ArticleScientific reports2022
Intellectually able adults with autism spectrum disorder show typical resting-state EEG activity.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Aperiodic Neural Activity in Clinical Investigations.The European journal of neuroscience · 2025Pooled it
- Cannabidiol blood metabolite levels after cannabidiol treatment are associated with broadband EEG changes and improvements in visuomotor and non-verbal cognitive abilities in boys with autism requiring higher levels of support.Translational psychiatry · 2026Trial
- Task-Related Aperiodic EEG (1/f) Activity in Autism.Autism research : official journal of the International Society for Autism Research · 2026Article
- Qualitative EEG abnormalities in ASD reflect inhibition-dominated brain dynamics.Scientific reports · 2026Article
- Functional Analyses in Patient-Derived Neurons Establish Pathogenicity forHuman mutation · 2026Article
- Cannabidiol blood metabolite levels after cannabidiol treatment are associated with broadband EEG changes and improvements in visuomotor and non-verbal cognitive abilities in boys with autism requiring higher levels of support.medRxiv : the preprint server for health sciences · 2025Article
- Leveraging AI-Driven Neuroimaging Biomarkers for Early Detection and Social Function Prediction in Autism Spectrum Disorders: A Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- Temporal dynamics of neural activity in autism: dynamical systems perspective on sensitivity, neural learning, and social interactions.Frontiers in neuroscience · 2025Article
- Dimensionality reduction of quantitative EEG and clinical profiles uncover associations with monogenic neurodevelopmental phenotypes in SNAREopathies.Frontiers in neuroscience · 2025Article
- Functional excitation-inhibition ratio indicates near-critical oscillations across frequencies.Imaging neuroscience (Cambridge, Mass.) · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
There is broad interest in discovering quantifiable physiological biomarkers for psychiatric disorders to aid diagnostic assessment. However, finding biomarkers for autism spectrum disorder (ASD) has proven particularly difficult, partly due to high heterogeneity. Here, we recorded five minutes eyes-closed rest electroencephalography (EEG) from 186 adults (51% with ASD and 49% without ASD) and investigated the potential of EEG biomarkers to classify ASD using three conventional machine learning models with two-layer cross-validation. Comprehensive characterization of spectral, temporal and spatial dimensions of source-modelled EEG resulted in 3443 biomarkers per recording. We found no significant group-mean or group-variance differences for any of the EEG features. Interestingly, we obtained validation accuracies above 80%; however, the best machine learning model merely distinguished ASD from the non-autistic comparison group with a mean balanced test accuracy of 56% on the entirely unseen test set. The large drop in model performance between validation and testing, stress the importance of rigorous model evaluation, and further highlights the high heterogeneity in ASD. Overall, the lack of significant differences and weak classification indicates that, at the group level, intellectually able adults with ASD show remarkably typical resting-state EEG.
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