Results included in this manuscript come from preprocessing performed using fMRIPrep 23.1.4 (Esteban et al. (2019); Esteban et al. (2018); RRID:SCR_016216), which is based on Nipype 1.8.6 (K. Gorgolewski et al. (2011); K. J. Gorgolewski et al. (2018); RRID:SCR_002502).
A total of 79 T1-weighted (T1w) images were found within the input BIDS dataset. Anatomical preprocessing was reused from previously existing derivative objects.
For each of the 1 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 from the shortest
echo of the BOLD run using a custom methodology of fMRIPrep.
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
3dTshift from AFNI (Cox and Hyde 1997, RRID:SCR_005927). The BOLD
time-series (including slice-timing correction when applied) were
resampled onto their original, native space by applying the transforms
to correct for head-motion. These resampled BOLD time-series will be
referred to as preprocessed BOLD in original space, or just
preprocessed BOLD. A T2★ map was estimated from the
preprocessed EPI echoes, by voxel-wise fitting the maximal number of
echoes with reliable signal in that voxel to a monoexponential signal
decay model with nonlinear regression. The T2★/S0
estimates from a log-linear regression fit were used for initial values.
The calculated T2★ map was then used to optimally combine
preprocessed BOLD across echoes following the method described in (Posse et al. 1999). The
optimally combined time series was carried forward as the
preprocessed BOLD. The BOLD reference was then co-registered to
the T1w reference using bbregister (FreeSurfer) which
implements boundary-based registration (Greve and Fischl 2009). Co-registration was
configured with six degrees of freedom. 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 was computed using two
formulations following Power (absolute sum of relative motions, Power et al. (2014))
and Jenkinson (relative root mean square displacement between affines,
Jenkinson et al.
(2002)). FD and DVARS are calculated for each functional run,
both using their implementations in Nipype (following the definitions
by Power et al. 2014). 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, Behzadi et al. 2007).
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, a mask of pixels that likely contain a volume fraction of GM is
subtracted from the aCompCor masks. 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 (Satterthwaite et al.
2013). Frames that exceeded a threshold of 0.5 mm FD or 1.5
standardized DVARS were annotated as motion outliers. Additional
nuisance timeseries are calculated by means of principal components
analysis of the signal found within a thin band (crown) of
voxels around the edge of the brain, as proposed by (Patriat, Reynolds,
and Birn 2017). 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. 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 1964). Non-gridded
(surface) resamplings were performed using mri_vol2surf
(FreeSurfer).
Many internal operations of fMRIPrep use Nilearn 0.10.1 (Abraham et al. 2014, 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.
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.