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Substance use disorders involve a pattern of using drugs more extensively than intended and continuing use despite harmful consequences. This includes legal substances like alcohol and nicotine, as well as illegal drugs. These disorders often involve both physical and psychological dependence, reflecting compulsive use of substances that significantly alter thoughts, feelings, and behaviors, contributing to a major public health issue.
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Related Experiment Video

Updated: Jun 5, 2026

Handwriting Analysis Indicates Spontaneous Dyskinesias in Neuroleptic Naïve Adolescents at High Risk for Psychosis
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Published on: November 21, 2013

Predicting Substance Use and Psychotic-Like Experiences in Adolescents.

Carolyn M Amir, Catherine Walsh, Haley R Wang

    Medrxiv : the Preprint Server for Health Sciences
    |June 4, 2026
    PubMed
    Summary

    Childhood brain scans and clinical data can predict adolescent substance use and psychosis risk. Early identification of these risks is possible, aiding in timely interventions for mental health.

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    Area of Science:

    • Neuroscience
    • Developmental Psychology
    • Psychiatry

    Background:

    • Adolescence is a critical period for developing substance use and psychosis-spectrum symptoms.
    • Early risk factors for these co-occurring conditions are not well understood.
    • Predicting these outcomes in childhood is crucial for early intervention.

    Purpose of the Study:

    • To identify childhood predictors of adolescent substance use and psychotic-like experiences.
    • To determine if distinct risk profiles exist for each outcome and their co-occurrence.
    • To utilize machine learning to analyze neuroimaging and clinical data.

    Main Methods:

    • Longitudinal data from the Adolescent Brain Cognitive Development (ABCD) Study (n=10,134).
    • Assessed demographic, clinical, structural, and functional neuroimaging measures in childhood (mean age=9.96 years).
    • Employed multivariate machine learning models to predict adolescent outcomes.

    Main Results:

    • Machine learning models accurately predicted psychotic-like experiences (AUROC=0.780), co-occurring substance use and psychotic-like experiences (AUROC=0.828), and substance use (AUROC=0.626).
    • Identified distinct patterns in functional brain connectivity, brain activation, demographics, and clinical factors for each outcome.
    • Demonstrated that risk profiles are detectable in childhood.

    Conclusions:

    • Partially dissociable developmental risk profiles for adolescent substance use and psychosis-spectrum symptoms are detectable in childhood.
    • Findings highlight the importance of considering comorbidity when assessing risk factors for mental health outcomes.
    • Early neuroimaging and clinical data can inform prediction models for adolescent mental health.