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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}}
- \bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}
- \end{document}
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