ReviewFrontiers in psychiatry2017
How Useful Is Electroencephalography in the Diagnosis of Autism Spectrum Disorders and the Delineation of Subtypes: A Systematic Review.
Review in Frontiers in psychiatry, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 citing papers in PubMed, 3 syntheses or guidelines pooled it, 68 citations in OpenAlex.
- Resting-state EEG power differences in autism spectrum disorder: a systematic review and meta-analysis.Translational psychiatry · 2023Pooled it
- Motor networks in children with autism spectrum disorder: a systematic review on EEG studies.Experimental brain research · 2022Pooled it
- EEG signatures of cognitive and social development of preschool children-a systematic review.PloS one · 2021Pooled it
- A randomized controlled trial into the effects of probiotics on electroencephalography in preschoolers with autism.Autism : the international journal of research and practice · 2023Trial
- Early Trajectories of Resting-State EEG power in autistic children: a longitudinal study across language profiles.Translational psychiatry · 2026Article
- Article
- Towards Multi-Brain Decoding in Autism: A Self-Supervised Learning Approach.Neuroinformatics · 2026Article
- A dynamical systems framework for precision psychiatry.NPJ digital medicine · 2025Review
- Leveraging AI-Driven Neuroimaging Biomarkers for Early Detection and Social Function Prediction in Autism Spectrum Disorders: A Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- Early neurophysiological markers of aberrant auditory processing associated with increased risk of autism spectrum disorder: A systematic review.Pediatric investigation · 2025Review
- Transcranial photobiomodulation for reducing symptoms of autism spectrum disorder and modulating brain electrophysiology in children aged 2-7: an open label study.Frontiers in child and adolescent psychiatry · 2025Article
- Where do neurodevelopmental conditions fit in transdiagnostic psychiatric frameworks? Incorporating a new neurodevelopmental spectrum.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2024Article
- Interest paradigm for early identification of autism spectrum disorder: an analysis from electroencephalography combined with eye tracking.Frontiers in neuroscience · 2024Article
- Classification of autism spectrum disorder using electroencephalography in Chinese children: a cross-sectional retrospective study.Frontiers in neuroscience · 2024Article
- EEG Features in Autism Spectrum Disorder: A Retrospective Analysis in a Cohort of Preschool Children.Brain sciences · 2023Article
- Energy-Efficient EEG-Based Scheme for Autism Spectrum Disorder Detection Using Wearable Sensors.Sensors (Basel, Switzerland) · 2023Article
- Systematic Review on EEG Analysis to Diagnose and Treat Autism by Evaluating Functional Connectivity and Spectral Power.Neuropsychiatric disease and treatment · 2023Review
- A biomarker discovery framework for childhood anxiety.Frontiers in psychiatry · 2023Article
- Article
- Can EEG Correlates Predict Treatment Efficacy in Children with Overlapping ASD and SLI Symptoms: A Case Report.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 3 countries.
Funding
Abstract
Autism spectrum disorders (ASD) are thought to be associated with abnormal neural connectivity. Presently, neural connectivity is a theoretical construct that cannot be easily measured. Research in network science and time series analysis suggests that neural network structure, a marker of neural activity, can be measured with electroencephalography (EEG). EEG can be quantified by different methods of analysis to potentially detect brain abnormalities. The aim of this review is to examine evidence for the utility of three methods of EEG signal analysis in the ASD diagnosis and subtype delineation. We conducted a review of literature in which 40 studies were identified and classified according to the principal method of EEG analysis in three categories: functional connectivity analysis, spectral power analysis, and information dynamics. All studies identified significant differences between ASD patients and non-ASD subjects. However, due to high heterogeneity in the results, generalizations could not be inferred and none of the methods alone are currently useful as a new diagnostic tool. The lack of studies prevented the analysis of these methods as tools for ASD subtypes delineation. These results confirm EEG abnormalities in ASD, but as yet not sufficient to help in the diagnosis. Future research with larger samples and more robust study designs could allow for higher sensitivity and consistency in characterizing ASD, paving the way for developing new means of diagnosis.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.