Results included in this manuscript come from preprocessing performed using fMRIPrep 22.0.0 (Esteban, Markiewicz, et al. (2018); Esteban, Blair, et al. (2018); RRID:SCR_016216), which is based on Nipype 1.8.4.dev0 (K. Gorgolewski et al. (2011); K. J. Gorgolewski et al. (2018); RRID:SCR_002502).
A total of 1 fieldmaps were found available within the input BIDS
structure for this particular subject. A B0
nonuniformity map (or fieldmap) was estimated from the
phase-drift map(s) measure with two consecutive GRE (gradient-recalled
echo) acquisitions. The corresponding phase-map(s) were phase-unwrapped
with prelude
(FSL 6.0.4:ddd0a010).
A total of 1 T1-weighted (T1w) images were found within the input
BIDS dataset.The T1-weighted (T1w) image was corrected for intensity
non-uniformity (INU) with N4BiasFieldCorrection
(Tustison et al. 2010),
distributed with ANTs 2.3.3 (Avants et al. 2008, RRID:SCR_004757), and used
as T1w-reference throughout the workflow. The T1w-reference was then
skull-stripped with a Nipype implementation of the
antsBrainExtraction.sh
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 6.0.4:ddd0a010,
RRID:SCR_002823, Zhang, Brady, and Smith 2001). Brain surfaces
were reconstructed using recon-all
(FreeSurfer 7.2.0, RRID:SCR_001847, Dale,
Fischl, and Sereno 1999), and the brain mask estimated previously
was refined with a custom variation of the method to reconcile
ANTs-derived and FreeSurfer-derived segmentations of the cortical
gray-matter of Mindboggle (RRID:SCR_002438, Klein et al. 2017).
Volume-based spatial normalization to one standard space
(MNI152NLin2009cAsym) was performed through nonlinear registration with
antsRegistration
(ANTs 2.3.3), 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 [Fonov et al. (2009),
RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym].
For each of the 4 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. 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 6.0.4:ddd0a010, Jenkinson et
al. 2002). The estimated fieldmap was then aligned with
rigid-registration to the target EPI (echo-planar imaging) reference
run. The field coefficients were mapped on to the reference EPI using
the transform. BOLD runs were slice-time corrected to 0.972s (0.5 of
slice acquisition range 0s-1.94s) using 3dTshift
from AFNI
20210200 (Cox and Hyde 1997,
RRID:SCR_005927). 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. 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 0.9.1 (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.