Derivatives
sMRIPrep
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.
fMRIPrep
Overview
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
).
Outputs
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).
Recommended preprocessing strategy
The confounding regressors are correlated, thus it is critical to only 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 also critical to regress some of the available confounds prior to analysis. See the fMRIprep documentation for details on available confound regressors. For python users, we recommend using nilearn and the tool load_confounds_strategy to load confounds from the fMRIprep outputs, using with a standardized strategy. As the NeuroMod data consistently exhibits low levels of motion, we recommend against removing time points with excessive motion (aka scrubbing), and the minimal
strategy available in nilearn is a reasonable choice. Because of the 2 mm spatial resolution of the fMRI scan, there is substantial impact of thermal noise, and some amount of spatial smoothing is advisable, the extent of it being determined by your hypotheses and analysis.
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.
Copyright Waiver
The above 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.