Support vector machine classification was implemented in MATLAB (MathWorks, Natick, Mass.) with a radial basis function kernel, tuned by parameter sweep in a 10-fold inner loop nested within an optimized split-half cross-validation (32) (for details, see the Supplemental Methods section in the online supplement). The radial basis function kernel facilitates the inclusion of nonlinear relationships in the classifier. In other words, the support vector machine can detect informative patterns in the data that may not be identified by traditional linear analyses such as models 1 and 2. To mitigate site, sex, age, and intracranial volume effects, region- of-interest data were residualized prior to classification. Five studies without control participants were excluded. Area under the receiver operating characteristic curve and corresponding p values based on equivalence with the Mann-Whitney U test were calculated to estimate generalizable classifier performance on the independent half of the data for each of two train-test scenarios (i.e., train on the first half, test on the second, and vice versa). A greater area under the receiver operating characteristic curve, which plots true positive rate against false positive
