Modeling motivation for alcohol in humans using traditional and machine learning approaches

다국어 초록 (Multilingual Abstract)

Given the significant cost of alcohol use disorder (AUD), identifying risk factors for alcohol seeking represents a research priority. Prominent addiction theories emphasize the role of motivation in the alcohol seeking process, which has largely been studied using preclinical models. In order to bridge the gap between preclinical and clinical studies, this study examined predictors of motivation for alcohol self‐administration using a novel paradigm. Heavy drinkers (n = 67) completed an alcohol infusion consisting of an alcohol challenge (target breath alcohol = 60 mg%) and a progressive‐ratio alcohol self‐administration paradigm (maximum breath alcohol 120 mg%; ratio requirements range = 20–3 139 response). Growth curve modeling was used to predict breath alcohol trajectories during alcohol self‐administration. K‐means clustering was used to identify motivated (n = 41) and unmotivated (n = 26) self‐administration trajectories. The data were analyzed using two approaches: a theory‐driven test of a‐priori predictors and a data‐driven, machine learning model. In both approaches, steeper delay discounting, indicating a preference for smaller, sooner rewards, predicted motivated alcohol seeking. The data‐driven approach further identified phasic alcohol craving as a predictor of motivated alcohol self‐administration. Additional application of this model to AUD translational science and treatment development appear warranted.
K‐means clustering was used to identify motivated and unmotivated self‐administration trajectories for heavy drinkers. Data driven models found that steeper delay discounting and higher phasic alcohol craving were predictors of motivated alcohol self‐administration. This combination of traditional and novel analytic approaches should be applied to AUD translational science and treatment development.