Results included in this manuscript come from preprocessing performed using fMRIPrep 23.2.1 (@fmriprep1; @fmriprep2; RRID:SCR_016216), which is based on Nipype 1.8.6 (@nipype1; @nipype2; RRID:SCR_002502).
Anatomical data preprocessing
: A total of 9 T1-weighted (T1w) images were found within the input
BIDS dataset. Each T1w image was corrected for intensity
non-uniformity (INU) with N4BiasFieldCorrection [@n4], distributed with ANTs 2.5.0
[@ants, RRID:SCR_004757].
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 (version unknown), RRID:SCR_002823, @fsl_fast].
An anatomical T1w-reference map was computed after registration of
9 T1w images (after INU-correction) using
mri_robust_template [FreeSurfer 7.3.2, @fs_template].
An anatomical T2w-reference map was computed after registration of
2 T2w images (after INU-correction) using
mri_robust_template [FreeSurfer 7.3.2, @fs_template].
Brain surfaces were reconstructed using recon-all [FreeSurfer 7.3.2,
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 [RRID:SCR_002438, @mindboggle].
A T2-weighted image was used to improve pial surface refinement.
Brain surfaces were reconstructed using recon-all [FreeSurfer 7.3.2,
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 [RRID:SCR_002438, @mindboggle].
Volume-based spatial normalization to two standard spaces (MNI152NLin2009cAsym, MNI152NLin6Asym) was performed through
nonlinear registration with antsRegistration (ANTs 2.5.0),
using brain-extracted versions of both T1w reference and the T1w template.
The following templates were were selected for spatial normalization
and accessed with TemplateFlow [23.1.0, @templateflow]:
ICBM 152 Nonlinear Asymmetrical template version 2009c [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym], FSL's MNI ICBM 152 non-linear 6th Generation Asymmetric Average Brain Stereotaxic Registration Model [@mni152nlin6asym, RRID:SCR_002823; TemplateFlow ID: MNI152NLin6Asym].
Functional data preprocessing
: For each of the 12 BOLD runs found per subject (across all
tasks and sessions), the following preprocessing was performed.
First, a reference volume was generated,
using a custom methodology of fMRIPrep, for use in head motion correction.
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 , @mcflirt].
The BOLD reference was then co-registered to the T1w reference using
bbregister (FreeSurfer) which implements boundary-based registration [@bbr].
Co-registration was configured with six degrees of freedom.
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 nitransforms,
configured with cubic B-spline interpolation.
Many internal operations of fMRIPrep use Nilearn 0.10.2 [@nilearn, 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.