The functional data was preprocessed using the fMRIprep pipeline version: 1.5.0. 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 installion, worflow and outputs, please visit the documentation page. Note that the slicetiming and recon-all options were disabled (i.e. fMRIprep was invoked with the flags --fs-no-reconall --ignore slicetiming).


The outputs of fMRIprep can be found under the folder of each dataset (e.g. movie10) derivatives/fmriprep in the Courtois NeuroMod datalad. The description of participant, session, task and event tags can be found in the Datasets section. Each participant folder (sub-*) contains:

  • anat folder with T1 preprocessed and segmented in native and MNI space, registration parameters
  • ses-*/func containing for each fMRI run of that session file prefixed with:
    • _boldref.nii.gz : a BOLD single volume reference.
    • _*-brain_mask.nii.gz : the brain mask in fMRI space.
    • _*-preproc_bold.nii.gz : the preprocessed BOLD timeseries.
    • _*-confounds_regressors.tsv : a tabular tsv file, containing a large set of confounds to use in analysis steps (eg. GLM). Note that regressors are likely correlated, thus it is recommended to use a subset of these regressors. Also note that preprocessed time series have not been corrected for any confounds, but simply realigned in space, and it is therefore critical to regress some of the available confounds prior to analysis. For python users, we recommend using nilearn and the tool load_confounds to load confounds from the fMRIprep outputs, using with the Params24 strategy. In particular, as the NeuroMod data consistently exhibits low levels of motion, we recommend against removing time points with excessive motion (aka scrubbing).

Pipeline description

The following boilerplate text was automatically generated by fMRIPrep with the express intention that users should copy and paste this text into their manuscripts unchanged. It is released under the CC0 license. All references in the text link to a .bib file with detailed reference list, ready to be incorporated in a LaTeX document.

Results included in this manuscript come from preprocessing performed using fMRIPrep 20.1.1+38.g8480eabb (fmriprep1; fmriprep2; RRID:SCR_016216), which is based on Nipype 1.5.0 (nipype1; nipype2; RRID:SCR_002502).

Anatomical data preprocessing

The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) with N4BiasFieldCorrection [n4], distributed with ANTs 2.2.0 [ants, RRID:SCR_004757], and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped with a Nipype implementation of the workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using fast [FSL 5.0.9, RRID:SCR_002823, fsl_fast]. Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through nonlinear registration with antsRegistration (ANTs 2.2.0), using brain-extracted versions of both T1w reference and the T1w template. The following template was selected for spatial normalization: ICBM 152 Nonlinear Asymmetrical template version 2009c [mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym].

Functional data preprocessing

For each of the 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 using a custom methodology of fMRIPrep. A deformation field to correct for susceptibility distortions was estimated based on two echo-planar imaging (EPI) references with opposing phase-encoding directions, using 3dQwarp afni (AFNI 20160207). Based on the estimated susceptibility distortion, an unwarped BOLD 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 flirt [FSL 5.0.9, flirt] with the boundary-based registration [bbr] cost-function. Co-registration was configured with nine degrees of freedom to account for distortions remaining in the BOLD reference. 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]. 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. Several confounding time-series were calculated based on the preprocessed BOLD: framewise displacement (FD), DVARS and three region-wise global signals. 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 5% variable voxels within a mask covering the subcortical regions. This subcortical mask is obtained by heavily eroding the brain mask, which ensures it does not include cortical GM regions. For aCompCor, components are calculated within the intersection of the aforementioned mask and the union of CSF and WM masks calculated in T1w space, after their projection to the native space of each functional run (using the inverse BOLD-to-T1w transformation). 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 0.5.2 [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.