Kazi Anees, Mora Jocelyn, Fischl Bruce, Dalca Adrian V, Aganj Iman
ArXiv. 2023 Sep 20:arXiv:2305.02199v2.
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.
我们基于扩散磁共振图像得出的脑连接性来处理分类问题。我们提出了一种受图卷积网络（GCN）启发的机器学习模型，该模型以脑连接性输入图为基础，并通过具有多个头的并行GCN机制分别处理数据。所提出的网络设计简单，采用了不同的头，包括专注于边和节点的图卷积，从而全面地从输入数据中捕获表征。为了测试我们的模型从脑连接性数据中提取互补性和代表性特征的能力，我们选择了性别分类任务。这量化了连接组因性别而异的程度，这对于增进我们对两性健康和疾病的理解非常重要。我们展示了在两个公开可用数据集上的实验：PREVENT-AD（347名受试者）和OASIS3（771名受试者）。与我们测试的现有机器学习算法（包括经典方法以及（图和非图）深度学习）相比，所提出的模型表现出了最高的性能。我们对模型的每个组件进行了详细分析。
