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Multivariate patterns linking brain microstructure to temperament and behavior in adolescent eating disorders.

Carolina Makowski1, Golia Shafiei2, Megan Martinho3

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Adolescents with eating disorders show distinct brain-behavior patterns. An

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Radiology

Background:

  • Eating disorders (EDs) are complex psychiatric conditions with diverse symptoms.
  • Previous neuroimaging studies often focused on limited brain regions or symptoms in EDs.
  • Understanding the neurobiological underpinnings of ED heterogeneity is crucial for treatment.

Purpose of the Study:

  • To map whole-brain microstructural and morphometric patterns to various behaviors in adolescents with EDs.
  • To utilize multivariate methods for a comprehensive analysis of brain-behavior relationships in EDs.
  • To identify neurobiological correlates of specific ED symptom profiles.

Main Methods:

  • Diffusion-weighted imaging analyzed with restriction spectrum imaging in 91 adolescents with EDs and 48 controls.
  • Partial least squares analysis to link 38 behavioral measures to white matter and subcortical diffusion patterns.
  • Analysis across 65 regions of interest covering cognition, temperament, and ED symptoms.

Main Results:

  • The primary analysis revealed a significant latent variable explaining 46.9% of brain-microstructure and behavior covariance.
  • An 'undercontrolled' behavioral profile (emotional dysregulation, novelty seeking, low effortful control) correlated with increased white matter diffusion, especially in frontal, limbic, and thalamic tracts.
  • Individuals with binge-purge symptoms had higher scores for this latent variable compared to those with restrictive eating symptoms.

Conclusions:

  • Multivariate modeling effectively captures the complex brain-behavior relationships in eating disorders.
  • Findings highlight specific neurobiological patterns associated with an 'undercontrolled' profile and binge-purge behaviors.
  • This neurobiologically-informed approach can aid in future clinical subtyping and prediction models for EDs.