NeuroTree: Hierarchical Functional Brain Pathway Decoding for Mental Health Disorders

Jun-En Ding, Dongsheng Luo, Chenwei Wu, Feng Liu

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. The code and datasets are available at https://github.com/Ding1119/NeuroTree.

Lay Summary: Mental disorders are among the most widespread diseases globally. In this work, we propose a novel framework called NuroTree that contributes to computational neuroscience by integrating demographic information into Neural ODEs for brain network modeling via $k$-hop graph convolution, investigating addiction and schizophrenia datasets to decode fMRI signals and construct disease-specific brain trees with hierarchical functional subnetworks, and achieving state-of-the-art classification performance while effectively interpreting how these disorders alter functional connectivity related to brain age.

Application-Driven Machine Learning: This submission is on Application-Driven Machine Learning.

Link To Code: https://github.com/Ding1119/NeuroTree

Primary Area: Applications->Neuroscience, Cognitive Science

Keywords: fMRI, Mental Disorders, Graph Neural Networks, Neuroscience

Submission Number: 5740