Autism spectrum disorder (ASD) is a neuro-developmental disorder that affects the social abilities of patients. Studies have shown that a small number of abnormal functional connections (FCs) exist in the cerebral hemisphere of ASD patients. The identification of these abnormal FCs provides a biological ground for the diagnosis of ASD. In this paper, we propose a combined deep feature selection (DFS) and graph convolutional network method to classify ASD. Firstly, in the DFS process, a sparse one-to-one layer is added between the input and the first hidden layer of a multilayer perceptron, thus each functional connection (FC) feature can be weighted and a subset of FC features can be selected accordingly. Then based on the selected FCs and the phenotypic information of subjects, a graph convolutional network is constructed to classify ASD and typically developed controls. Finally, we test our proposed method on the ABIDE database and compare it with some other methods in the literature. Experimental results indicate that the DFS can effectively select critical FC features for classification according to the weights of input FC features. With DFS, the performance of GCN classifier can be improved dramatically. The proposed method achieves state-of-the-art performance with an accuracy of 79.5% and an area under the receiver operating characteristic curve (AUC) of 0.85 on the preprocessed ABIDE dataset; it is superior to the other methods. Further studies on the top-ranked thirty FCs obtained by DFS show that these FCs are widespread over the cerebral hemisphere, and the ASD group appears a significantly higher number of weak connections compared to the typically developed group.

自闭症谱系障碍 (ASD) 是一种影响患者社交能力的神经发育障碍。研究表明，自闭症患者的大脑半球存在少量异常功能连接（FCs）。这些异常 FC 的鉴定为 ASD 的诊断提供了生物学基础。在本文中，我们提出了一种结合深度特征选择 (DFS) 和图卷积网络的方法来对 ASD 进行分类。首先，在 DFS 过程中，在多层感知器的输入和第一个隐藏层之间添加一个稀疏的一对一层，从而可以对每个功能连接（FC）特征进行加权并选择 FC 特征的子集因此。然后根据选择的FC和受试者的表型信息，构建了一个图形卷积网络来对 ASD 和通常开发的控件进行分类。最后，我们在 ABIDE 数据库上测试了我们提出的方法，并将其与文献中的其他一些方法进行了比较。实验结果表明，DFS 可以根据输入 FC 特征的权重有效地选择关键 FC 特征进行分类。使用 DFS，可以显着提高 GCN 分类器的性能。所提出的方法在预处理的 ABIDE 数据集上实现了最先进的性能，准确度为 79.5%，接受者操作特征曲线 (AUC) 下的面积为 0.85；它优于其他方法。对 DFS 获得的排名靠前的 30 个 FC 的进一步研究表明，这些 FC 广泛分布于大脑半球，