BrainHGT: A Hierarchical Graph Transformer for Interpretable Brain Network Analysis

TL;DR

BrainHGT introduces a hierarchical Graph Transformer that models the brain's modular and hierarchical structure, improving disease classification and interpretability by capturing local, long-range, and modular interactions.

Contribution

It proposes a novel hierarchical Graph Transformer with a long-short range attention encoder and a prior-guided clustering module for more biologically plausible brain network analysis.

Findings

Significantly improves disease identification accuracy

Effectively captures brain sub-functional modules

Enhances interpretability of brain network models

Abstract

Graph Transformer shows remarkable potential in brain network analysis due to its ability to model graph structures and complex node relationships. Most existing methods typically model the brain as a flat network, ignoring its modular structure, and their attention mechanisms treat all brain region connections equally, ignoring distance-related node connection patterns. However, brain information processing is a hierarchical process that involves local and long-range interactions between brain regions, interactions between regions and sub-functional modules, and interactions among functional modules themselves. This hierarchical interaction mechanism enables the brain to efficiently integrate local computations and global information flow, supporting the execution of complex cognitive functions. To address this issue, we propose BrainHGT, a hierarchical Graph Transformer that simulates the brain's natural information processing from local regions to global communities. Specifically, we design a novel long-short range attention encoder that utilizes parallel pathways to handle dense local interactions and sparse long-range connections, thereby effectively alleviating the over-globalizing issue. To further capture the brain's modular architecture, we designe a prior-guided clustering module that utilizes a cross-attention mechanism to group brain regions into functional communities and leverage neuroanatomical prior to guide the clustering process, thereby improving the biological plausibility and interpretability. Experimental results indicate that our proposed method significantly improves performance of disease identification, and can reliably capture the sub-functional modules of the brain, demonstrating its interpretability.