# NeuroTree: Hierarchical Functional Brain Pathway Decoding for Mental Health Disorders

arXiv: https://arxiv.org/abs/2502.18786 · HTML: https://mlsanity.com/paper/2502.18786/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 71 references that are in the map.
- First posted on arXiv 26 Feb 2025.

## Builds on (most cited first, 14 of 14)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing](https://mlsanity.com/paper/1905.00067/) (2019)
- [InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction](https://mlsanity.com/paper/1903.04233/) (2019)
- [Convolutional Kernel Networks for Graph-Structured Data](https://mlsanity.com/paper/2003.05189/) (2020)
- [Path Neural Networks: Expressive and Accurate Graph Neural Networks](https://mlsanity.com/paper/2306.05955/) (2023)
- [Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis](https://mlsanity.com/paper/2007.14589/) (2020)
- [CortexODE: Learning Cortical Surface Reconstruction by Neural ODEs](https://mlsanity.com/paper/2202.08329/) (2022)
- [BrainODE: Dynamic Brain Signal Analysis via Graph-Aided Neural Ordinary Differential Equations](https://mlsanity.com/paper/2405.00077/) (2024)
- [Learning High-Order Relationships of Brain Regions](https://mlsanity.com/paper/2312.02203/) (2023)
- [Higher-order Weighted Graph Convolutional Networks](https://mlsanity.com/paper/1911.04129/) (2019)
- [Machine learning techniques for the Schizophrenia diagnosis: A comprehensive review and future research directions](https://mlsanity.com/paper/2301.07496/) (2023)
- [Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation](https://mlsanity.com/paper/2405.13190/) (2024)
- [Spatial Craving Patterns in Marijuana Users: Insights from fMRI Brain Connectivity Analysis with High-Order Graph Attention Neural Networks](https://mlsanity.com/paper/2403.00033/) (2024)
- [Identifying the Hierarchical Emotional Areas in the Human Brain Through Information Fusion](https://mlsanity.com/paper/2408.00525/) (2024)

## Built on by (most cited first, 1 of 1)

- [DynFS-MoE: Dynamic Functional-Structural Mixture-of-Experts for Post-Traumatic Epilepsy Diagnosis](https://mlsanity.com/paper/2606.16203/) (2026)

## Abstract (arXiv)

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.