Abstract
Autism spectrum disorder (ASD) represents a challenge for neurophysiological characterization due to its clinical heterogeneity. In this context, resting-state electroencephalography (rsEEG) has emerged as a valuable tool for exploring functional patterns in the pediatric population. The aim of this review was to synthesize the available evidence on rsEEG findings in children and adolescents with ASD and to analyze the methodological approaches employed in recent literature. A PubMed search was conducted, restricted to human studies with full-text availability and focused on pediatric samples. Thirty-eight empirical investigations met these criteria. In addition, VOSviewer was used to perform a semantic co-occurrence analysis of titles and abstracts, which grouped the evidence into four main themes: neurofunctional development, alterations in brain rhythms, dynamics of functional connectivity, and computational algorithms applied to diagnosis. The results indicate atypical trajectories in the maturation of oscillatory activity and brain connectivity, decreased signal complexity, regional functional reorganization, and dissociations between global integration and local instability. Advances were also documented in the application of deep learning models, such as convolutional networks, long short-term memory networks, and graph-based networks, which have achieved high diagnostic accuracy using rsEEG data. In conclusion, the reviewed evidence supports rsEEG as a promising tool for the development of ASD biomarkers, highlighting that its integration with artificial intelligence optimizes the characterization of the atypical brain functional architecture in autism.
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