Work

NeuroTree: Hierarchical Functional Brain Pathway Decoding for Mental Health Disorders

Year: 2025

Type: article

Abstract: -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.

Source: PubMed Central

DOI: 10.48550/arxiv.2502.18786

Authors: Ding, Jun-En, Luo, Dongsheng, Zilverstand, Anna, Kulkarni, Kaustubh, Liu, Feng

Institutions: Stevens Institute of Technology, Florida International University, University of Minnesota, Icahn School of Medicine at Mount Sinai

Language: English

Topic: Functional Brain Connectivity Studies

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: green

Funder: National Institutes of Health