Image data preprocessing was described in detail in  and is therefore only briefly summarized here. All data processing and analysis steps were performed in Matlab (Mathworks, Natick, MA), in part using functionality available within the spm12 software package (rev. 6906; University College London, UK). A 7th degree B-spline interpolation algorithm was used when writing images , but all other parameters were left at their default values unless specified otherwise. Initially, all images were reoriented and bias-corrected, using functionality provided within the unified segmentation framework . Tissue segmentation was then achieved using the cat12 toolbox (r1092 ) which is a priorless modification and extension of the SPM12 “new segment” approach . Tissue probability maps (for gray matter [GM] and white matter [WM] only) were spatially normalized using an affine registration scheme  to allow for an initial overlap of large structures. We opted for an affine approach here (instead of the usually recommended rigid-body procedure ) as the overall size difference between the subjects included here (between infancy and old age , ) must be expected to otherwise pose insurmountable challenges for the ensuing non-linear deformation steps (see below). Visual quality control was also performed as previously described , using individual inspection of each map at the level of the basal ganglia and the cerebellum to identify overt failure of spatial normalization or tissue segmentation.