NeuroTree: Hierarchical Functional Brain Pathway Decoding for Mental Health Disorders

TL;DR

NeuroTree is a novel framework that decodes hierarchical brain pathways from fMRI data, improving understanding and diagnosis of mental disorders by capturing complex neural relationships and patterns.

Contribution

It introduces a learnable NeuroTree model combining GNNs, neural ODEs, and contrastive learning to better characterize brain network hierarchies and improve mental disorder classification.

Findings

Achieves state-of-the-art performance on mental disorder datasets.

Provides insights into age-related neural deterioration patterns.

Enhances understanding of hierarchical brain pathway structures.

Abstract

Mental disorders are among the most widespread diseases globally. Analyzing functional brain networks through functional magnetic resonance imaging (fMRI) is crucial for understanding mental disorder behaviors. Although existing fMRI-based graph neural networks (GNNs) have demonstrated significant potential in brain network feature extraction, they often fail to characterize complex relationships between brain regions and demographic information in mental disorders. To overcome these limitations, we propose a learnable NeuroTree framework that integrates a k-hop AGE-GCN with neural ordinary differential equations (ODEs) and contrastive masked functional connectivity (CMFC) to enhance similarities and dissimilarities of brain region distance. Furthermore, NeuroTree effectively decodes fMRI network features into tree structures, which improves the capture of high-order brain regional pathway features and enables the identification of hierarchical neural behavioral patterns essential for understanding disease-related brain subnetworks. Our empirical evaluations demonstrate that NeuroTree achieves state-of-the-art performance across two distinct mental disorder datasets. It provides valuable insights into age-related deterioration patterns, elucidating their underlying neural mechanisms.