The field of neuroimaging faces a crisis of relevance if published studies cannot be replicated, as noted in a series of reviews (22, 23). The authors of the present study joined to form a working group within the preexisting framework of the ENIGMA project to assemble a sufficiently large sample to overcome issues related to low statistical power that affect most individual neuroimaging studies. Using a rigorous cross-validation method, several brain regions were found to have a reliable association with substance dependence, including a shared set of regions across substances, such as the insula and the medial orbitofrontal cortex. Although the univariate analyses failed to identify linear effects in relation to dependence on nicotine, methamphetamine, and cannabis specifically, a machine learning algorithm, which was also able to detect nonlinear patterns in the data, successfully classified individuals dependent on alcohol or nicotine relative to nondependent control subjects. This suggests that the overall pattern of volumetric effects may contain more useful information with regard to the development of a neuroimaging biomarker of substance dependence than is revealed by the magnitude of single brain regions examined in isolation.
