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arXiv · 2507.02847

MvHo-IB: Multi-View Higher-Order Information Bottleneck for Brain Disorder Diagnosis

Kunyu Zhang·Qiang Li·Shujian Yu
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Abstract

Recent evidence suggests that modeling higher-order interactions (HOIs) in functional magnetic resonance imaging (fMRI) data can enhance the diagnostic accuracy of machine learning systems. However, effectively extracting and utilizing HOIs remains a significant challenge. In this work, we propose MvHo-IB, a novel multi-view learning framework that integrates both pairwise interactions and HOIs for diagnostic decision-making, while automatically compressing task-irrelevant redundant information. MvHo-IB introduces several key innovations: (1) a principled method that combines O-information from information theory with a matrix-based Renyi alpha-order entropy estimator to quantify and extract HOIs, (2) a purpose-built Brain3DCNN encoder to effectively utilize these interactions, and (3) a new multi-view learning information bottleneck objective to enhance representation learning. Experiments on three benchmark fMRI datasets demonstrate that MvHo-IB achieves state-of-the-art performance, significantly outperforming previous methods, including recent hypergraph-based techniques. The implementation of MvHo-IB is available at https://github.com/zky04/MvHo-IB.

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Kunyu Zhang, Qiang Li, Shujian Yu. 2025-07-03. MvHo-IB: Multi-View Higher-Order Information Bottleneck for Brain Disorder Diagnosis. https://doi.org/10.1007/978-3-032-05182-0_40

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