Sherlock/fmriprep/logs/CITATION.tex

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\begin{document}

Results included in this manuscript come from preprocessing performed
using \emph{fMRIPprep} 1.2.6-1 (\citet{fmriprep1}; \citet{fmriprep2};
RRID:SCR\_016216), which is based on \emph{Nipype} 1.1.7
(\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).

\begin{description}
\item[Anatomical data preprocessing]
The T1-weighted (T1w) image was corrected for intensity non-uniformity
(INU) using \texttt{N4BiasFieldCorrection} \citep[ANTs 2.2.0]{n4}, and
used as T1w-reference throughout the workflow. The T1w-reference was
then skull-stripped using \texttt{antsBrainExtraction.sh} (ANTs 2.2.0),
using OASIS as target template. 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 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 using a custom
methodology of \emph{fMRIPrep}. A deformation field to correct for
susceptibility distortions was estimated based on \emph{fMRIPrep}'s
\emph{fieldmap-less} approach. The deformation field is that resulting
from co-registering the BOLD reference to the same-subject T1w-reference
with its intensity inverted \citep{fieldmapless1, fieldmapless2}.
Registration is performed with \texttt{antsRegistration} (ANTs 2.2.0),
and the process regularized by constraining deformation to be nonzero
only along the phase-encoding direction, and modulated with an average
fieldmap template \citep{fieldmapless3}. 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{flirt} \citep[FSL 5.0.9,][]{flirt} with the boundary-based
registration \citep{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 \texttt{mcflirt} \citep[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
\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. 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} 0.5.0
\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}