Accurate and early diagnosis of Alzheimer's disease (AD) is critical for effective intervention and requires integrating complementary information from multimodal neuroimaging data. However, conventional fusion approaches often rely on simple concatenation of features, which cannot adaptively balance the contributions of biomarkers such as amyloid PET and MRI across brain regions. In this work, we propose MREF-AD, a Multimodal Regional Expert Fusion model for AD diagnosis. It is a Mixture-of-Experts (MoE) framework that models mesoscopic brain regions within each modality as independent experts and employs a gating network to learn subject-specific fusion weights. Utilizing tabular neuroimaging and demographic information from the Alzheimer's Disease Neuroimaging Initiative (ADNI), MREF-AD achieves competitive performance over strong classic and deep baselines while providing interpretable, modality- and region-level insight into how structural and molecular imaging jointly contribute to AD diagnosis. The source code is available at https://github.com/PennShenLab/mref-ad.

翻译：阿尔茨海默病（AD）的准确早期诊断对有效干预至关重要，需要整合多模态神经影像数据中的互补信息。然而，传统融合方法往往依赖特征简单拼接，无法自适应平衡不同脑区中淀粉样蛋白PET和MRI等生物标志物的贡献。本文提出MREF-AD——多模态区域专家融合AD诊断模型。该模型基于混合专家（MoE）框架，将各模态中的中观脑区建模为独立专家，并通过门控网络学习个体化融合权重。利用阿尔茨海默病神经影像学倡议（ADNI）的表格化神经影像和人口统计学信息，MREF-AD在强经典与深度基线方法中展现出竞争性能，同时能从模态和区域层面提供可解释性洞见，揭示结构影像与分子影像如何共同影响AD诊断。源代码已开源：https://github.com/PennShenLab/mref-ad。