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Related Experiment Video

Updated: Dec 5, 2025

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Predicting alcohol dependence from multi-site brain structural measures.

Sage Hahn1, Scott Mackey1, Janna Cousijn2

  • 1Department of Psychiatry, University of Vermont College of Medicine, Burlington, Vermont, USA.

Human Brain Mapping
|October 16, 2020
PubMed
Summary
This summary is machine-generated.

Researchers identified brain imaging biomarkers for alcohol dependence (AD) using a large dataset. This approach helps create generalizable models for diagnosing AD across different populations and research sites.

Keywords:
addictionalcohol dependencegenetic algorithmmachine learningmulti-sitepredictionstructural MRI

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Area of Science:

  • Neuroimaging
  • Addiction Psychiatry
  • Machine Learning in Medicine

Background:

  • Alcohol dependence (AD) diagnosis relies on clinical criteria, but neuroimaging may offer objective biomarkers.
  • Developing generalizable classification models for AD using structural MRI is challenging due to multi-site data variability.

Purpose of the Study:

  • To identify generalizable neuroimaging biomarkers for alcohol dependence from structural MRI data.
  • To develop and validate classification models that perform well on unseen sites and populations.

Main Methods:

  • Mega-analysis of 2,034 participants (AD and controls) across 27 sites from the ENIGMA Addiction Working Group.
  • Exploratory data analysis and evolutionary feature selection using leave-one-site-out cross-validation to mitigate site effects.
  • Ridge regression applied to selected features (cortical thickness, surface area, and putamen volume) for classification.

Main Results:

  • Inadvertent learning of site-effects was observed when not properly accounting for site variability.
  • Leave-one-site-out cross-validation identified key features: left superior frontal gyrus thickness, right lateral orbitofrontal cortex thickness, right transverse temporal gyrus surface area, and left putamen volume.
  • Ridge regression achieved a test-set area under the receiver operating characteristic curve of 0.768.

Conclusions:

  • Strategies for handling multi-site neuroimaging data with varied class distributions are crucial for generalizable AD biomarkers.
  • Specific structural MRI features show potential as biomarkers for identifying individuals with current alcohol dependence.