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Support vector machine-based classification of bulimia nervosa using diffusion tensor imaging.

Linli Zheng1,2, Yu Wang1, Ma Jing1

  • 1Mental Health Center, West China Hospital, Sichuan University, Chengdu, China.

Frontiers in Psychiatry
|September 26, 2025
PubMed
Summary

Machine learning using diffusion tensor imaging (DTI) effectively identified brain structure differences in individuals with bulimia nervosa (BN). Fractional anisotropy (FA) showed the most promise for diagnosing BN and understanding its neurobiology.

Keywords:
bulimia nervosadiffusion tensor magnetic resonance imageeating disordersmachine learningsupport vector machines

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Area of Science:

  • Neuroimaging
  • Psychiatric Disorders
  • Machine Learning

Background:

  • Bulimia nervosa (BN) is associated with brain structure alterations.
  • Identifying reliable neurobiological markers for BN is crucial for diagnosis and treatment.

Purpose of the Study:

  • To apply machine learning (ML) methods with diffusion tensor imaging (DTI) data to diagnose BN.
  • To uncover potential neurobiological markers for BN using DTI.

Main Methods:

  • 34 drug-naive females with BN and 34 healthy controls (HCs) underwent DTI scans.
  • Fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD) were extracted.
  • Support vector machines (SVM) were used for classification.

Main Results:

  • The FA model achieved the highest classification performance (AUC=0.821), with 82.35% accuracy, 82.35% specificity, and 85.29% sensitivity.
  • Key brain regions identified include the frontal lobe, brainstem, and cerebellum.
  • Other DTI metrics (MD, AD, RD) showed lower classification performance.

Conclusions:

  • DTI-based ML models can effectively distinguish individuals with BN from HCs.
  • FA is a promising neuroimaging marker for BN.
  • This approach offers insights into the neurobiology of BN.