PROJECT SUMMARY / ABSTRACT
Alcohol use disorder (AUD) is a chronic debilitating condition that accounts for over half of all substance
abuse treatment cases in the United States. Most AUD patients relapse despite treatment. It is increasingly
recognized that deficits in avoidance learning are critically involved in motivating habitual and heavy alcohol
use. Specifically, due to alcohol’s anxiolytic and analgesic properties, many engage in drinking to avoid painful
physical and affective states. Paradoxically, chronic alcohol use is associated with increased pain reactivity
and decreased cognitive control. These changes reinforce compulsive drinking as a maladaptive avoidance
strategy and further compromise learning of its harmful consequences. Yet, the neural, physiological, and
psychological processes inter-relating avoidance learning dysfunction with the maintenance and relapse of
AUD remain poorly understood. This K99/R00 proposal addresses this critical gap in research.
To this end, we propose to collect functional magnetic resonance imaging (fMRI) data in treatment-
seeking AUD patients during a probabilistic learning task which features pain and reward. Our first aim is to
characterize dysfunctions of the brain circuits supporting pain reactivity and cognitive control during avoidance
learning in AUD patients, as compared to social drinkers. In the second aim, we will evaluate how the neural
markers, along with clinical, physiological and behavioral metrics, may be used to (1) diagnostically distinguish
clinical characteristics; and (2) model the key pathophysiological pathways that sustain habitual alcohol use. In
the third aim, we will identify the risk factors that best predict relapse during the 12-month follow-up, thus
offering clinical implications for improving treatment outcomes.
The long-term goal of the candidate is to start an independent career in neuroscience research of
alcohol addiction. This proposed study will support this goal by serving as a launchpad for the candidate to
transition to an independent investigator. The candidate has trained extensively in cognitive neuroscience and
devoted himself to the field of addiction neuroscience. By conducting this study, the candidate will broaden his
training in the clinical and neurobiological investigation of AUD as well as gain expertise in machine learning
and Bayesian modeling. The candidate has identified his training needs and assembled a team of expert
mentors for this K99/R00 proposal. The training plan includes structured mentoring, supervised research,
formal coursework, presentations at scientific meetings, and professional development. The exceptional
research environment and intellectual resources at Yale University will allow the candidate to receive ample
guidance, learn novel techniques, and gain independence, while pursuing the research he is passionate about.