Machine learning-based computational validation of the Addictions Neuroclinical Assessment framework in relation to hazardous drinking.

backgroundAddiction is a multifaceted disorder driven by complex neurobiological and psychological mechanisms. The Addictions Neuroclinical Assessment (ANA) framework offers a dimensional mechanistic approach, focusing on three core domains: incentive salience, negative emotionality, and executive function. This study aimed to validate the ANA framework in relation to hazardous drinking using a machine learning approach, with the hypothesis that incentive salience and negative emotionality would be most strongly associated with drinking severity.

methodsWe analysed two independent datasets: a cohort of 1260 nonclinical community-based adults ascertained in 2016-2018 and a cohort of 655 young adults reporting regular binge drinking ascertained in 2017-2018. The three ANA domains were operationalized using behavioural and self-report measures. Four machine learning models (elastic net, support vector machines, random forest, and gradient boosting machines) with nested five-fold cross-validation were used to assess relations between ANA domains and hazardous drinking as measured via the Alcohol Use Disorder Identification Test (AUDIT), a validated screening instrument.

resultsAcross both datasets, elastic net consistently outperformed other models. Incentive salience, largely reflecting alcohol's reinforcing value, was most robustly related to AUDIT score ( LIMITATIONS: These findings may not generalize to individuals who are older or have severe AUD. Cross-sectional data limits longitudinal causal inferences.

conclusionThese results provide robust computational validation for the ANA framework, emphasizing incentive salience and negative emotionality as key domains linked to AUDIT score. Future research should explore diagnostic and longitudinal applications.