Promoting Collaborative Research on Human Connectome Analysis for Substance Use Disorders

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.