State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
College of Electronic & Information Engineering, Hebei University, Baoding, 071002, China.
Sci Rep. 2017 Nov 24;7(1):16253. doi: 10.1038/s41598-017-16440-z.
Alterations in brain connectivity have been extensively reported in autism spectrum disorder (ASD), while their effects on the topology of brain network are still unclear. This study investigated whether and how the brain networks in children with ASD were abnormally organized with resting state EEG. Temporal synchronization analysis was first applied to capture the aberrant brain connectivity. Then brain network topology was characterized by three graph analysis methods including the commonly-used weighted and binary graph, as well as minimum spanning tree (MST). Whole brain connectivity in ASD group was found to be significantly reduced in theta and alpha band compared to typically development children (TD). Weighted graph found significantly decreased path length together with marginally significantly decreased clustering coefficient in ASD in alpha band, indicating a loss of small-world architecture to a random network. Such abnormal network topology was also demonstrated in the binary graph. In MST analysis, children with ASD showed a significant lower leaf fractions with a decrease trend of tree hierarchy in the alpha band, suggesting a shift towards line-like decentralized organization in ASD. The altered brain network may offer an insight into the underlying pathology of ASD and possibly serve as a biomarker that may aid in diagnosis of ASD.
大脑连接的改变在自闭症谱系障碍(ASD)中被广泛报道,而它们对大脑网络拓扑结构的影响尚不清楚。本研究通过静息态 EEG 探讨了 ASD 儿童的大脑网络是否以及如何出现异常组织。首先应用时同步分析来捕捉异常的大脑连接。然后,通过三种图分析方法(包括常用的加权和二进制图以及最小生成树(MST))来描述大脑网络拓扑结构。与典型发育儿童(TD)相比,ASD 组在 theta 和 alpha 频段的全脑连接明显减少。在 alpha 频段,加权图发现路径长度显著降低,聚类系数略有显著降低,表明小世界结构向随机网络的丧失。这种异常的网络拓扑结构也在二进制图中得到了证明。在 MST 分析中,ASD 儿童在 alpha 频段的叶分数明显较低,且树层次结构呈下降趋势,表明 ASD 中存在线性分散的组织转变。改变的大脑网络可能为 ASD 的潜在病理学提供了一个见解,并可能作为一种生物标志物,有助于 ASD 的诊断。