Multimodal graph fusion-based GCN for Alzheimer's disease diagnosis using fMRI and T1-weighted MRI.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder marked by both structural atrophy and functional dysregulation in the brain, yet its early detection remains elusive. Although recent efforts have leveraged artificial intelligence combined with multimodal neuroimaging to improve diagnostic accuracy, these methods often falter in harmonizing disparate data sources and lack the transparency needed for clinical adoption. In particular, the sheer dimensionality of functional Magnetic Resonance Imaging (fMRI) and T1-weighted Magnetic Resonance Imaging (T1w-MRI) features, together with complex inter-modality relationships, can lead to overfitting and hinder the reliable identification of robust biomarkers. To overcome the aforementioned challenges, we propose a novel Multimodal Graph Fusion Graph Convolutional Network (MGF-GCN) that integrates functional (fMRI) and structural (T1w-MRI) brain features for accurate and interpretable AD diagnosis. We construct brain graphs by incorporating nonlinear Granger causality (NGC) from resting-state fMRI (rs-fMRI) to capture inter-regional functional dependencies, alongside morphological features from T1-weighted MRI to enrich node attributes. To effectively align and enhance multimodal representations while preserving the underlying topological structure, we introduce a cross-attention-based graph fusion strategy. To further improve both performance and interpretability, we develop a Bayesian Self-Attention Graph Convolutional Network (BSAGCN), where attention weights are modeled as probability distributions, allowing for the identification of critical brain regions and minimizing noise sensitivity. All features are extracted based on the BN246 brain atlas, facilitating fine-grained localization of potential biomarkers. Experimental results show that our approach significantly outperforms existing methods in diagnostic accuracy and interpretability, providing new insights into the pathophysiological mechanisms of AD and offering valuable support for clinical decision-making.