Deep Phenotyping of Heavy Drinking in Young Adults with Behavioral Scales, Neuropsychological Tasks, and Smartphone Sensing Technology

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ABSTRACT Alcohol use disorder (AUD) reaches peak levels during young adulthood, making it critical that we understand person-specific alcohol risk profiles in young adults to prevent AUD or intervene before this disorder becomes chronic. The National Institute on Alcohol Abuse & Alcoholism’s (NIAAA) neurobiological framework, the Addictions Neuroclinical Assessment (ANA), offers an innovative approach for understanding AUD in this population. The ANA posits that individual differences in 3 neurofunctional domains can help differentiate the substantial clinical heterogeneity in AUD. The ANA builds upon the NIMH Research Domain Criteria (RDoC), a dimensional framework for investigating mental disorders in terms of varying degrees of dysfunction in 6 core biological/psychological systems. As a starting point, NIAAA endorsed an initial 3-domain ANA model, which aligns with most RDoC systems. Two RDoC systems, not in the initial ANA, include sleep/circadian and social processes and are highly relevant to young adult alcohol risk. The 3-domain ANA model has been validated in research with adults and predicts treatment outcomes, including work by our team. It has yet to be investigated in young adults. We propose to study the ANA model, expanded to include sleep/circadian and social processes, in young adults (non-college/college, ages 18-25) (N=350), who report recent moderate to heavy drinking. Specifically, young adults will participate in a 12-month longitudinal study, which involves completing self-report questionnaires, neuropsychological tasks, and engaging in passive and active smartphone data collection. These assessments include recommended/similar ANA measures, RDoC-relevant sleep/circadian and social measures, and novel smartphone measures to improve ANA scalability. Smartphone data collection is rigorous, unobtrusive, scalable, and highly relevant for young adults given their extensive smartphone use. Smartphones can generate rich moment-by-moment neurobehavioral data (e.g., mobility, sociality) passively through embedded sensors and phone usage logs and actively through survey prompts. These digital behavioral indicators show promise for predicting psychiatric disorder symptoms, course, treatment response, and functional brain activity. We will use data from study participants to achieve the following aims: For Aim 1, we will validate an ANA model for young adults (ANA-YA) using baseline self-report and neuropsychological measures related to the 3 ANA domains and RDoC sleep/circadian and social processes. We will then examine baseline associations between the ANA-YA model and baseline drinking measures. We will also explore longitudinal change in ANA-YA phenotypes and test whether these changes predict 12-month alcohol outcomes. For Aim 2, we will examine the baseline associations of smartphone data to ANA-YA domains and then examine longitudinal change in smartphone data and whether these changes predict 12-month ANA-YA phenotypes. Our results will advance the science of young adult AUD neurobiology and identify efficient, valid assessments for distinguishing alcohol risk in this group.