EEG microstate-based static and dynamic brain functional network differences in autism spectrum disorder children and tDCS interventional modulation.

Researchers used brain scans to study how autistic children's brains connect and communicate differently from other children. They found clear differences in brain activity patterns that could identify autism with high accuracy. The study also tested a brain stimulation treatment called tDCS, which showed promising results in improving brain connectivity patterns in autistic children.

This study examined brain network differences between autistic children and typically developing children using EEG microstate analysis, which measures brief patterns of brain activity during rest. Researchers found significant differences in both static (stable) and dynamic (changing) brain connectivity patterns. Autistic children showed reduced connectivity in microstate A and increased connectivity in microstate D compared to controls, with overall reduced dynamic connectivity across all microstates. These features achieved 96.33% accuracy in distinguishing autistic from typically developing children using machine learning.

The study also investigated transcranial direct current stimulation (tDCS) intervention, finding it produced positive changes in brain connectivity patterns in autistic children.

EEG microstate analysis may provide objective biomarkers for autism diagnosis and monitoring treatment response. tDCS shows promise as a non-invasive intervention but requires further validation with larger samples and behavioral assessments before clinical implementation.

Sample size not reported, limiting assessment of study power. Intervention effects described as 'trends' and 'tendencies' suggest modest changes. Unknown study design and control conditions for tDCS intervention. Lack of behavioral outcome measures to correlate with brain changes.

Autism has garnered significant attention due to its abnormal brain network function. 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. 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. 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.