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Multi-Class Classification of Cannabis and Alcohol Use Disorder: Identifying Common and Substance-Specific Neural

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Area of Science:

  • Neuroscience
  • Computational Psychiatry
  • Addiction Research

Background:

  • Machine learning advances neural signature identification for substance use.
  • Most studies focus on single substances, limiting comparisons and generalizability for polysubstance users.
  • Existing methods struggle to compare shared and distinct network computations across different substance use profiles.

Purpose of the Study:

  • To develop an explainable, connectivity-based multiclass classification framework for addiction.
  • To model distributed brain network organization to differentiate shared and substance-specific addiction mechanisms.
  • To enable direct comparison of neural signatures among cannabis users, alcohol users, and healthy controls.

Main Methods:

  • Utilized functional connectivity features from cue-induced craving tasks.
  • Developed a multiclass classification framework for cannabis users (n=166), alcohol users (n=101), and healthy controls (n=238).
  • Compared functional connectivity-based models against activation-based models.

Main Results:

  • Achieved high out-of-sample classification accuracy: 87% for cannabis, 69% for alcohol, and 73% for controls.
  • Functional connectivity models significantly outperformed activation-based models.
  • Identified distinct network configurations, including enhanced prefrontal coupling in cannabis users and greater insular-striatal integration in alcohol users.

Conclusions:

  • Distinct brain network configurations characterize different substance use profiles.
  • Connectivity-based models offer interpretable biomarkers for addiction neuroscience.
  • Findings advance understanding of shared and substance-specific neural mechanisms in addiction.