Of all 353 participants, 67% had 2 or more scans and 42% had 3 or more brain MRI scans acquired at approximately 2-year intervals All sMRI scans were T-1 weighted images with contiguous 1.5 mm axial slices and 2.0 mm coronal slices, obtained on the same 1.5-T General Electric (Milwaukee, WI) Signa scanner using a 3D spoiled gradient recalled echo sequences. Native MRI scans were submitted to the CIVET pipeline to generate separate cortical models for each hemisphere as described previously (Giedd et al., 2007). Briefly, this fully automated and previously validated (Lerch and Evans, 2005; Shaw et al., 2008) set of algorithms consists of the following stages; Masking-out non-brain tissues from native-space sMRI scans using a Brain Extraction Tool method (Smith, 2002); registration of native scans into MNI-ICBM152 stereotaxic space by 9-parameter linear transformation (Collins et al., 1994); correction for non-uniformity artifacts (Sled et al., 1998); and image classification into white matter, grey matter and CSF using a neural-net classifier (Zijdenbos et al., 2002). Next, surface meshes representing the gray/white and gray/pial surfaces were generated with a Constrained Laplacian
