Brain & development · 2025
EEG microstate-based static and dynamic brain functional network differences in autism spectrum disorder children and tDCS interventional modulation.
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Kang Jiannan, Yang Xiaoke, Zhang Liang, Li Xiaoli, Zheng Shukai, Tian Xiaoyan
Аннотация
BACKGROUND: Autism has garnered significant attention due to its abnormal brain network function.
METHODS: EEG microstates are brief, stable patterns of brain activity during rest, lasting 80-120 milliseconds before rapidly transitioning to new configurations. A static brain functional network was constructed based on microstates, and the static brain functional network was further quantified using fuzzy entropy to build a dynamic brain functional network. The techniques thoroughly assessed how children with autism spectrum disorder (ASD) and typically developing (TD) brain networks differed from two angles: microstate static functional connectivity and dynamic temporal variability. These features were used in a support vector machine classification model to distinguish ASD children. Additionally, the impact of transcranial direct current stimulation (tDCS) on the brain functional network of ASD children was also assessed using this approach.
RESULTS: The static functional connectivity of microstate A in ASD children was significantly lower than that of TD children, while the static functional connectivity of microstate D was significantly higher in the ASD group. The dynamic functional connectivity of microstates A, B, C, and D in the ASD group was significantly reduced across the whole brain. The support vector machine (SVM) classification accuracy based on these features was 96.33 %. Furthermore, after tDCS intervention, ASD children showed a trend of increased static functional connectivity in microstates A and C, as well as a tendency for increased dynamic functional connectivity in microstates A, B, and D.
CONCLUSION: A notable disparity was observed between children diagnosed with ASD and TD regarding their static and dynamic brain networks. The excellent classification results were achieved. Furthermore, it was discovered that the tDCS intervention altered the children with ASD's static and dynamic brain networks.
METHODS: EEG microstates are brief, stable patterns of brain activity during rest, lasting 80-120 milliseconds before rapidly transitioning to new configurations. A static brain functional network was constructed based on microstates, and the static brain functional network was further quantified using fuzzy entropy to build a dynamic brain functional network. The techniques thoroughly assessed how children with autism spectrum disorder (ASD) and typically developing (TD) brain networks differed from two angles: microstate static functional connectivity and dynamic temporal variability. These features were used in a support vector machine classification model to distinguish ASD children. Additionally, the impact of transcranial direct current stimulation (tDCS) on the brain functional network of ASD children was also assessed using this approach.
RESULTS: The static functional connectivity of microstate A in ASD children was significantly lower than that of TD children, while the static functional connectivity of microstate D was significantly higher in the ASD group. The dynamic functional connectivity of microstates A, B, C, and D in the ASD group was significantly reduced across the whole brain. The support vector machine (SVM) classification accuracy based on these features was 96.33 %. Furthermore, after tDCS intervention, ASD children showed a trend of increased static functional connectivity in microstates A and C, as well as a tendency for increased dynamic functional connectivity in microstates A, B, and D.
CONCLUSION: A notable disparity was observed between children diagnosed with ASD and TD regarding their static and dynamic brain networks. The excellent classification results were achieved. Furthermore, it was discovered that the tDCS intervention altered the children with ASD's static and dynamic brain networks.