Training in Complex Systems and Data Science Approaches Applied to the Neurobiology of Drug Use

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The purpose of this program is to train pre- and post-doctoral scholars in the application of complex systems and data science approaches to the neurobiology of substance abuse, with the dual strategies of: (1) training candidates with expertise in data science, applied mathematics, computer science, and complex systems to apply their skills to the neuroscience of addiction; and (2) training candidates pursuing addiction research from neuroscience, psychiatry, psychology and genetics in the theory and application of Big Data methods, including network analysis, machine learning algorithms, and Bayesian statistical models. Trainees will be paired across disciplines, and academic stages, for research collaboration and reciprocal tutoring to facilitate the development of proficiency in each trainee's new field. Each trainee will also be dual mentored, their secondary mentor being their partner's primary. Over its five-year duration, the program will provide three years of funding for each of five pairs of pre- and post-doctoral researchers, with an initial cohort of four (two pairs), and additional pairs entering in each of the middle three years. The core curriculum will incorporate: (1) the established complex systems and data science graduate certificate at the University of Vermont; (2) course work in neuroscience, psychology and addiction, including classes focused on developing human subjects research skills; as well as (3) specialized courses designed to directly and effectively bridge the gap between the core disciplines. Trainees will also attend a biweekly journal club and monthly seminar, led by senior participants in the program, to further support the acquisition of multidisciplinary research skills. The overarching aim of the program is to produce researchers poised to apply state-of-the-art analytic tools to understand the neurobiology of drug abuse. The focus will be characterizing the neural substrates of addiction and other comorbid psychopathologies, always with an eye toward clinical application. Recent increases in the quantity and quality of large-sample, multi-modal datasets that address the neural, genetic and environmental substrates of addiction make this a propitious time for such a training program. Researchers at UVM are ideally suited to provide this training as there exist close links between addiction research, cognitive neuroscience, complex systems and data science, and the mentoring faculty have access to exceptional datasets that are ideal for interrogation with Big Data methods. Armed with coherent domain knowledge and practiced with advanced methods for complex systems, trainees will develop analysis pipelines that: (1) incorporate sophisticated aggregation of longitudinal and multi-modal datasets, including various neuroimaging modalities, genetic information, survey and clinical data; (2) harness the power of supercomputing and modern machine learning algorithms to step beyond linear and univariate effects; and (3) address questions of immediate clinical importance to substance abuse that can inform the determination of risk factors, treatment and intervention strategy, and policy decisions.