Evidence map›Paper›PMID 40440914›Full record

ArticleEBioMedicine2025

Paroxysmal cortical slowing linked to drug-resistant epilepsy.

Yonatan Serlin, Hamza Imtiaz, Tamir Avigdor, Anna Minarik, Sina Lash, Timothy Bardouille, Ben Whatley, Kristin M Ikeda, Dan Z Milikovsky, Sara K Inati and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in EBioMedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

14 authors.

Yonatan SerlinNeurophysiology of Epilepsy Unit, NINDS, National Institutes of Health, Bethesda, MD, USA; Department of Medical Neuroscience and the Brain Repair Center, Dalhousie University, Faculty of Medicine, Halifax, NS, Canada.
Hamza ImtiazDepartment of Medical Neuroscience and the Brain Repair Center, Dalhousie University, Faculty of Medicine, Halifax, NS, Canada.
Tamir AvigdorMontreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
Anna MinarikMontreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
Sina LashMontreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
Timothy BardouilleDepartment of Physics and Atmospheric Science, Dalhousie University, Halifax, NS, Canada.
Ben WhatleyDivision of Neurology, Dalhousie University, Halifax, NS, Canada.
Kristin M IkedaDivision of Neurology, Dalhousie University, Halifax, NS, Canada.
Dan Z MilikovskyDivision of Neurology, Department of Medicine, University Health Network, University of Toronto, Toronto, ON, Canada.
Sara K InatiNeurophysiology of Epilepsy Unit, NINDS, National Institutes of Health, Bethesda, MD, USA.
Theodor RüberDepartment of Neuroradiology, University Hospital Bonn, Bonn, Germany; Department of Epileptology, University Hospital Bonn, Bonn, Germany.
Rainer SurgesDepartment of Epileptology, University Hospital Bonn, Bonn, Germany.
Attila RáczDepartment of Epileptology, University Hospital Bonn, Bonn, Germany.
Alon FriedmanDepartment of Medical Neuroscience and the Brain Repair Center, Dalhousie University, Faculty of Medicine, Halifax, NS, Canada; Department of Brain and Cognitive Sciences, Ben-Gurion University of the Negev, Israel. Electronic address: alon.friedman@dal.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundParoxysmal slow wave events (PSWEs), defined as electroencephalography (EEG) segments where the median power frequency falls below 6 Hz for ≥5 s, have been shown to predict epilepsy in patients with a first seizure. We evaluated the prevalence and localisation of PSWEs in large independent EEG datasets, exploring their potential as biomarkers for drug-resistant epilepsy (DRE).

methodsAn exploratory analysis used 1064 participants from the Temple University EEG corpus, comparing patients with epilepsy (N = 903) with participants with seizure-mimics and normal EEGs (N = 161). Validation analysis used an independent cohort from Bonn University, comprising drug-responsive (N = 51) and patients with DRE (N = 44).

findingsIn the exploratory analysis, the proportion of time PSWEs were detected was longer in epilepsy compared with participants without epilepsy (P < 0.0001). Analyses of aetiology and EEG localisation revealed that PSWEs were most prolonged in patients with reported focal epilepsy (P = 0.004), particularly with temporal lobe involvement (P = 0.005). Patients with DRE had prolonged time in PSWEs (P = 0.005), corresponding with an increased risk of refractoriness (OR = 1.9; 95% CI 1.2-2.9). Validation analysis confirmed these findings, with prolonged PSWEs in DRE vs. drug-responsive patients (P < 0.0001, AUC = 0.829). Based on the cutoff established in the exploratory cohort, prolonged time in PSWEs in the validation cohort was associated with increased DRE risk (OR = 5.14, 95% CI 2.1-12.3). In patients with poor surgical outcomes (Engel IB-IV, N = 13), pre-surgical EEGs showed prolonged time in PSWEs compared with Engel IA (N = 24, P = 0.038).

interpretationAnalysis of 1159 EEGs from two independent cohorts demonstrated that PSWEs are more prevalent and prolonged in patients with focal epilepsy and may indicate a lack of therapeutic response.

fundingThe Canadian Institutes of Health Research (168164, 180636).

Indexed as

Cerebral CortexDrug Resistant EpilepsyAdolescentAdultBiomarkersElectroencephalographyFemaleHumansMaleMiddle AgedYoung AdultBiomarkersCortical slowingDiagnostic biomarkersDrug-resistant epilepsyElectroencephalographyOpen-source database

Identifiers

PMID40440914
PMCPMC12159515

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.