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EEG microstate-based static and dynamic brain functional network differences in autism spectrum disorder children and
Jiannan Kang1, Xiaoke Yang1, Liang Zhang1
1College of Electronic & Information Engineering, Hebei University, Baoding, China.
Brain & Development
|August 21, 2025
Summary
Autism spectrum disorder (ASD) in children shows distinct brain network patterns compared to typically developing peers. These differences, identified using EEG microstates, enabled accurate ASD classification and were positively impacted by transcranial direct current stimulation (tDCS).
Area of Science:
- Neuroscience
- Brain Network Analysis
- Autism Spectrum Disorder Research
Background:
- Autism is characterized by abnormal brain network function.
- Understanding these network differences is crucial for diagnosis and intervention.
Purpose of the Study:
- To differentiate brain network characteristics between children with autism spectrum disorder (ASD) and typically developing (TD) children.
- To assess the impact of transcranial direct current stimulation (tDCS) on ASD brain networks.
Main Methods:
- Utilized EEG microstates to construct static and dynamic brain functional networks.
- Quantified network differences using fuzzy entropy and static functional connectivity.
- Employed a support vector machine (SVM) model for ASD classification.
- Evaluated tDCS effects on ASD brain networks.
Main Results:
- ASD children exhibited significantly lower static functional connectivity in microstate A and higher in microstate D compared to TD children.
- Dynamic functional connectivity was reduced across microstates A, B, C, and D in the ASD group.
- SVM classification achieved 96.33% accuracy.
- tDCS intervention showed a trend towards increased static and dynamic functional connectivity in ASD children.
Conclusions:
- Significant disparities exist in static and dynamic brain networks between children with ASD and TD individuals.
- The applied methods provide excellent classification accuracy for ASD.
- tDCS intervention demonstrates a potential to modulate brain network function in children with ASD.
