Brain images were globally segmented to extract three main structures: GM, WM, and cerebrospinal fluid (CSF), using a nonparametric technique of probability density function estimation, based on the mean-shift algorithm.18 The procedure begins by filtering the data with a modified mean-shift method that incorporates edge-confidence maps, in order to obtain the distribution modes without any a priori information. Afterward, an adjacency graph is constructed and analyzed to fuse those regions belonging to the same class, given spatial and intensity similarity measures; a final step is applied to prune regions whose size is below a certain threshold, assigning them to the nearest class. Brain tissues’ classification is carried out by considering anatomical atlases with the Statistical Probability Mapping (SPM) software.19 The outcome of this procedure is a set of images, where GM, WM, and CSF are segmented and from these we construct the global anatomical volumes that will be measured using compactness indices. Also, a more detailed separation of these structures is accomplished by using the Individual Brain Atlases using Statistical Parametric Mapping (IBASPM),20,21 that uses templates containing those substructures belonging to the frontal, temporal, parietal, and occipital lobes. These templates are registered to the same data space and applied to delimit the substructures corresponding, for both hemispheres, to frontal (FR, FL), temporal (TR, TL), parietal (PR, PL), and occipital (OR, OL) lobes. These regions, together with WM and GM, give a total of 10 brain structures to be characterized.