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Predicting drug craving among ketamine-dependent users through machine learning based on brain structural measures.

Yunkai Sun1, Qiuxia Wu2, Jinsong Tang1

  • 1Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, PR China.

Progress in Neuro-Psychopharmacology & Biological Psychiatry
|December 11, 2024
PubMed
Summary

Neuroimaging identified brain structure biomarkers that predict ketamine craving. These brain regions, including the orbitofrontal cortex and amygdala, are linked to dopamine and serotonin systems, offering insights into addiction relapse.

Keywords:
AddictionCortical thicknessCravingDependenceKetamineMachine learning

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Area of Science:

  • Neuroimaging and Addiction Neuroscience
  • Neurobiology of Substance Use Disorders
  • Neuroimaging Biomarkers

Background:

  • Craving is a primary driver of drug-seeking behavior and a significant predictor of relapse in substance use disorders.
  • Identifying objective biomarkers for craving is crucial for understanding and treating addiction.
  • Ketamine dependence is associated with intense craving, necessitating effective prediction and management strategies.

Purpose of the Study:

  • To identify and validate neuroanatomical biomarkers for quantifying and predicting craving in ketamine-dependent individuals.
  • To develop a predictive model of craving based on neuroanatomical features.
  • To explore the relationship between neuroanatomical predictors of craving and neurotransmitter systems.

Main Methods:

  • Support vector regression (SVR) with a 10-fold cross-validation framework was used to build a craving prediction model.
  • The model utilized regional cortical thickness (CT), surface area (SA), and subcortical volume (SV) derived from T1-weighted MRI scans.
  • Generalizability was tested on an independent sample, and spatial correlation analysis linked regional contributions to receptor/transporter densities.

Main Results:

  • A neuroanatomical craving model successfully predicted self-reported craving (r=0.635).
  • Key predictors included surface area in the medial orbitofrontal cortex and supramarginal gyrus, cortical thickness in the anterior cingulate and occipital regions, and amygdala gray matter volume.
  • These predictors demonstrated generalizability to an independent sample and correlated with dopamine D2, serotonin 5-HTa/b receptors, and serotonin transporter densities.

Conclusions:

  • The study provides a novel perspective on craving prediction in ketamine dependence using neuroanatomical biomarkers.
  • Specific frontal and parietal brain regions were identified as key correlates of craving.
  • The findings suggest a link between the neuroanatomical underpinnings of craving and the brain's dopaminergic and serotonergic systems.