Interpretable Deep Forecasting of Hazardous Substance Use during High School

点击翻译按钮获取中文摘要

ABSTRACT Substance use disorder (SUD) affects over 20 million Americans, causing personal strife and cost. A major SUD risk factor is hazardous use (HU) of substances during high school (HUSH), when the brain continues to develop, rendering it especially sensitive to environmental factors. Identifying the effects of and risk for HUSH generally focuses on selected interactions between fixed (i.e., trauma, demographics) and modifiable (i.e., mental health, emotion, environment, behavior, sleep) factors, and occasionally brain development features differentiating substance using and non-using cohorts. Results have yielded limited improvements to risk assessment. Thus, we propose a paradigm shift in the study of HUSH, replacing measurement selection and population splitting with mapping individuals to comprehensive multi-dimensional measures. The objective of our novel data-driven process is to identify constellations of fixed and modifiable factors forecasting HUSH in individuals. As our analysis is based on public data sets that include brain imaging, we will document interactions of those constellations with neural circuits to determine neuromechanistic underpinnings of HUSH. Myriad factors influence hazardous substance use, such as the fixed contributors of sex and family history of SUD; the modifiable factors of unhealthy sleep habits, peer pressure, and risk-taking propensity; and brain development characterized by an atypical imbalance between emotion and control network. We will model this heterogeneity via machine (deep) learning technology identifying constellations of measurements in line with our hypotheses regarding prevention, i.e., modifiable behaviors interacting with anomalies in neuroadaptation forecasting HUSH initiation. Aim 1 will forecast initiation of HUSH in the last years of high school based on the closest visit before turning age 16 years for no/low substance users in National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA, N =350) and confirm findings on the larger Adolescent Brain Cognitive Developmental cohort (ABCD, N>11K). HUSH will be defined by substance use criteria recorded through annual self-reports and refined by weekly surveys administered via cell phones. Aim 2 will create a self-supervised learning model explicitly tracking over time interactions across modifiable behaviors, fixed factors, and brain circuits important for hypothesis testing. We will cross-validate the model by identifying HUSH for each high school year and forecast based on data collected prior to high school. We will explore dynamically updating the model as data are acquired to predict resilience, i.e., youth who abstain from hazardous substance use during high school, despite having risk factors such as traumatic and untoward COVID pandemic experiences. Each aim is linked to hypothesis testing concerning factors that can be altered to mitigate the risk of HUSH. This project will be the first to provide patterns accurately forecasting the risk of HUSH on an individual level. Accurately computing this risk would be foundational for enhancing prevention efforts.