MRI data preprocessing

MRI data were preprocessed using Statistical Parametric Mapping software (SPM8, Wellcome Department of Cognitive Neurology, UCL) under MATLAB 8.4.0. The structural T1 images of each subject were reoriented to the anterior commissure and then processed using Unified Segmentation 26 with medium regularization. Lesioned areas were not excluded from subsequent analyses (see below), but cost function masking (CFM) 27 was applied to achieve accurate segmentation and optimal normalization of the lesioned GM and white matter (WM) tissue, with no postregistration lesion shrinkage or out‐of‐brain distortion. Using MRIcron (https://www.nitrc.org/projects/mricron), CFM was performed by manually depicting the lesioned areas slice‐by‐slice to the T1 images of each subject. The segmented GM and WM images were modulated to preserve the original signal strength and then normalized to the MNI space. After this, to reduce residual interindividual variability, GM and WM probability maps were smoothed using an isotropic spatial filter (FWHM 6 mm). For fMRI data, the functional runs were first realigned and their mean image was calculated. The T1 image and its lesion mask were then co‐registered to this mean functional image. The normalization parameters were again estimated using Unified Segmentation with CFM and were applied to the whole functional run to register it to MNI space. In this registration step, data were resampled into 2.0 × 2.0 × 2.0 mm voxel size. Finally, the normalized fMRI data were smoothed using an 8‐mm FWHM kernel.