The support vector machine classification found that the pattern of regional volume differences could be used successfully to distinguish between nondependent control subjects and individuals dependent on alcohol and nicotine. The transformation of the data with a radial basis function kernel prior to classification facilitated the detection of nonlinear patterns that cannot be detected by models 1 and 2. Additionally, the support vector machine can identify a multivariate pattern of effects across numerous regions of interest, each of which, in isolation, may not pass statistical threshold. Thus, the support vector machine detected useful information in the pattern of results that was not apparent from the linear analysis. The significant classifications suggest that the overall pattern of volumetric effects may contain useful clinical information that would not be apparent if only traditional univariate linear analyses were performed. While influential features in the classification partly overlapped with the regions of interest identified by the univariate analyses—for example, brain regions associated with alcohol dependence, such as the hippocampus and amygdala—additional regions not identified by the linear mixed effects analyses (i.e., model 1 and
