Journal article
Integrating multilevel, multidomain and multimodal neuroimaging factors to predict early alcohol exposure trajectories using explainable AI
Developmental cognitive neuroscience, v 75, 101597
01 Oct 2025
PMID: 40684513
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Various multilevel, multidomain factors at the individual-, family-, and environmental-level, and changes in neurobiology have been associated with the likelihood of developing alcohol use disorder (AUD) or binge drinking later in life. Prior studies have examined only limited subsets of these factors, typically focusing on cross-sectional associations with alcohol initiation, binge drinking, or AUD rather than exploring longitudinal alcohol use trajectories. Our study addresses these gaps by applying machine learning methods to a comprehensive set of multilevel, multidomain factors and multimodal brain imaging features (including brain structure and functional connectivity) to prospectively predict early alcohol sipping trajectories. Using data from the Adolescent Brain Cognitive Development Study, we identified functional connectivity features and multilevel factors that distinguish youth with an increasing alcohol sipping trajectory from those who initially experimented with alcohol but reduced their consumption over time. Moreover, structural and functional features predicted differences between youth who increasingly sipped over time and those who did not engage in alcohol experimentation. 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, environmental and neurobiological factors impact the development of different alcohol sipping trajectories over time.
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Details
- Title
- Integrating multilevel, multidomain and multimodal neuroimaging factors to predict early alcohol exposure trajectories using explainable AI
- Creators
- Ana Ferariu - Drexel UniversityHansoo Chang - Drexel UniversityAshni Kumar - Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA, USAAlexandra Sahl - Drexel UniversityStephanie Gorka - The Ohio State UniversityLei Wang - The Ohio State UniversityWesley K Thompson - Laureate Institute for Brain ResearchFengqing Zhang (Corresponding Author) - Drexel University
- Publication Details
- Developmental cognitive neuroscience, v 75, 101597
- Publisher
- Elsevier
- Number of pages
- 13
- Grant note
- National Institutes of Health
Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development SM (ABCD) Study (https://abcdstudy.org) , held in the NIMH Data Archive (NDA) . This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9-10 and follow them over 10 years into early adulthood. The ABCD Study (R) is supported by the National Institutes of Health and
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Psychological and Brain Sciences (Psychology)
- Web of Science ID
- WOS:001601303400001
- Scopus ID
- 2-s2.0-105011062625
- Other Identifier
- 991022065123504721
UN Sustainable Development Goals (SDGs)
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InCites Highlights
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Neurosciences
- Psychology, Developmental