Autism
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by impairments in social communication and repetitive behaviors, encompassing a broad phenotypic spectrum.
Genetic mutations and environmental factors (e.g., maternal immune activation and neuroinflammation during pregnancy) play significant roles in the development of ASD. Overactivation of microglial cells in ASD leads to inadequate synaptic pruning (insufficient synaptic elimination), resulting in the formation of excessively connected neural networks.
EEG analyses reveal that individuals with ASD exhibit a distinctive "U-shaped" spectral curve, characterized by increased power in low-frequency waves (delta and theta) and reduced power in mid-frequency waves (alpha). Specifically, increased theta power in the frontal and temporal regions, widespread alpha band reduction, and excessive beta and gamma activity in the occipital and parietal regions are key EEG biomarkers of ASD. These biomarkers support the notion that individuals with ASD exhibit excessive local brain connectivity while showing deficits in long-range connectivity.
Furthermore, EEG signal complexity and wave amplitude variability have been found to differ in individuals with ASD compared to typically developing individuals. Machine learning-based EEG analyses enable the development of models capable of diagnosing ASD with high accuracy, offering new approaches for personalized interventions.
AI-driven EEG analyses play a crucial role in understanding the underlying neurobiological mechanisms of ASD, contributing to early diagnosis and the development of individualized therapeutic strategies.