Results included in this manuscript come from preprocessing performed using fMRIPprep 1.2.6-1 (@fmriprep1; @fmriprep2; RRID:SCR_016216), which is based on Nipype 1.1.7 (@nipype1; @nipype2; RRID:SCR_002502).
Anatomical data preprocessing
: The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
using N4BiasFieldCorrection
[@n4, ANTs 2.2.0],
and used as T1w-reference throughout the workflow.
The T1w-reference was then skull-stripped using antsBrainExtraction.sh
(ANTs 2.2.0), using OASIS as target template.
Spatial normalization to the ICBM 152 Nonlinear Asymmetrical
template version 2009c [@mni, RRID:SCR_008796] was performed
through nonlinear registration with antsRegistration
[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 fast
[FSL 5.0.9, RRID:SCR_002823,
@fsl_fast].
Functional data preprocessing
: For each of the 3 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 fMRIPrep.
The BOLD reference was then co-registered to the T1w reference using
flirt
[FSL 5.0.9, @flirt] with the boundary-based registration [@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
mcflirt
[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 preprocessed
BOLD in original space, or just preprocessed BOLD.
The BOLD time-series were resampled to MNI152NLin2009cAsym standard space,
generating a preprocessed BOLD run in MNI152NLin2009cAsym space.
First, a reference volume and its skull-stripped version were generated
using a custom methodology of fMRIPrep.
Several confounding time-series were calculated based on the
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 Nipype [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 [CompCor, @compcor].
Principal components are estimated after high-pass filtering the
preprocessed BOLD time-series (using a discrete cosine filter with
128s cut-off) for the two 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 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 antsApplyTransforms
(ANTs),
configured with Lanczos interpolation to minimize the smoothing
effects of other kernels [@lanczos].
Non-gridded (surface) resamplings were performed using mri_vol2surf
(FreeSurfer).
Many internal operations of fMRIPrep use Nilearn 0.5.0 [@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.