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 1 T1-weighted (T1w) images were found within the input
BIDS dataset. The T1w image was corrected for intensity
non-uniformity (INU) with N4BiasFieldCorrection [@n4], distributed with ANTs 2.5.0
[@ants, 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 (version unknown), RRID:SCR_002823, @fsl_fast].
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].
Preprocessing of B0 inhomogeneity mappings
: A total of 12 fieldmaps were found available within the input
BIDS structure for this particular subject.
A deformation field to correct for susceptibility distortions was estimated
based on fMRIPrep's fieldmap-less approach.
The deformation field is that resulting from co-registering the EPI reference
to the same-subject T1w-reference with its intensity inverted [@fieldmapless1;
@fieldmapless2].
Registration is performed with antsRegistration
(ANTs 2.5.0), and
the process regularized by constraining deformation to be nonzero only
along the phase-encoding direction, and modulated with an average fieldmap
template [@fieldmapless3].
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 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.
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.