Adaptive Spectral Graph Attention Filtering Network for Alzheimer's Disease Classification Using Multimodal Data.

Abstract

Early detection of Alzheimer's Disease (AD) is critical for timely intervention and management. However, existing graph-based approaches often fail to fully leverage the rich spectral-domain information inherent in brain network signals. To address this limitation, we propose an Adaptive Spectral Graph Attention Filtering Network (ASGAFN), which effectively models the spectral structures of functional and structural brain networks to enhance classification performance. Specifically, we first construct structural and functional brain network graphs from diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI). Subsequently, a frequency-encoding-guided attention mechanism is designed to learn a shared spectral response function across graphs. This enables the construction of interpretable and adaptive spectral filters while mitigating semantic misalignment across different spectral domains. Furthermore, a spectral energy sensing module is incorporated to facilitate graph-specific adaptation, thereby enhancing flexibility and subject-level discriminability. Finally, the refined spectral signals are transformed back to the spatial domain and fused via a Multimodal Fusion and Enhancement Layer (MFEL). Extensive experiments demonstrate that ASGAFN significantly outperforms multiple baselines in AD-related classification tasks. It achieves accuracies of 96.64% (AD vs. NC), 90.48% (MCI vs. NC), and 91.75% (AD vs. MCI). Additionally, it attains an accuracy of 87.12% in the three-class classification task, underscoring its effectiveness in distinguishing among multiple disease stages. These findings validate the efficacy of spectral-domain modeling and multimodal fusion.