Alzheimer's disease (AD) is a progressive neurodegenerative condition necessitating early and precise diagnosis to provide prompt clinical management. Given the paramount importance of early diagnosis, recent studies have increasingly focused on computer-aided diagnostic models to enhance precision and reliability. However, most graph-based approaches still rely on fixed structural designs, which restrict their flexibility and limit generalization across heterogeneous patient data. To overcome these limitations, the Meta-Relational Copula-Based Graph Attention Network (MRC-GAT) is proposed as an efficient multimodal model for AD classification tasks. The proposed architecture, copula-based similarity alignment, relational attention, and node fusion are integrated as the core components of episodic meta-learning, such that the multimodal features, including risk factors (RF), Cognitive test scores, and MRI attributes, are first aligned via a copula-based transformation in a common statistical space and then combined by a multi-relational attention mechanism. According to evaluations performed on the TADPOLE and NACC datasets, the MRC-GAT model achieved accuracies of 96.87% and 92.31%, respectively, demonstrating state-of-the-art performance compared to existing diagnostic models. Finally, the proposed model confirms the robustness and applicability of the proposed method by providing interpretability at various stages of disease diagnosis.

翻译：阿尔茨海默病（AD）是一种进行性神经退行性疾病，需要早期且精确的诊断以提供及时的临床管理。鉴于早期诊断的至关重要性，近期研究日益关注计算机辅助诊断模型，以提高诊断的精确性和可靠性。然而，大多数基于图的方法仍依赖于固定的结构设计，这限制了其灵活性，并制约了其在异质患者数据上的泛化能力。为克服这些限制，本文提出了基于元关系Copula的图注意力网络（MRC-GAT），作为一种高效的多模态AD分类模型。所提出的架构将基于Copula的相似性对齐、关系注意力与节点融合整合为情景式元学习的核心组件，使得包括风险因素（RF）、认知测试分数和MRI属性在内的多模态特征，首先通过基于Copula的变换在一个共同的统计空间中对齐，随后通过一个多关系注意力机制进行融合。根据在TADPOLE和NACC数据集上的评估，MRC-GAT模型分别取得了96.87%和92.31%的准确率，与现有诊断模型相比展现了最先进的性能。最后，所提出的模型通过在疾病诊断的各个阶段提供可解释性，证实了其方法的鲁棒性和适用性。