Project Summary/Abstract Brain connectivity plays a fundamental role in neurologic function and dysfunction, and can directly impact substance use or be alerted by inappropriate uses of substances. However, there is a limited understanding of the bidirectional relationship between brain connectomics and substance use, e.g., are there connections of the brain that predispose an individual to substance use disorders (SUD), and how SUD impacts the brain and its development. Improving this understanding is of critical importance in obtaining mechanistic insights into factors underlying substance misuse and neuropsychiatric disorders. With the availability of large-scale and longitudinal data sets such as ABCD, we are now at the golden time to significantly advance our understanding of the causal or association relationship between SUD and brain connectivity. However, we are facing both computational and theoretical challenges in brain network data analysis, considering the complexity and scale of the brain imaging data. It is critical to train the next-generation neuroscience data scientists with sufficient knowledge to correctly do a full life cycle of data science (LCDS) for brain connectomes analysis. Here, a full LCDS includes steps to collect data for the best brain connectome analysis, reliably and robustly extract brain connectomes, and rigorously analyze variations in the data. The proposed educational plan aims at (i) developing easy-to-use computational tools for connectome reconstruction, visualization, and statistical analysis and training students and young investigators to use these tools; and (ii) enhancing rigorous and reproducible statistical analysis of brain network data through short courses, summer camps, and workshops. The success of the project relies on the unique brain imaging and machine learning expertise of the PIs (Drs. Wu and Zhang) and their collaborative relationships with experts in biostatistics, mental health, computer science, and psychology research faculty in the Department of Psychiatry (PSYCH), the Department of Statistics & Operation Research (STOR), the Department of Biostatistics (BIOS), the Department of Computer Science (CS), the Department of Psychology (PSY), UNC Neuroscience Center (UNCNC), and the Carolina Institute of Developmental Disabilities (CIDD) at the University of North Carolina (UNC) at Chapel Hill, and other departments in Duke University, Wake Forest University, Wake Forest School of Medicine, and UNC at Greensboro.