Each individual image is then mapped to an optimal, dataset-specific shape and appearance template space, derived by a diffeomorphic groupwise normalization method [ 16, 17].
To avoid data "double-dipping," the peri-rolandic regions for this analysis were defined by anatomical masks (delineated in standard template space (Doria et al. 2010)) registered to the individual subject's native space (Kriegeskorte et al. 2009).
Next, GM and white matter segments were registered to a template generated from their own mean by DARTEL, before normalizing them to the Montreal Neurological Institute template space.
First, the normalization parameters for each individual registration are not affected by which image is selected as the "fixed" or reference template space [ 19].
Finally, individual parameter maps for each time point were transformed into the overall study template by concatenating the transformations from native space to the subject-specific template space and from subject-specific template space to the overall study template in a single interpolation step (Supplementary Fig. 1).
For example, patterns of relatively lower regional GM volume in the temporal lobe are quantified by patterns of RAVENS decrease in the temporal lobe in the template space.
We generated the group-specific templates to reduce the error arising from registering individual images onto the template space.
All voxel coordinates in the text and figures refer to the MNI152 standard brain template in MNI152 template space.
To achieve this, usually, a template is registered to the subjects and then the anatomical parcellation map defined on the template space is warped to subjects' native spaces.
These 6 regions were used as ROIs by defining 6-mm radius spheres around the peak coordinates in MNI template space (for ROI placement, see Supplementary Fig. A ).
