We tackle classification based on brain connectivity derived from diffusion magnetic resonance images. We propose a machine-learning model inspired by graph convolutional networks (GCNs), which takes a brain connectivity input graph and processes the data separately through a parallel GCN mechanism with multiple heads. The proposed network is a simple design that employs different heads involving graph convolutions focused on edges and nodes, capturing representations from the input data thoroughly. To test the ability of our model to extract complementary and representative features from brain connectivity data, we chose the task of sex classification. This quantifies the degree to which the connectome varies depending on the sex, which is important for improving our understanding of health and disease in both sexes. We show experiments on two publicly available datasets: PREVENT-AD (347 subjects) and OASIS3 (771 subjects). The proposed model demonstrates the highest performance compared to the existing machine-learning algorithms we tested, including classical methods and (graph and non-graph) deep learning. We provide a detailed analysis of each component of our model.

翻译：我们解决基于扩散磁共振图像导出的脑连接分类问题。我们提出一种受图卷积网络（GCNs）启发的机器学习模型，该模型以脑连接输入图为对象，通过并行多头部GCN机制分别处理数据。所提出的网络设计简洁，采用包含基于边和节点的图卷积的不同头部，从输入数据中全面捕获表示。为测试模型从脑连接数据中提取互补和代表性特征的能力，我们选择了性别分类任务。这量化了连接组随性别变化的程度，对于增进我们理解两性健康与疾病至关重要。我们在两个公开数据集上进行了实验：PREVENT-AD（347名受试者）和OASIS3（771名受试者）。与现有机器学习算法（包括经典方法及（图与非图）深度学习）相比，所提模型展现了最高性能。我们提供了模型各组件的详细分析。