While graph convolution-based methods have become the de-facto standard for graph representation learning, their applications to disease prediction tasks remain quite limited, particularly in the classification of neurodevelopmental and neurodegenerative brain disorders. In this paper, we introduce an aggregator normalization graph convolutional network by leveraging aggregation in graph sampling, as well as skip connections and identity mapping. The proposed model learns discriminative graph node representations by incorporating both imaging and non-imaging features into the graph nodes and edges, respectively, with the aim of augmenting predictive capabilities and providing a holistic perspective on the underlying mechanisms of brain disorders. Skip connections enable the direct flow of information from the input features to later layers of the network, while identity mapping helps maintain the structural information of the graph during feature learning. We benchmark our model against several recent baseline methods on two large datasets, Autism Brain Imaging Data Exchange (ABIDE) and Alzheimer’s Disease Neuroimaging Initiative (ADNI), for the prediction of autism spectrum disorder and Alzheimer’s disease, respectively. Experimental results demonstrate the competitive performance of our approach in comparison with recent baselines in terms of several evaluation metrics, achieving relative improvements of 50% and 13.56% in classification accuracy over graph convolutional networks (GCNs) on ABIDE and ADNI, respectively. Our study involved the development of a graph convolutional aggregation model, which aimed to predict the status of subjects in a population graph. We learned discriminative node representations by utilizing imaging and non-imaging features associated with the graph nodes and edges. Our model outperformed existing graph convolutional-based methods for disease prediction on two large benchmark datasets, as shown through extensive experiments. We achieved significant relative improvements in classification accuracy over GCN and other strong baselines.

虽然基于图卷积的方法已经成为图表示学习的事实上的标准，但它们在疾病预测任务中的应用仍然相当有限，特别是在神经发育和神经退行性脑疾病的分类中。在本文中，我们通过利用图采样中的聚合以及跳过连接和恒等映射来引入聚合器归一化图卷积网络。所提出的模型通过将成像和非成像特征分别合并到图节点和边缘中来学习判别性图节点表示，目的是增强预测能力并提供关于大脑疾病潜在机制的整体视角。跳过连接使信息能够从输入特征直接流向网络的后续层，而恒等映射有助于在特征学习期间维护图的结构信息。我们将我们的模型与两个大型数据集（自闭症脑成像数据交换（ABIDE）和阿尔茨海默病神经影像计划（ADNI））上的几种最新基线方法进行基准测试，分别用于预测自闭症谱系障碍和阿尔茨海默病。实验结果表明，与最近的基线相比，我们的方法在多个评估指标方面具有竞争性能，在 ABIDE 和 ADNI 上的图卷积网络 (GCN) 上的分类准确率分别提高了 50% 和 13.56%。我们的研究涉及图卷积聚合模型的开发，旨在预测人口图中受试者的状态。我们通过利用与图节点和边相关的成像和非成像特征来学习判别性节点表示。正如广泛的实验所示，我们的模型在两个大型基准数据集上优于现有的基于图卷积的疾病预测方法。与 GCN 和其他强基线相比，我们在分类准确性方面取得了显着的相对改进。