ABSTRACT This application represents our ongoing commitment to developing an innovative and interdisciplinary research program on the classification of substance use disorders (SUDs). This research is achieved through quantitative analysis of multidimensional data that combine clinical symptoms and diagnoses, imaging markers, and genotypes. The team has a PI with expertise in computational science and the development and implementation of innovative statistical algorithms to understand multidimensional data; a PI with extensive experience in systems, imaging and addiction neuroscience; and a co-I who has expertise in the genetics of SUDs. Our previous R01 project employed a sample of ~12,000 individuals aggregated from multiple genetic studies of alcohol and drug dependence to generate SUD subtypes based on clinical symptoms. Because clinical manifestations are distal endpoints in the biological pathway, the genetic effects identified are often weak and inconsistent, and consequently difficult to detect even in large samples. As championed by the NIMH Research Domain Criteria (RDoC) research, the etiologies of psychiatric disorders, including SUDs, can be fruitfully characterized by dimensional neural features. This project thus extends our ongoing work to include imaging neural features in the classification of SUDs. Specifically, we will utilize a large database from the UK Biobank Project that provides both genetic and multi-modality magnetic resonance imaging (MRI) data. Building on our work with the US Human Connectome Project, we aim in the current project to integrate clinical, imaging, and genotype data to investigate the neurobiological substrates of SUD diagnostic labels, and to derive SUD subtypes that are optimized for gene finding. Methodologically, we replace the classic statistical analysis that is confirmatory and biased to an a priori hypothesis by an approach that emphasizes pattern discoveries from big data. Our specific aims are to: (I): identify neuroimaging features that represent robust markers of addiction and differentiate SUD subtypes that can be confirmed by multi-modality evidence; (II) employ a novel brain connectivity model, on the basis of graph convolutional neural networks, to identify neural markers that precisely characterize the differences in structural changes and functional circuits related to SUDs; and (III) derive an innovative machine learning model to identify highly heritable neurobiological subtypes of SUDs that facilitate investigation of the genetic basis of addiction. We will focus on alcohol and nicotine use disorders to demonstrate the conceptual and methodological approaches. We believe that, by providing a productive conceptual and methodological platform to integrate imaging and genetic data to understand the etiologies of SUDs, this research is highly responsive to the RFA “Leveraging Big Data Science to Elucidate the Neural Mechanisms of Addiction and SUD.” The machine learning tools develope...