possible | ||
| % gain/loss, and 1 to model the parametric size of the reaction time. In | ||
| % the case of the no-loss-nor-gain block-task regressor a single extra | ||
| % parametric modulation regressor was added to model the parametric size of | ||
| % the reaction time. We hypothesized that when looking at the parametric | ||
| % effect of GAIN in the brain, we should be interested in the parametric | ||
| % modulators related to the 'possible gain' condition, i.e. the parametric | ||
| % size of the possible gain trials, and the parametric size of the reaction | ||
| % time for said trials. Similarly, when looking at the parametric effect of | ||
| % LOSS, we should be interested in the parametric modulators related to the | ||
| % 'possible loss' condition, i.e. the parametric size of the possible loss | ||
| % trials, and the parametric size of the reaction time related to these | ||
| % trials. We thus had two contrasts for the 1st level analysis: the GAIN | ||
| % contrast which set the two GAIN parametric modulator regressors to 1 and | ||
| % all other GLM regressors to 0; and the LOSS contrast which set the two | ||
| % LOSS parametric modulator regressors to 1 and all other GLM regressors to | ||
| % 0. For the 2nd level group analysis, where the effect of interest was a | ||
| % greater positive response to LOSS in one group vs another, our two-sample | ||
| % t-test was run by setting the contrast [-1 1], assuming the first group | ||
| % was set as the equal indifference group and that the LOSS contrasts | ||
| % resulting from the 1st level analysis of all subjects were fed into this | ||
| % analysis.", | ||
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| matlabbatch{end+1}.spm.stats.con.spmmat = '<UNDEFINED>'; | ||
| matlabbatch{end}.spm.stats.con.consess{1}.tcon.name = 'gain_param'; | ||
| matlabbatch{end}.spm.stats.con.consess{1}.tcon.weights = [0 1 1 0 0 0 0 0]; | ||
| matlabbatch{end}.spm.stats.con.consess{2}.tcon.name = 'loss_param'; | ||
| matlabbatch{end}.spm.stats.con.consess{2}.tcon.weights = [0 0 0 0 1 1 0 0]; | ||
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| % --> Creates con001.nii and con002.nii | ||
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| % "preprocessing.inter_subject_reg": "Intersubject registration was | ||
| % conducted on contrast maps that were output from 1st level statistical | ||
| % analysis. SPM12's normalise (write) function was used to transform these | ||
| % contrast maps from native subject space into 2mm isotropic MNI space | ||
| % (using the MNI template supplied with SPM12). This function required a | ||
| % forward transformation field, which was output from SPM12's unified | ||
| % segmentation step as a field mapping between subject native space and MNI | ||
| % space (see segmentation explanantion above). The normalise (write) | ||
| % process performed interpolation using a 4th degree B-spline. Further | ||
| % SPM12 defaults for normalise (write) and the unified segmentation step | ||
| % (including warping and regularisation parameters) were kept as is.", | ||
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| matlabbatch{end}.spm.spatial.normalise.write.subj.resample = ... | ||
| {'ABS_PATH/ANAT_SEG_PARAM'}; | ||
| matlabbatch{end+1}.spm.spatial.normalise.write.subj.resample = { | ||
| 'ABS_PATH/con001.nii,1' | ||
| 'ABS_PATH/con002.nii.nii,1' | ||
| }; | ||
| % --> Creates wcon001.nii and wcon002.nii | ||
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Here,
first image in that rundoes not refer to the sbref file, right ?There was a problem hiding this comment.
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Agreed. Though the first image in the run and the sbref should be very close.