摘要利用微状态分析方法,在静息状态下的脑电图(EEG)尺度上探究自闭症谱系障碍(ASD)儿童与正常儿童(TD)在脑机制上的差异。根据Cartool中的准则和不同微状态类别的数目对于被试者EEG数据的解释程度,确定微状态类别的数目为4;使用原子化与凝聚层次聚类算法,分割出个人水平和组水平上的微状态类别,分别标记为微状态A、B、C和D。然后根据这4类微状态的地形图和EEG数据各时间点的GEV相关性,将数据拟合回EEG数据,最终得到微状态时间序列,提取时域上的参数特征,比较ASD组和TD组的差异。选取的时间参数为平均持续时间、发生频率、时间覆盖率和转移概率,并通过计算马尔可夫模型的方法探究微状态序列的独立性。结果表明,在ASD组vs TD组中表现有统计差异(P<0.05)的微状态时间参数有:持续时间 (A:0.110±0.013 vs 0.180±0.048,C:0.140±0.024 vs 0.220±0.067,D:0.130±0.050 vs 0.190±0.037,单位:s)、时间覆盖率 (A:22.0±5.4 vs 27.0±7.2,B:27.0±4.7 vs 18.0±5.5,单位:%)、发生频率 (A:1.93±0.52 vs 1.55±0.22,B:2.08±0.46 vs 1.39±0.32,C:2.10±0.49 vs 1.47±0.30,D:1.78±0.19 vs 1.27±0.27,单位:次/s),且卡方检验不支持微状态类别之间在时间序列上是独立的零假设(P<0.01),提示微状态类别之间存在依赖性以及信息共享性。本研究为自闭症的评估提供了客观指标和科学依据。
Abstract：This study aimed to use the microstate analysis method to investigate the differences in brain mechanism between children with autism spectrum disorder (ASD) and healthy children on the electroencephalography (EEG) scale in the resting state. According to the guidelines in Cartool and the degree of interpretation of the participants' EEG data by the number of different microstates, the number of microstates was determined to be 4. Using atomize & agglomerate hierarchical clustering algorithms, the microstates at the individual level and group level were segmented and labeled as microstate Class A, microstate Class B, microstate Class C, and microstate Class D. Next, fit the data back to the EEG data according to the topographic maps of the four classes of microstates and the level of the GEV correlation of the EEG data at each time point, and finally obtained the microstate time series, and extracted the characteristics in the time domain to compare the differences between the ASD group and the TD group. The time parameters selected in this study included average duration, frequency of occurrence, time coverage and transition probability. And the method of calculating the Markov model explored the independence of the microstates sequence. The microstate time parameters that showed there were differences (P<0.05) in the ASD group vs. the TD group for the duration (A: 0.110±0.013 vs 0.180±0.048, C: 0.140±0.024 vs 0.220±0.067, D: 0.130±0.050 vs 0.190±0.037, unit: s), time coverage (A: 22.0±5.4 vs 27.0±7.2, B: 27.0±4.7 vs 18.0±5.5, unit: %), occurrence(A: 1.93±0.52 vs 1.55±0.22,B: 2.08±0.46 vs 1.39±0.32,C: 2.10±0.49 vs 1.47±0.30,D: 1.78±0.19 vs 1.27±0.27, unit: times/s). Moreover, chi-square test did not supported the hypothesis that the microstates was independent zero (P<0.01), suggesting the dependence and information sharing between the microstates. The results of this study provided objective indicators and scientific basis for the assessment of autism.
张锁良, 万灵燕, 张志明, 康健楠, 李小俚, 庞姣. 自闭症谱系障碍儿童静息状态下脑电微状态研究[J]. 中国生物医学工程学报, 2021, 40(6): 653-661.
Zhang Suoliang, Wan Lingyan, Zhang Zhiming, Kang Jiannan, Li Xiaoli, Pang Jiao. Study on the Differences of Resting-State EEG Microstate in Children with Autism Spectrum Disorder. Chinese Journal of Biomedical Engineering, 2021, 40(6): 653-661.
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