Information Bottleneck-Guided Adaptive Hypergraph Transformer for Brain Disease Diagnosis

2026-09-29

AI Summary

This study addresses the lack of unified integration of higher-order and long-range dependencies in brain network analysis, as well as the limitation that data redundancy imposes on diagnostic accuracy. To this end, we propose the IBAHGT model, which pioneers an information bottleneck principle for hypergraphs. By incorporating adaptive hypergraph convolution, a Transformer encoder, and a node-level adaptive fusion mechanism, the method optimizes information flow to maximize salient features while minimizing redundancy, thereby achieving fine-grained multi-source information fusion. Experimental results demonstrate that the proposed model outperforms existing state-of-the-art methods and effectively identifies clinically valuable biomarkers, offering a novel paradigm for precise diagnosis of brain disorders.

Abstract

Exploring high-order correlations and long-range dependencies in brain networks holds significant value for both neuroscience research and clinical diagnosis. However, previous studies have lacked a unified integration of high-order and long-range dependency information in brain networks, and there is substantial redundancy behind various types of information. These issues limit their effectiveness in the diagnosis of brain diseases. To address this, we propose an Information Bottleneck-Guided Adaptive HyperGraph Transformer (IBAHGT). By incorporating the information bottleneck (IB) principle, this approach enables adaptive learning of high-order correlations and both short- and long-range dependencies within a unified framework for brain network analysis, achieving high-precision brain disease diagnosis. IBAHGT consists of three key components: an information bottleneck-guided adaptive hypergraph convolution, which introduces a novel hypergraph information bottleneck (HIB) principle to adaptively learn hypergraph message-passing weights between nodes and hyperedges, optimizes information flow and captures high-order information in brain networks that is maximally informative and minimally redundant (MIMR). The Transformer encoder captures global information within brain networks through the attention mechanism, specifically modeling short- and long-range dependencies. An information bottleneck-guided node-level adaptive fusion employs the IB principle to learn independent weights for each node, facilitating the fine-grained integration of high-order information and global information to obtain an efficient representation for downstream tasks. Extensive experiments demonstrate that the proposed method outperforms current state-of-the-art methods and can identify biomarkers for clinical applications.

Problem

• brain disease diagnosis
• high-order correlations
• long-range dependencies
• information redundancy
• brain networks

Innovation

• Information Bottleneck
• Adaptive Hypergraph Convolution
• Transformer Encoder
• Node-level Adaptive Fusion
• Brain Disease Diagnosis