The Role of Electroencephalography (EEG) in the Diagnosis and Subtyping of Autism Spectrum Disorder: A Review

Abstract

Autism Spectrum Disorder (ASD) is thought to be linked with atypical neural connections. Currently, neural connectivity is a theoretically structured construct that is not easily measurable. Research in network science and time-series analysis indicates that the configuration of neural networks serves as an indicator of neural activity, which may be assessed using electroencephalography (EEG). EEG offers various analysis techniques to potentially identify brain irregularities. This review aims to assess the efficacy of two EEG signal analysis approaches in diagnosing and categorizing ASD. Literature review categorized studies into functional connectivity analysis and spectral power analysis based on predominant EEG analysis methods. Most researches reported significant distinctions between ASD individuals and nonautistic individuals. While, the diverse outcomes preclude definitive conclusions, and presently, no single method emerges as a reliable diagnostic tool. Due to limited research, these methods cannot adequately delineate ASD subtypes. While confirming EEG abnormalities in ASD, current findings fall short of diagnostic utility. Future investigations with larger cohorts and robust methodologies may enhance the sensitivity and consistency of ASD characteristics, fostering the development of novel diagnostic modalities.