Integrating multilevel, multidomain and multimodal neuroimaging factors to predict early alcohol exposure trajectories using explainable AI

Abstract

ABSTRACT Alcohol consumption tends to increase from childhood to adolescence, and risk factors at the individual, family, and environmental level (multilevel, multidomain factors), as well as changes in brain structure and function, have been associated with the likelihood of developing alcohol use disorder (AUD) or binge drinking later in life. Most studies have focused on limited subsets of multilevel or neuroimaging factors, typically emphasizing the risk of alcohol initiation, binge drinking, or AUD in cross-sectional designs rather than exploring longitudinal alcohol consumption trajectories. Our study addresses these gaps by examining a comprehensive set of multilevel, multidomain factors and multimodal brain imaging features to prospectively predict early alcohol sipping trajectories over time with large data from the Adolescent Brain Cognitive Development Study. We applied machine learning methods to baseline individual, family, and environmental factors as well as structural and functional brain connectivity features, analyzing these predictors separately and in combination. Key findings reveal that functional connectivity and multilevel factors distinguish youth with an increasing alcohol sipping trajectory from those who initially experimented with alcohol but reduced their consumption over time. Moreover, we found important structural and functional features that predicted those who increasingly sipped over time versus the ones who did not engage in alcohol experimentation. Stable interactions between age, socioeconomical status and positive attitudes towards drinking could predict a pattern of increasing alcohol sipping over time. These trends could inform how individual, family, and environmental factors together with brain imaging features impact the development of different alcohol sipping trajectories over time.