2.7. fmri Preprocessing

For each of the BOLD-fMRI runs obtained 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 customized methodology of fMRIPrep. A B₀ field map was estimated based on two echo-planar imaging (EPI) references with opposing phase-encoding directions, with 3dQwarp (AFNI version 20160207) [69]. Based on the estimated susceptibility distortion, a corrected echo-planar imaging (EPI) 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 flirt (FSL 5.0.9) [70,71,72] with the boundary-based registration cost-function [73].

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) were estimated before any spatiotemporal filtering using mcflirt (FSL 5.0.9) [70]. The BOLD time series 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. For the comparison subjects’ data only, the BOLD time series were resampled into standard space, generating a preprocessed BOLD series in MNI152NLin2009cAsym space. Several confounding time series were calculated based on the preprocessed BOLD series: framewise displacement (FD), DVARS and three region-wise global signals (WM, GM CSF). FD was computed using two formulations following Power (absolute sum of relative motions) [74] and Jenkinson (relative root mean square displacement between affines) [71]. FD and DVARS were calculated for each functional run, both using their implementations in Nipype following the definitions [74]. The head motion estimates calculated in the correction step were also placed within the corresponding confounds file. The confound time series derived from head motion estimates and global signals were expanded with the inclusion of temporal derivatives and quadratic terms for each [75]. All resampling was 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) resampling was performed using antsApplyTransforms (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels [76].

No slice timing correction was applied given the sub-second TR. Functional data were projected only onto each subject’s native anatomical space and not to template spaces to reduce distortion of atypical hemispherectomy anatomy.