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.
Alzheimer's disease is an irreparable and progressive neurodegenerative disease, mainly affecting older people, causing drastic impairment in cognitive functions, memory loss, and behavioral changes [1]. With the increasing aging population in the entire global health setup, Alzheimer's cases are on the rise, and estimates show that by 2050, more than 130 million people are likely to suffer from Alzheimer's, causing an enormous burden on the entire global health infrastructure. The pre-dementia phase of Alzheimer's, identified as Mild Cognitive Impairment (MCI), is an intermediate phase between aging and dementia, wherein the required cognitive functions are diminished, but the capability to manage day-to-day activities is maintained [2]. The lack of any conclusive treatment [3], underlines the prime importance of early detection and subsequent interventions to control the progression of Alzheimer's and improve the health outcomes [4]. The diagnostic requirements for Alzheimer's generally entailed cognitive function tests like the Mini-Mental State Examination (MMSE) and the Clinical Dementia Rating (CDR) [5], accompanied by neuroimaging techniques, focusing on obtaining structural and functional details on the pathology of Alzheimer's and MCI, describing the abnormalities in the Alzheimer's and MCI-inflicted brains [6]. These techniques, being very cumbersome, require extensive expertise and possess low accuracy rates, thereby underlining the increasing demands and requirements for sophisticated computer-aided diagnostic techniques, possessing enhanced accuracy and efficiency in Alzheimer's diagnostics.
Based on the weaknesses of conventional diagnostic approaches and neuroimaging analysis techniques, various studies have been conducted to develop innovative advanced computing approaches, intending to improve early Alzheimer’s disease diagnosis. In early-stage diagnostic studies, conventional machine learning classifiers, including logistic regression, sparse inverse covariance estimation, and multi-kernel SVMs, were used to distinguish between Alzheimer’s disease, Mild Cognitive impairment, and Cognitive Normal (CN) and showed moderate accuracy and • A two-stage relational attention modeling with node-wise gated fusion is proposed, which enables explanation of attention weights on each edge and each modality. • An episodic meta-learning approach is proposed to improve the generalization capability of the model, and it can make inductive inference on novel instances and achieve stable performance on various data sets.
The remainder of this article is structured as follows. In Section 2, related work is reviewed; Section 3 describes the methodology and details of the proposed architecture; Section 4 outlines the evaluation setup, datasets, evaluation metrics, and reports experimental results; and Section 5 concludes the paper by summarizing the key findings and suggesting potential directions for future research.
In this section, the current progress in multimodal fusion, graph learning, and Explainable AI in Alzheimer’s disease diagnosis will be examined. The section will begin with the discussions on multimodal fusion techniques, followed by an analysis of graph-based approaches, and finally describe emerging techniques improving the interpretability and accuracy of Alzheimer’s disease diagnostic modeling.
The multimodal fusion techniques used in diagnosing Alzheimer’s disease focus on combining diverse clinical, cognitive, neuroimaging, and genetic features into a unified modeling scheme. More recently, studies on multimodal fusion in Alzheimer’s disease can be primarily clustered into two overarching paradigms, namely feature-level fusion and decision-level fusion. At feature level fusion, cascaded deep learning models, as exemplified by the multimodal mixing transformer (3MT), processed clinical and neuroimaging features together through the mechanisms of crossattention and modality dropout, obtaining robust generalization under missing-modality scenarios [21]. Similarly, another deep learning architecture, the deep multimodal discriminative and interpretability network (DMDIN), achieved feature alignments through the use of multilayer perceptrons and generalized canonical correlation analysis, creating a discriminative shared space with improved separation and identification of distinct patterns in the various modalities [22]. In another line of work, interpretable models of disease progression have utilized MRI, clinical scoring, and genetic polymorphisms through interaction models, boosting robustness against center variability [23]. Meanwhile, other trimodal fusion techniques feature successful discrimination between progressive and stable MCI through the combined use of SNPs, gray-matter ratios, and sMRI features, underscoring the morphological predominance of gray-matter ratios [24]. At the decision level, various late-fusion methods combine the predictions of modality-specific learners. For
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