Disentangled attention graph neural network for Alzheimer's disease diagnosis

Abstract

Neurodegenerative disorders, notably Alzheimer's Disease type Dementia (ADD), are recognized for their imprint on brain connectivity. Recent investigations employing Graph Neural Networks (GNNs) have demonstrated considerable promise in diagnosing ADD. Among the various GNN architectures, attention-based GNNs have gained prominence due to their capacity to emphasize diagnostically significant alterations in neural connectivity while suppressing irrelevant ones. Nevertheless, a notable limitation observed in attention-based GNNs pertains to the homogeneity of attention coefficients across different attention heads, suggesting a tendency for the GNN to overlook spatially localized critical alterations at the subnetwork scale (mesoscale). In response to this challenge, we propose a novel Disentangled Attention GNN (DAGNN) model trained to discern attention coefficients across different heads. We show that DAGNN can generate uncorrelated latent representations across heads, potentially learning localized representations at mesoscale. We empirically show that these latent representations are superior to state-of-the-art GNN based representations in ADD diagnosis while providing insight into spatially localized changes in connectivity.