Title: Multi-Scale Adaptive Hypergraph Learning for High-Order Brain Connectivity Analysis
Primary area: applications to neuroscience & cognitive science
Keywords: Neurodegenerative Disease, Brain Network Analysis, High-Order Graph Learning

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). Graph-based models are typically employed to investigate brain networks with regional features and their interconnectivity. However, traditional approaches primarily focus on pairwise node interactions between directly connected nodes, limiting their ability to capture higher-order dependencies from multiple brain regions. In this regard, we introduce an adaptive multi-scale hypergraph learning framework, i.e., MASH, which constructs hierarchical node representations and dynamically learns high-order interaction via hyperedges to capture the interactions beyond pairwise structural connections. Through extensive experiments on the ADNI and PPMI dataset, we demonstrate the superiority of MASH by improving classification of different disease stages. Our model further identifies key regions of interest (ROIs) and their group-wise interactions from the learned hyperedges that are closely associated with disease progression, highlighting its potential as a powerful tool for brain network analysis with neurodegenerative disorders.