MRC-GAT: A Meta-Relational Copula-Based Graph Attention Network for Interpretable Multimodal Alzheimer's Disease Diagnosis

2026-02-17

AI Summary

This work addresses the limitations of existing graph-based Alzheimer’s disease (AD) diagnostic models, whose rigid architectures struggle to accommodate patient heterogeneity, thereby compromising generalization and diagnostic flexibility. To overcome this, the authors propose MRC-GAT, a novel framework that integrates multimodal data—including risk factors, cognitive assessments, and MRI scans—within a meta-learning paradigm. Heterogeneous features are aligned via Copula transformations, and a multi-relational graph attention mechanism is introduced to enable interpretable disease classification. Evaluated on the TADPOLE and NACC datasets, MRC-GAT achieves accuracies of 96.87% and 92.31%, respectively, significantly outperforming current state-of-the-art methods. The model demonstrates strong generalization, adaptive flexibility across diverse patient profiles, and interpretable diagnostic insights across multiple disease stages.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Computer Vision: Multi-modal Vision

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• Alzheimer's disease
• graph-based diagnosis
• heterogeneous data
• generalization
• early diagnosis

Innovation

• Copula-based similarity alignment
• relational attention
• episodic meta-learning
• multimodal fusion
• interpretable graph neural network