自闭症谱系障碍儿童静息状态下脑电微状态研究

doi:10.3969/j.issn.0258-8021.2021.06.02

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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.

Key words： resting state EEG autism spectrum disorder microstate atomize & agglomerate hierarchical clustering

Received: 22 January 2021

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R318

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	Articles by authors
	Zhang Suoliang
	Wan Lingyan
	Zhang Zhiming
	Kang Jiannan
	Li Xiaoli
	Pang Jiao

Cite this article:

Zhang Suoliang,Wan Lingyan,Zhang Zhiming, et al. Study on the Differences of Resting-State EEG Microstate in Children with Autism Spectrum Disorder[J]. Chinese Journal of Biomedical Engineering, 2021, 40(6): 653-661.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2021.06.02 OR http://cjbme.csbme.org/EN/Y2021/V40/I6/653

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