Evidence map›Paper›PMID 40650866›Full record

ArticleEpilepsia2025

Self-supervised data-driven approach defines pathological high-frequency oscillations in epilepsy.

Yipeng Zhang, Atsuro Daida, Lawrence Liu, Naoto Kuroda, Yuanyi Ding, Shingo Oana, Sotaro Kanai, Tonmoy Monsoor, Chenda Duan, Shaun A Hussain and 10 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Epilepsia, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Internal and external validation of comprehensive high-frequency activity biomarkers for epilepsy surgery.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2026
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

20 authors.

Yipeng ZhangDepartment of Electrical and Computer Engineering, University of California, Los Angeles (UCLA), Los Angeles, California, USA.ORCID https://orcid.org/0000-0003-2869-4692
Atsuro DaidaDivision of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
Lawrence LiuDepartment of Electrical and Computer Engineering, University of California, Los Angeles (UCLA), Los Angeles, California, USA.
Naoto KurodaDepartment of Pediatrics and Neurology, Children's Hospital of Michigan, Wayne State University School of Medicine, Detroit, Michigan, USA.ORCID https://orcid.org/0000-0003-1977-4893
Yuanyi DingDepartment of Electrical and Computer Engineering, University of California, Los Angeles (UCLA), Los Angeles, California, USA.
Shingo OanaDivision of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
Sotaro KanaiDivision of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
Tonmoy MonsoorDepartment of Electrical and Computer Engineering, University of California, Los Angeles (UCLA), Los Angeles, California, USA.
Chenda DuanDepartment of Electrical and Computer Engineering, University of California, Los Angeles (UCLA), Los Angeles, California, USA.
Shaun A HussainDivision of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.ORCID https://orcid.org/0000-0001-6947-8852
Joe X QiaoDivision of Neuroradiology, Department of Radiology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, California, USA.
Noriko SalamonDivision of Neuroradiology, Department of Radiology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, California, USA.
Aria FallahDepartment of Neurosurgery, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, California, USA.ORCID https://orcid.org/0000-0002-9703-0964
Myung Shin SimDepartment of Medicine, Statistics Core, University of California, Los Angeles, Los Angeles, California, USA.
Raman SankarDivision of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
Richard J StabaDepartment of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, California, USA.
Jerome EngelDepartment of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, California, USA.
Eishi AsanoDepartment of Pediatrics and Neurology, Children's Hospital of Michigan, Wayne State University School of Medicine, Detroit, Michigan, USA.ORCID https://orcid.org/0000-0001-8391-4067
Vwani RoychowdhuryDepartment of Electrical and Computer Engineering, University of California, Los Angeles (UCLA), Los Angeles, California, USA.ORCID https://orcid.org/0000-0003-0832-6489
Hiroki NariaiDivision of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.ORCID https://orcid.org/0000-0002-8318-2924

Funding

UCLA Clinical Translational Science InstituteUL1TR001881 · NCATS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ARLEEN F. BROWN, ARASH NAEIM · 2016 to 2026
$118.1M
The Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) Public Engagement CoreU54NS100064 · NINDS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI ENGEL, JEROME NONE, GALANOPOULOU, ARISTEA S · 2017 to 2021
$21.7M
Translational Platform for Epilepsy Therapy and Biomarker DiscoveryR01NS127524 · NINDS · ALBERT EINSTEIN COLLEGE OF MEDICINE · PI Denes V. Agoston, LISA D COLES · 2022 to 2026
$11.0M
IN VIVO STUDIES OF THE EPILEPTIC HIPPOCAMPUSR01NS033310 · NINDS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI JEROME NONE ENGEL, Richard Staba · 1994 to 2026
$7.5M
Functional brain mapping in pediatric neurosurgeryR01NS064033 · NINDS · WAYNE STATE UNIVERSITY · PI EISHI ASANO · 2009 to 2026
$5.7M
Defining the Epileptogenic Network and Identifying Which Components Generate SeizuresR01NS106957 · NINDS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Richard Staba · 2018 to 2026
$3.4M
Refining neurophysiological biomarkers of epilepsy using deep learning to guide pediatric epilepsy surgeryK23NS128318 · NINDS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Hiroki Nariai · 2023 to 2026
$968k
Children's Discovery and Innovation Institute, University of California, Los Angeles CDI-SEED-010124NCATS NIH HHS UL1 TR001881NCATS NIH HHS UL1TR001881NINDS NIH HHS K23 NS128318NINDS NIH HHS K23NS128318NINDS NIH HHS R01 NS033310NINDS NIH HHS R01NS033310NINDS NIH HHS R01 NS064033NINDS NIH HHS R01NS064033NINDS NIH HHS R01 NS106957NINDS NIH HHS R01NS106957NINDS NIH HHS R01 NS127524NINDS NIH HHS R01NS127524NINDS NIH HHS U54 NS100064NINDS NIH HHS U54NS100064
6 · The paper itself

Abstract

objectiveInterictal high-frequency oscillations (HFOs) are a promising neurophysiological biomarker of the epileptogenic zone (EZ). However, objective criteria for distinguishing pathological from physiological HFOs remain elusive, hindering clinical application. We investigated whether the distinct mechanisms underlying pathological and physiological HFOs are encapsulated in their signal morphology in intracranial electroencephalographic (iEEG) recordings and whether this distinction could be captured by a deep generative model.

methodsIn a retrospective cohort of 185 epilepsy patients who underwent iEEG monitoring, we analyzed 686 410 HFOs across 18 265 brain contacts. To learn morphological characteristics, each event was transformed into a time-frequency plot and input into a variational autoencoder. We characterized latent space clusters containing morphologically defined putative pathological HFOs (mpHFOs) using interpretability analysis, including latent space disentanglement and time-domain perturbation. We built a predictive model to forecast postoperative seizure outcomes at 12 months based on the resection status of brain regions exhibiting mpHFOs. This model was compared to current clinical standards that evaluate outcomes based on the extent of seizure onset zone (SOZ) removal.

resultsmpHFOs showed strong associations with expert-defined spikes and were predominantly located within the SOZ. The interpretability analysis discovered novel pathological features, including high power in the gamma (30-80 Hz) and ripple (>80 Hz) bands centered on the event with spike-like activity. These characteristics were consistent across multiple variables, including institution, electrode type, patient demographics, and anatomical location. Predicting postoperative seizure outcomes using the resection ratio of mpHFOs outperformed unclassified HFOs (F1 = .72 vs. .68, p < .01) and matched current clinical standards using SOZ resection (F1 = .74, p = .76). Combining mpHFO data with demographic and SOZ resection status further improved prediction performance (F1 = .83, p < .01). SIGNIFICANCE: Our data-driven approach using the generative artificial intelligence model yielded a novel, explainable definition of pathological HFOs, which has the potential to further enhance the clinical use of HFOs for EZ delineation.

Indexed as

BrainBrain WavesElectrocorticographyEpilepsyAdolescentAdultChildCohort StudiesElectroencephalographyFemaleHumansMaleMiddle AgedRetrospective StudiesYoung Adultartificial intelligencehigh‐frequency oscillationmachine learningself‐supervised learning

Identifiers

PMID40650866
PMCPMC12266643

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