Yang Sihong, Jin Dezhi, Liu Jun, He Ye
School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Brain Sci. 2022 Jul 5;12(7):883. doi: 10.3390/brainsci12070883.
Accumulated studies have determined the changes in functional connectivity in autism spectrum disorder (ASD) and spurred the application of machine learning for classifying ASD. Graph Neural Network provides a new method for network analysis in brain disorders to identify the underlying network features associated with functional deficits. Here, we proposed an improved model of Graph Isomorphism Network (GIN) that implements the Weisfeiler-Lehman (WL) graph isomorphism test to learn the graph features while taking into account the importance of each node in the classification to improve the interpretability of the algorithm. We applied the proposed method on multisite datasets of resting-state functional connectome from Autism Brain Imaging Data Exchange (ABIDE) after stringent quality control. The proposed method outperformed other commonly used classification methods on five different evaluation metrics. We also identified salient ROIs in visual and frontoparietal control networks, which could provide potential neuroimaging biomarkers for ASD identification.
大量研究已确定了自闭症谱系障碍（ASD）中功能连接性的变化，并推动了机器学习在ASD分类中的应用。图神经网络为脑部疾病的网络分析提供了一种新方法，以识别与功能缺陷相关的潜在网络特征。在此，我们提出了一种改进的图同构网络（GIN）模型，该模型实施了魏斯费勒-莱曼（WL）图同构测试，以学习图特征，同时考虑到每个节点在分类中的重要性，以提高算法的可解释性。在经过严格的质量控制后，我们将所提出的方法应用于来自自闭症脑成像数据交换（ABIDE）的静息态功能连接组多站点数据集。在所提出的方法在五个不同的评估指标上优于其他常用的分类方法。我们还在视觉和额顶叶控制网络中识别出了显著的感兴趣区域（ROI），这可为ASD识别提供潜在的神经影像生物标志物。
