The anatomical data was preprocessed using sMRIPrep pipeline. It took as input the T1w and T2w of the first 2 sessions of all participants, which were averaged after coregistration.



The functional data was preprocessed using the fMRIprep pipeline. FmriPrep is an fMRI data preprocessing pipeline that requires minimal user input, while providing error and output reporting. It performs basic processing steps (coregistration, normalization, unwarping, noise component extraction, segmentation, skullstripping etc.) and provides outputs that can be easily submitted to a variety of group level analyses, including task-based or resting-state fMRI, graph theory measures, surface or volume-based statistics, etc. The fMRIprep pipeline uses a combination of tools from well-known software packages, including FSL, ANTs, FreeSurfer and AFNI. For additional information regarding fMRIPrep installation, workflow and outputs, please visit the documentation page. Note that the slicetiming option was disabled (i.e. fMRIprep was invoked with the flag --ignore slicetiming).


The outputs of fMRIprep can be found as sub-datasets of the cneuromod.processed super-dataset. fMRIPrep functional preprocessing was run using the anatomical “fast-track” (flag --anat-derivatives) with sMRIPrep output described above, so as to use the same anatomical basis for all functional dataset. The output was generated in T1w, MNI152NLin2009cAsym and fsLR-den-91k spaces as defined by templateflow to respectively enable native space and volumetric or surface-based analyses.

The description of participant, session, task and event tags can be found in the Datasets section. Each participant folder (sub-*) contains:

  • ses-*/func containing for each fMRI run of that session file prefixed with:

    • *_boldref.nii.gz : a BOLD single volume reference.

    • *_desc-brain_mask.nii.gz : the brain mask in fMRI space.

    • *_desc-preproc_bold.nii.gz : the preprocessed BOLD timeseries.

    • *_desc-confounds_timeseries.tsv : a tabular tsv file, containing a large set of confounds to use in analysis steps (eg. GLM).

Pipeline description

Results included in this manuscript come from preprocessing performed using fMRIPrep 20.2.5 (@fmriprep1; @fmriprep2; RRID:SCR_016216), which is based on Nipype 1.6.1 (@nipype1; @nipype2; RRID:SCR_002502).

Anatomical data preprocessing

: A total of 0 T1-weighted (T1w) images were found within the input BIDS dataset. Anatomical preprocessing was reused from previously existing derivative objects.

Functional data preprocessing

: For each of the 2 BOLD runs found per subject (across all tasks and sessions), the following preprocessing was performed. First, a reference volume and its skull-stripped version were generated by aligning and averaging 1 single-band references (SBRefs). A B0-nonuniformity map (or fieldmap) was estimated based on two (or more) echo-planar imaging (EPI) references with opposing phase-encoding directions, with 3dQwarp @afni (AFNI 20160207). Based on the estimated susceptibility distortion, a corrected EPI (echo-planar imaging) reference was calculated for a more accurate co-registration with the anatomical reference. The BOLD reference was then co-registered to the T1w reference using bbregister (FreeSurfer) which implements boundary-based registration [@bbr]. Co-registration was configured with six degrees of freedom. Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) are estimated before any spatiotemporal filtering using mcflirt [FSL 5.0.9, @mcflirt]. First, a reference volume and its skull-stripped version were generated using a custom methodology of fMRIPrep. The BOLD time-series were resampled onto the following surfaces (FreeSurfer reconstruction nomenclature): fsaverage. The BOLD time-series (including slice-timing correction when applied) were resampled onto their original, native space by applying a single, composite transform to correct for head-motion and susceptibility distortions. These resampled BOLD time-series will be referred to as preprocessed BOLD in original space, or just preprocessed BOLD. The BOLD time-series were resampled into standard space, generating a preprocessed BOLD run in MNI152NLin2009cAsym space. First, a reference volume and its skull-stripped version were generated using a custom methodology of fMRIPrep. Grayordinates files [@hcppipelines] containing 91k samples were also generated using the highest-resolution fsaverage as intermediate standardized surface space. Several confounding time-series were calculated based on the preprocessed BOLD: framewise displacement (FD), DVARS and three region-wise global signals. FD was computed using two formulations following Power (absolute sum of relative motions, @power_fd_dvars) and Jenkinson (relative root mean square displacement between affines, @mcflirt). FD and DVARS are calculated for each functional run, both using their implementations in Nipype [following the definitions by @power_fd_dvars]. The three global signals are extracted within the CSF, the WM, and the whole-brain masks. Additionally, a set of physiological regressors were extracted to allow for component-based noise correction [CompCor, @compcor]. Principal components are estimated after high-pass filtering the preprocessed BOLD time-series (using a discrete cosine filter with 128s cut-off) for the two CompCor variants: temporal (tCompCor) and anatomical (aCompCor). tCompCor components are then calculated from the top 2% variable voxels within the brain mask. For aCompCor, three probabilistic masks (CSF, WM and combined CSF+WM) are generated in anatomical space. The implementation differs from that of Behzadi et al. in that instead of eroding the masks by 2 pixels on BOLD space, the aCompCor masks are subtracted a mask of pixels that likely contain a volume fraction of GM. This mask is obtained by dilating a GM mask extracted from the FreeSurfer’s aseg segmentation, and it ensures components are not extracted from voxels containing a minimal fraction of GM. Finally, these masks are resampled into BOLD space and binarized by thresholding at 0.99 (as in the original implementation). Components are also calculated separately within the WM and CSF masks. For each CompCor decomposition, the k components with the largest singular values are retained, such that the retained components’ time series are sufficient to explain 50 percent of variance across the nuisance mask (CSF, WM, combined, or temporal). The remaining components are dropped from consideration. The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. The confound time series derived from head motion estimates and global signals were expanded with the inclusion of temporal derivatives and quadratic terms for each [@confounds_satterthwaite_2013]. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS were annotated as motion outliers. All resamplings can be performed with a single interpolation step by composing all the pertinent transformations (i.e. head-motion transform matrices, susceptibility distortion correction when available, and co-registrations to anatomical and output spaces). Gridded (volumetric) resamplings were performed using antsApplyTransforms (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels [@lanczos]. Non-gridded (surface) resamplings were performed using mri_vol2surf (FreeSurfer).

Many internal operations of fMRIPrep use Nilearn 0.6.2 [@nilearn, RRID:SCR_001362], mostly within the functional processing workflow. For more details of the pipeline, see the section corresponding to workflows in fMRIPrep’s documentation.