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

medRxiv [Preprint]. 2024 Nov 26:2024.11.24.24317857. doi: 10.1101/2024.11.24.24317857.

Abstract

Eating disorders (EDs) are multifaceted psychiatric disorders characterized by varying behaviors, traits, and cognitive profiles thought to drive symptom heterogeneity and severity. Non-invasive neuroimaging methods have been critical to elucidate the neurobiological circuitry involved in ED-related behaviors, but often focused on a limited set of regions of interest and/or symptoms. The current study harnesses multivariate methods to map microstructural and morphometric patterns across the entire brain to multiple domains of behavior and symptomatology in patients. Diffusion-weighted images, modeled with restriction spectrum imaging, were analyzed for 91 adolescent patients with an ED and 48 healthy controls. Partial least squares analysis was applied to map 38 behavioral measures (encompassing cognition, temperament, and ED symptoms) to restricted diffusion in white matter tracts and subcortical structures across 65 regions of interest. The first significant latent variable explained 46.9% of the covariance between microstructure and behavior. This latent variable retained a significant brain-behavior correlation in held-out data, where an 'undercontrolled' behavioral profile (e.g., higher emotional dysregulation, novelty seeking; lower effortful control and interoceptive awareness) was linked to increased restricted diffusion across white matter tracts, particularly those joining frontal, limbic, and thalamic regions. Individually-derived brain and behavior scores for this latent variable were higher in patients with binge-purge symptoms, compared to those with only restrictive eating symptoms. Findings demonstrate the value of applying multivariate modeling to the array of brain-behavior relationships inherent to the clinical presentation of EDs, and their relevance for providing a neurobiologically-informed model for future clinical subtyping and prediction efforts.

Publication types

  • Preprint