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 model 2) were also involved (see Table S3 in the online supplement). Future efforts of the Addiction Working Group will include the incorporation of other imaging modalities with which it may be possible to distinguish individuals with dependence on additional substances, such as methamphetamine and cannabis, from nondependent control subjects. It would also be clinically useful to examine whether the support vector machine classifications developed in this study offer an index of the strength of substance dependence in individuals who go on to recover or relapse. It is worth noting that current blood and urine tests do not identify dependence, as the machine learning classifier in the present study does, but rather detect, and to an extent quantify, recent substance use. While the present findings are preliminary and the support vector machine classifications should be tested on other independent samples, if brain volume is confirmed as a viable biomarker of dependence, or of biological risk of dependence, it could be
