Gated Multi-Graph Fusion via Graph Attention Networks for Alzheimer's Disease Detection

2026-06-30

AI Summary

This study addresses the limitations of existing Alzheimer’s disease (AD) speech-based detection methods, which often overlook the disruption of nonlinear linguistic structures and clinical heterogeneity. To this end, the authors propose a “Content–Structure–Flow” multi-view graph representation framework that leverages automatic speech recognition to construct semantic, dependency, and pointwise mutual information (PMI) co-occurrence graphs, effectively capturing narrative logic deviations. An heterogeneity-aware adaptive gating mechanism is further introduced to dynamically fuse these multi-view graphs, enhancing robustness across diverse populations. Integrating graph attention networks with the proposed multi-view fusion strategy, the model achieves a classification accuracy of 90.00% on the ADReSSo dataset. Ablation studies confirm the effectiveness and necessity of each component in the framework.

Abstract

Spontaneous speech is a vital non-invasive biomarker for Alzheimer's Disease (AD), yet many systems overlook non-linear structural disruptions and clinical heterogeneity in pathological language. We propose a Multi-View Gated Graph Attention Network that transcribes audio via Automatic Speech Recognition (ASR) to construct semantic, dependency, and co-occurrence graphs, characterizing speech through a "content-structure-flow" framework. Notably, the co-occurrence graph leverages Pointwise Mutual Information (PMI) from a normative corpus to quantify narrative logic and linguistic deviation. To address symptomatic diversity, an adaptive gated fusion mechanism dynamically integrates these views. Evaluated on the ADReSSo dataset, our model achieves 90.00% accuracy. Ablation results confirm that the PMI-based graph and heterogeneity-aware gating are essential for robust classification across diverse clinical populations. Our source code is publicly available at https://github.com/opeacc/AD.

Problem

• Alzheimer's Disease
• spontaneous speech
• clinical heterogeneity
• non-linear structural disruptions
• pathological language

Innovation

• Graph Attention Networks
• Pointwise Mutual Information
• Gated Fusion
• Alzheimer's Disease Detection
• Multi-View Graph Representation