Graph neural network (GNN) models are increasingly being used for the classification of electroencephalography (EEG) data. However, GNN-based diagnosis of neurological disorders, such as Alzheimer's disease (AD), remains a relatively unexplored area of research. Previous studies have relied on functional connectivity methods to infer brain graph structures and used simple GNN architectures for the diagnosis of AD. In this work, we propose a novel adaptive gated graph convolutional network (AGGCN) that can provide explainable predictions. AGGCN adaptively learns graph structures by combining convolution-based node feature enhancement with a well-known correlation-based measure of functional connectivity. Furthermore, the gated graph convolution can dynamically weigh the contribution of various spatial scales. The proposed model achieves high accuracy in both eyes-closed and eyes-open conditions, indicating the stability of learned representations. Finally, we demonstrate that the proposed AGGCN model generates consistent explanations of its predictions that might be relevant for further study of AD-related alterations of brain networks.
翻译:图神经网络(GNN)模型越来越多地被用于分类脑电图(EEG)数据。然而,基于 GNN 的神经障碍,如阿尔茨海默病(AD)的诊断仍是一个相对未开发的研究领域。以往的研究依赖于功能性连接方法来推断脑图结构,并使用简单的 GNN 架构诊断 AD。在本文中,我们提出了一种新的自适应门控图卷积网络(AGGCN),可以提供可解释的预测。AGGCN 通过将基于卷积的节点特征增强与功能性连接的著名相关性测量相结合,自适应地学习图结构。此外,门控图卷积可以动态地权衡各种空间尺度的贡献。所提出的模型在闭眼和睁眼状态下均达到了较高的准确性,表明其所学习的表示的稳定性。最后,我们证明了所提出的 AGGCN 模型能够生成一致的预测解释,这可能与进一步研究 AD 相关的脑网络的变化有关。