AI 中文总结
提出信息瓶颈引导的自适应超图Transformer（IBAHGT），统一整合高阶相关性与长短程依赖，实现高精度脑疾病诊断并优于现有方法。
AI 中文摘要
探索大脑网络中的高阶相关性和长程依赖性对于神经科学研究和临床诊断都具有重要价值。然而，以往的研究缺乏对大脑网络中高阶和长程依赖信息的统一整合，且各类信息背后存在大量冗余。这些问题限制了它们在脑疾病诊断中的有效性。为解决这一问题，我们提出了一种信息瓶颈引导的自适应超图Transformer（IBAHGT）。通过引入信息瓶颈（IB）原理，该方法能够在统一框架内自适应学习高阶相关性以及短程和长程依赖性，用于大脑网络分析，实现高精度的脑疾病诊断。IBAHGT由三个关键组件组成：信息瓶颈引导的自适应超图卷积，该组件引入了一种新颖的超图信息瓶颈（HIB）原理，以自适应学习节点与超边之间的超图消息传递权重，优化信息流并捕获大脑网络中具有最大信息量和最小冗余（MIMR）的高阶信息。Transformer编码器通过注意力机制捕获大脑网络中的全局信息，具体建模短程和长程依赖性。信息瓶颈引导的节点级自适应融合利用IB原理为每个节点学习独立权重，促进高阶信息与全局信息的细粒度整合，以获得适用于下游任务的高效表示。大量实验表明，所提出的方法优于当前最先进的方法，并能识别用于临床应用的生物标志物。
英文摘要
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.
CommentsAccepted by Neurips 2026
