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

Mahmoud Elsayed1,2, Kyla L Belisario1,2, James G Murphy3

  • 1Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Canada.

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Summary

The Addictions Neuroclinical Assessment (ANA) framework shows incentive salience and negative emotionality are key to hazardous drinking severity. Machine learning models validated these findings, highlighting core domains for understanding alcohol use disorder.

Keywords:
Addictions Neuroclinical Assessmentalcohol misusealcohol use disorderhazardous drinkingmachine learning

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Area of Science:

  • Neuroscience
  • Psychology
  • Machine Learning

Background:

  • Addiction is a complex disorder involving neurobiological and psychological factors.
  • The Addictions Neuroclinical Assessment (ANA) framework uses a dimensional approach focusing on incentive salience, negative emotionality, and executive function.
  • This study aimed to validate the ANA framework for hazardous drinking using machine learning.

Purpose of the Study:

  • To computationally validate the Addictions Neuroclinical Assessment (ANA) framework.
  • To investigate the association between ANA domains (incentive salience, negative emotionality, executive function) and hazardous drinking severity.
  • To test the hypothesis that incentive salience and negative emotionality are most strongly linked to drinking severity.

Main Methods:

  • Analysis of two independent datasets (N=1260 nonclinical adults, N=655 young binge drinkers).
  • Operationalization of ANA domains using behavioral and self-report measures.
  • Application of four machine learning models (elastic net, SVM, random forest, gradient boosting) with cross-validation to assess relations with Alcohol Use Disorder Identification Test (AUDIT) scores.

Main Results:

  • Elastic net models demonstrated superior performance in predicting AUDIT scores.
  • Incentive salience was the strongest predictor (R²=0.389-0.419), followed by negative emotionality (R²=0.293-0.317).
  • Executive function showed a weaker association (R²=0.098-0.109); optimized models explained over half the variance (R²=0.539-0.549).

Conclusions:

  • The study provides robust computational validation for the ANA framework in the context of hazardous drinking.
  • Incentive salience and negative emotionality are identified as critical domains associated with drinking severity.
  • Future research should explore the diagnostic and longitudinal applications of the ANA framework.