Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that affects millions of older adults, with prevalence expected to rise significantly in the coming years. Early diagnosis, particularly during the mild cognitive impairment (MCI) stage, is critical for timely intervention. Structural Magnetic Resonance Imaging (sMRI) has emerged as a key modality for detecting AD-related brain changes, but traditional graph-based approaches often struggle with modality and inter-site heterogeneity, limiting diagnostic performance. In this paper, we propose Graph Matching Network for Alzheimer's Disease Diagnosis (GMN4AD), designed to model interactions between heterogeneous brain graphs derived from neuroimaging data. Unlike conventional methods that treat each brain graph independently, GMN4AD leverages graph matching to capture cross-graph relationships, enhancing diagnostic precision. Furthermore, we introduce a test-time domain adaptation strategy that combines contrastive learning to mitigate domain shifts during inference. Extensive experiments on three public AD datasets demonstrate that GMN4AD achieves superior performance compared to state-of-the-art methods, offering a robust and generalizable solution for AD diagnosis.

翻译：阿尔茨海默病（AD）是一种进行性神经退行性疾病，影响数百万老年人，且其患病率在未来数年内预计将显著上升。早期诊断，尤其是在轻度认知障碍（MCI）阶段，对于及时干预至关重要。结构磁共振成像（sMRI）已成为检测与AD相关脑部变化的关键模态，但传统的基于图的方法常受限于模态与站点间异质性，从而制约了诊断性能。本文提出一种面向阿尔茨海默病诊断的图匹配网络（GMN4AD），旨在建模源自神经影像数据的异质性脑图之间的交互关系。不同于将每张脑图独立处理的传统方法，GMN4AD利用图匹配技术捕获跨图关联，从而提升诊断精度。此外，我们引入一种测试时域自适应策略，结合对比学习以在推理阶段缓解域偏移。在三个公开AD数据集上的大量实验表明，GMN4AD相较于现有最优方法实现了优越性能，为AD诊断提供了鲁棒且泛化性强的解决方案。