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：阿尔茨海默病是一种进行性神经退行性疾病，影响数百万老年人，预计未来几年患病率将显著上升。早期诊断，特别是在轻度认知障碍阶段，对于及时干预至关重要。结构磁共振成像已成为检测阿尔茨海默病相关脑部变化的关键影像学手段，但传统的基于图的方法通常难以处理模态间和跨站点异质性问题，限制了诊断性能。本文提出用于阿尔茨海默病诊断的图匹配网络，旨在模拟从神经影像数据导出的异质性脑图之间的交互。与将每个脑图独立处理的传统方法不同，GMN4AD利用图匹配来捕捉跨图关系，从而提升诊断精度。此外，我们引入一种结合对比学习的测试时域适应策略，以在推理过程中缓解域漂移。在三个公开的阿尔茨海默病数据集上进行的大量实验表明，与当前最先进方法相比，GMN4AD取得了卓越性能，为阿尔茨海默病诊断提供了一种鲁棒且可泛化的解决方案。