Evidence map›Paper›PMID 37264615›Full record

ReviewJournal of child neurology2023

Electroencephalographic (EEG) Biomarkers in Genetic Neurodevelopmental Disorders.

Kimberly Goodspeed, Dallas Armstrong, Alison Dolce, Patricia Evans, Rana Said, Peter Tsai, Deepa Sirsi

Open access · hybridAbstract readReview
In one paragraph

Review in Journal of child neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
9.1field-weighted citation impact, top 2% of its field
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed, 29 citations in OpenAlex.

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  12. International journal of molecular sciences · 2025
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  16. Brian Intensify: An Adaptive Machine Learning Framework for Auditory EEG Stimulation and Cognitive Enhancement in FXS.2025 3rd International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings) · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Kimberly GoodspeedDepartment of Pediatrics, Division of Neurology, University of Texas Southwestern Medical Center, Dallas, TX, USA.ORCID 0000-0002-8823-2839
Dallas ArmstrongDepartment of Pediatrics, Division of Neurology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Alison DolceDepartment of Pediatrics, Division of Neurology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Patricia EvansDepartment of Pediatrics, Division of Neurology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Rana SaidDepartment of Pediatrics, Division of Neurology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Peter TsaiDepartment of Pediatrics, Division of Neurology, University of Texas Southwestern Medical Center, Dallas, TX, USA.ORCID 0000-0002-0729-9688
Deepa SirsiDepartment of Pediatrics, Division of Neurology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
The University of Texas Southwestern Medical Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Collectively, neurodevelopmental disorders are highly prevalent, but more than a third of neurodevelopmental disorders have an identifiable genetic etiology, each of which is individually rare. The genes associated with neurodevelopmental disorders are often involved in early brain development, neuronal signaling, or synaptic plasticity. Novel treatments for many genetic neurodevelopmental disorders are being developed, but disease-relevant clinical outcome assessments and biomarkers are limited. Electroencephalography (EEG) is a promising noninvasive potential biomarker of brain function. It has been used extensively in epileptic disorders, but its application in neurodevelopmental disorders needs further investigation. In this review, we explore the use of EEG in 3 of the most prevalent genetic neurodevelopmental disorders-Angelman syndrome, Rett syndrome, and fragile X syndrome. Quantitative analyses of EEGs, such as power spectral analysis or measures of connectivity, can quantify EEG signatures seen on qualitative review and potentially correlate with phenotypes. In both Angelman syndrome and Rett syndrome, increased delta power on spectral analysis has correlated with clinical markers of disease severity including developmental disability and seizure burden, whereas spectral power analysis on EEG in fragile X syndrome tends to demonstrate abnormalities in gamma power. Further studies are needed to establish reliable relationships between quantitative EEG biomarkers and clinical phenotypes in rare genetic neurodevelopmental disorders.

Indexed as

Angelman SyndromeFragile X SyndromeNeurodevelopmental DisordersRett SyndromeBiomarkersElectroencephalographyHumansBiomarkers

Identifiers

PMID37264615
PMCPMC10644693
OpenAlexW4379093634

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.