Brain Region-Centered MultiModal Hypergraph Fusion for MCI Conversion Prediction

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Abstract

Alzheimers disease (AD) is a progressive neurodegenerative disorder, with mild cognitive impairment (MCI) as its prodromal stage. Accurate MCI conversion prediction is critical for early intervention and resource allocation. Recently, deep learning-based multi-modal neuroimaging fusion has become a hot research topic in AI-assisted AD diagnosis. Existing multimodal fusion approaches are limited by modality heterogeneity, ability to model inter-regional interactions, and insufficient interpretability. To address these challenges, BRC-MMHF, a Brain Region-Centered MultiModal Hypergraph Fusion framework, is proposed. In this framework, parameter-free channel exchange and ROI-level feature extraction mechanisms are employed to reduce modality heterogeneity and extract structurally consistent features from MRI and PET. A multimodal hypergraph models high-order interregional cross-modal relationships, while a lesion-aware module highlights disease-relevant regions to enhance interpretability. Structured clinical data are incorporated through a lightweight tabular encoder to improve adaptability and diagnostic robustness. Experiments on the ADNI dataset show that BRC-MMHF achieves 80.71% accuracy and 89.7% AUC in MCI conversion prediction, outperforming a range of state-of-the-art methods based on MRI and PET imaging, while providing high interpretability.

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