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\begin{document}

Results included in this manuscript come from preprocessing performed
using \emph{fMRIPprep} 1.2.2 (\citet{fmriprep1}; \citet{fmriprep2};
RRID:SCR\_016216), which is based on \emph{Nipype} 1.1.5
(\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).

\begin{description}
\item[Anatomical data preprocessing]
A total of 2 T1-weighted (T1w) images were found within the input BIDS
dataset. All of them were corrected for intensity non-uniformity (INU)
using \texttt{N4BiasFieldCorrection} \citep[ANTs 2.2.0]{n4}. A
T1w-reference map was computed after registration of 2 T1w images (after
INU-correction) using \texttt{mri\_robust\_template} \citep[FreeSurfer
6.0.1,][]{fs_template}. The T1w-reference was then skull-stripped using
\texttt{antsBrainExtraction.sh} (ANTs 2.2.0), using OASIS as target
template. Brain surfaces were reconstructed using \texttt{recon-all}
\citep[FreeSurfer 6.0.1, RRID:SCR\_001847,][]{fs_reconall}, 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
\citep[RRID:SCR\_002438,][]{mindboggle}. Spatial normalization to the
ICBM 152 Nonlinear Asymmetrical template version 2009c
\citep[RRID:SCR\_008796]{mni} was performed through nonlinear
registration with \texttt{antsRegistration} \citep[ANTs 2.2.0,
RRID:SCR\_004757,][]{ants}, using brain-extracted versions of both T1w
volume and template. Brain tissue segmentation of cerebrospinal fluid
(CSF), white-matter (WM) and gray-matter (GM) was performed on the
brain-extracted T1w using \texttt{fast} \citep[FSL 5.0.9,
RRID:SCR\_002823,][]{fsl_fast}.
\item[Functional data preprocessing]
For each of the 11 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 \emph{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
\texttt{3dQwarp} \citet{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
\texttt{bbregister} (FreeSurfer) which implements boundary-based
registration \citep{bbr}. 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
\texttt{mcflirt} \citep[FSL 5.0.9,][]{mcflirt}. BOLD runs were
slice-time corrected using \texttt{3dTshift} from AFNI 20160207
\citep[RRID:SCR\_005927]{afni}. 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 \emph{preprocessed BOLD in
original space}, or just \emph{preprocessed BOLD}. The BOLD time-series
were resampled to MNI152NLin2009cAsym standard space, generating a
\emph{preprocessed BOLD run in MNI152NLin2009cAsym space}. First, a
reference volume and its skull-stripped version were generated using a
custom methodology of \emph{fMRIPrep}. Several confounding time-series
were calculated based on the \emph{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 \emph{Nipype} \citep[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 \citep[\emph{CompCor},][]{compcor}. Principal
components are estimated after high-pass filtering the
\emph{preprocessed BOLD} time-series (using a discrete cosine filter
with 128s cut-off) for the two \emph{CompCor} variants: temporal
(tCompCor) and anatomical (aCompCor). Six 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, six 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). The head-motion estimates calculated in the correction
step were also placed within the corresponding confounds file. The BOLD
time-series, were resampled to surfaces on the following spaces:
\emph{fsnative}, \emph{fsaverage}. All resamplings can be performed with
\emph{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 template spaces). Gridded (volumetric) resamplings were performed
using \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
interpolation to minimize the smoothing effects of other kernels
\citep{lanczos}. Non-gridded (surface) resamplings were performed using
\texttt{mri\_vol2surf} (FreeSurfer).
\end{description}

Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.4.2
\citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
processing workflow. For more details of the pipeline, see
\href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
section corresponding to workflows in \emph{fMRIPrep}'s documentation}.

\hypertarget{references}{%
\subsubsection{References}\label{references}}

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\end{document}
