ArticleEpilepsia open2026
Scalp fast oscillations detected by high-density EEG as a noninvasive predictor of surgical outcome in temporal lobe epilepsy with hippocampal sclerosis.
Article in Epilepsia open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
objectiveTo evaluate whether scalp oscillatory patterns-particularly scalp fast oscillations (SFOs) (>30 Hz) detected using 256-channel high-density EEG (HD-EEG)-are associated with and can predict surgical outcomes in patients with drug-resistant temporal lobe epilepsy with hippocampal sclerosis (TLE-HS) undergoing anterior temporal lobectomy (ATL).
methodThis prospective cohort study included 47 patients with drug-resistant TLE-HS who underwent HD-EEG and subsequent ATL with at least 24 months of postoperative follow-up. A total of 70 averaged interictal epileptiform discharges (IEDs) were analyzed using time-frequency methods. Scalp oscillation frequencies across anterior temporal, posterior temporal, and extratemporal regions were calculated, and cluster analysis was performed to identify significant patterns. Scalp fast oscillations (SFOs; >30 Hz) were separately analyzed. Associations with surgical outcomes were assessed using generalized linear mixed models, and diagnostic performance was evaluated.
resultsA total of 70 averaged IEDs from 47 patients were analyzed. Patients with seizure recurrence exhibited higher median scalp oscillation frequencies across anterior temporal (34.26 Hz), posterior temporal (29.47 Hz), and extratemporal regions (25.25 Hz) compared to seizure-free patients (12.50, 7.56, and 9.17 Hz, respectively). Among 22 IEDs with SFOs (>30 Hz), cluster-based analysis identified a specific frequency pattern significantly associated with surgical outcome (p = 0.031). This SFO-based pattern independently predicted seizure recurrence (odds ratio = 12.60, 95% CI: 1.19-133.89, p = 0.036), with a sensitivity of 87.5%, specificity of 64.3%, and an area under the receiver operating characteristic curve of 0.759. SIGNIFICANCE: This study demonstrates that specific scalp oscillatory patterns, particularly SFOs detected via 256-channel HD-EEG, are predictive of surgical outcomes in TLE-HS. These findings suggest that scalp oscillation analysis may serve as a valuable, noninvasive biomarker in the presurgical evaluation of epilepsy and could aid in delineating the extent of the epileptogenic network. PLAIN LANGUAGE SUMMARY: In this study, we used advanced brainwave recordings (called high-density EEG) to examine electrical patterns in the brains of people with epilepsy. We found that certain fast brainwave signals were linked to whether surgery could stop seizures. These signals may help doctors better plan epilepsy surgery in the future.
Indexed as
Identifiers
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.