Learning Multi-Scale Hypergraph for High-Order Brain Connectivity Analysis

2026-06-02

AI Summary

Existing brain network analysis methods struggle to effectively model high-order interactions among multiple brain regions, limiting the early and precise classification of neurodegenerative diseases. To address this challenge, this work proposes the MuHL framework, which overcomes the limitations of conventional approaches that rely on predefined hyperedges or merely learn edge weights. MuHL leverages multiscale graph signal processing and hierarchical feature extraction to adaptively learn dynamic high-order connectivity structures within a continuous hypergraph space. The method achieves significant improvements in classifying different stages of Alzheimer’s disease and Parkinson’s disease across multiple brain network benchmarks, while also uncovering key brain regions and their collective interaction patterns closely associated with disease progression.

Abstract

Understanding complex interactions between brain regions is critical for early neurodegenerative disease classification such as Alzheimer's Disease (AD) and Parkinson's Disease (PD). While graph-based models are widely used to analyze brain networks, most existing approaches primarily focus on pairwise interactions between directly connected nodes, limiting their ability to capture higher-order dependencies across multiple regions. Although hypergraph-based methods have been proposed to model higher-order relations, many rely on predefined hyperedges or restrict learning to hyperedge weights, reducing flexibility and limiting their capacity to capture multi-resolution structural patterns. In this regard, we introduce an adaptive multi-scale hyperedge learning framework, i.e., MuHL, which constructs hierarchical node features and dynamically learns high-order interactions through continuous hyperedge construction over multi-resolution graph signals. Extensive experiments on multiple brain network benchmarks demonstrate that MuHL consistently improves disease classification performance across different stages, and further identifies key regions of interest (ROIs) and their group-wise interactions from the learned hyperedges that are associated with disease progression, highlighting its potential as a powerful tool for brain network analysis in neurodegenerative disorders.

Problem

• hypergraph
• brain connectivity
• high-order interactions
• neurodegenerative disease
• multi-scale

Innovation

• multi-scale hypergraph
• high-order brain connectivity
• adaptive hyperedge learning
• neurodegenerative disease classification
• graph signal processing