Learning High-Order Relationships with Hypergraph Attention-based Spatio-Temporal Aggregation for Brain Disease Analysis

TL;DR

This paper introduces a novel hypergraph attention-based framework that jointly learns high-order brain structures and their temporal dynamics from fMRI data, improving disease classification and revealing meaningful brain interactions.

Contribution

It proposes a new model that learns sparse high-order brain relationships and their temporal evolution, overcoming limitations of predefined structures and ignoring temporal information.

Findings

Outperforms state-of-the-art methods on benchmark datasets.

Effectively identifies meaningful high-order brain interactions.

Provides new insights into brain network modeling for neuropsychiatric disorders.

Abstract

Traditional functional connectivity based on functional magnetic resonance imaging (fMRI) can only capture pairwise interactions between brain regions. Hypergraphs, which reveal high-order relationships among multiple brain regions, have been widely used for disease analysis. However, existing methods often rely on predefined hypergraph structures, limiting their ability to model complex patterns. Moreover, temporal information, an essential component of brain high-order relationships, is frequently overlooked. To address these limitations, we propose a novel framework that jointly learns informative and sparse high-order brain structures along with their temporal dynamics. Inspired by the information bottleneck principle, we introduce an objective that maximizes information and minimizes redundancy, aiming to retain disease-relevant high-order features while suppressing irrelevant signals. Our model comprises a multi-hyperedge binary mask module for hypergraph structure learning, a hypergraph self-attention aggregation module that captures spatial features through adaptive attention across nodes and hyperedges, and a spatio-temporal low-dimensional network for extracting discriminative spatio-temporal representations for disease classification. Experiments on benchmark fMRI datasets demonstrate that our method outperforms the state-of-the-art approaches and successfully identifies meaningful high-order brain interactions. These findings provide new insights into brain network modeling and the study of neuropsychiatric disorders.