Evidence map›Paper›PMID 39817338›Full record

ArticleAnnals of neurology2025

Diagnosing Epilepsy with Normal Interictal EEG Using Dynamic Network Models.

Patrick Myers, Kristin M Gunnarsdottir, Adam Li, Vlad Razskazovskiy, Jeff Craley, Alana Chandler, Dale Wyeth, Edmund Wyeth, Kareem A Zaghloul, Sara K Inati and 9 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Annals of neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
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

11 citing papers in PubMed.

  1. Article
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  3. Review
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  6. Review
  7. Mapping epileptogenic brain using a unified spatial-temporal-spectral source imaging framework.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
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  9. Review
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  11. Article
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

19 authors.

Patrick MyersDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.ORCID 0000-0001-6645-1478
Kristin M GunnarsdottirDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Adam LiDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Vlad RazskazovskiyNeurosurgery and Epilepsy Center, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
Jeff CraleyDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Alana ChandlerDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Dale WyethDivision of Epilepsy, Thomas Jefferson University Hospital, Philadelphia, PA, USA.
Edmund WyethDivision of Epilepsy, Thomas Jefferson University Hospital, Philadelphia, PA, USA.
Kareem A ZaghloulSurgical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.
Sara K InatiSurgical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.
Jennifer L HoppDepartment of Neurology, University of Maryland Medical Center, Baltimore, MD, USA.
Babitha HaridasDepartment of Neurology, Comprehensive Epilepsy Center, Johns Hopkins University, Baltimore, MD, USA.
Jorge Gonzalez-MartinezNeurosurgery and Epilepsy Center, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
Anto BagícNeurosurgery and Epilepsy Center, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
Joon-Yi KangDepartment of Neurology, Comprehensive Epilepsy Center, Johns Hopkins University, Baltimore, MD, USA.
Michael R SperlingDivision of Epilepsy, Thomas Jefferson University Hospital, Philadelphia, PA, USA.
Niravkumar BarotDepartment of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA.
Sridevi V SarmaDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Khalil S HusariDepartment of Neurology, Comprehensive Epilepsy Center, Johns Hopkins University, Baltimore, MD, USA.

Funding

EEG Biomarkers Derived from Dynamical Network Models Enable Rapid Paths to Accurate Diagnosis and Effective Treatment of EpilepsyR35NS132228 · NINDS · JOHNS HOPKINS UNIVERSITY · PI Sridevi V. Sarma · 2023 to 2026
$2.6M
Establishing novel properties of dynamic systems models to identify epileptogenic networks in patients with drug resistant epilepsyR01NS122927 · NINDS · JOHNS HOPKINS UNIVERSITY · PI Jorge Alvaro Gonzalez-Martinez, Sridevi V. Sarma · 2022 to 2026
$1.3M
NINDS NIH HHS R01 NS122927NINDS NIH HHS R35 NS132228NINDS NIH HHS R35NS132228The Louis B. Thalheimer Fund for Translational Research
6 · The paper itself

Abstract

objectiveWhereas a scalp electroencephalogram (EEG) is important for diagnosing epilepsy, a single routine EEG is limited in its diagnostic value. Only a small percentage of routine EEGs show interictal epileptiform discharges (IEDs) and overall misdiagnosis rates of epilepsy are 20% to 30%. We aim to demonstrate how network properties in EEG recordings can be used to improve the speed and accuracy differentiating epilepsy from mimics, such as functional seizures - even in the absence of IEDs.

methodsIn this multicenter study, we analyzed routine scalp EEGs from 218 patients with suspected epilepsy and normal initial EEGs. The patients' diagnoses were later confirmed based on an epilepsy monitoring unit (EMU) admission. About 46% ultimately being diagnosed with epilepsy and 54% with non-epileptic conditions. A logistic regression model was trained using spectral and network-derived EEG features to differentiate between epilepsy and non-epilepsy. Of the 218 patients, 90% were used for training and 10% were held out for testing. Within the training set, 10-fold cross validation was performed. The resulting tool was named "EpiScalp."

resultsEpiScalp achieved an area under the curve (AUC) of 0.940, an accuracy of 0.904, a sensitivity of 0.835, and a specificity of 0.963 in classifying patients as having epilepsy or not.

interpretationEpiScalp provides an accurate diagnostic aid from a single initial EEG recording, even in more challenging epilepsy cases with normal initial EEGs. This may represent a paradigm shift in epilepsy diagnosis by deriving an objective measure of epilepsy likelihood from previously uninformative EEGs. ANN NEUROL 2025;97:907-918.

Indexed as

ElectroencephalographyEpilepsyNeural Networks, ComputerAdolescentAdultAgedChildFemaleHumansMaleMiddle AgedYoung Adult

Identifiers

PMID39817338
PMCPMC12010061

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.