Evidence map›Paper›PMID 41283772›Full record

ArticleEpilepsia2026

EEG functional connectivity as a marker of evolution from infantile epileptic spasms syndrome to Lennox-Gastaut syndrome.

Blanca Romero Milà, Virginia B Liu, Rachel J Smith, Derek K Hu, Natalie A Benneian, Shaun A Hussain, Maija Steenari, Donald Phillips, David Adams, Clare Skora and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Epilepsia, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

12 authors.

Blanca Romero MilàDepartment of Biomedical Engineering, University of California, Irvine, Irvine, California, USA.ORCID https://orcid.org/0009-0009-9441-7918
Virginia B LiuDivision of Neurology, Children's Hospital of Orange County, Orange, California, USA.
Rachel J SmithDepartment of Electrical and Computer Engineering, University of Alabama at Birmingham, Birmingham, Alabama, USA.ORCID https://orcid.org/0000-0001-7142-0684
Derek K HuDepartment of Biomedical Engineering, California State University, Long Beach, Long Beach, California, USA.ORCID https://orcid.org/0000-0003-4720-2622
Natalie A BenneianDepartment of Biomedical Engineering, University of California, Irvine, Irvine, California, USA.
Shaun A HussainDivision of Pediatric Neurology, University of California, Los Angeles, California, USA.ORCID https://orcid.org/0000-0001-6947-8852
Maija SteenariDivision of Neurology, Children's Hospital of Orange County, Orange, California, USA.
Donald PhillipsDivision of Neurology, Children's Hospital of Orange County, Orange, California, USA.ORCID https://orcid.org/0009-0000-2817-4481
David AdamsDivision of Neurology, Children's Hospital of Orange County, Orange, California, USA.
Clare SkoraDivision of Neurology, Children's Hospital of Orange County, Orange, California, USA.
Beth A LopourDepartment of Biomedical Engineering, University of California, Irvine, Irvine, California, USA.ORCID https://orcid.org/0000-0003-4233-4802
Daniel W ShreyDivision of Neurology, Children's Hospital of Orange County, Orange, California, USA.ORCID https://orcid.org/0000-0002-3163-4773

Funding

Children's Hospital of Orange CountyLGS FoundationUC Irvine California-Catalonia Engineering Program
6 · The paper itself

Abstract

objectiveTimely diagnosis and effective treatment of Lennox-Gastaut syndrome (LGS) improve prognosis and lower health care costs, but the transition from infantile epileptic spasms syndrome (IESS) to LGS is highly variable and insidious. Objective biomarkers are needed to monitor this progression and guide clinical decision-making.

methodsWe retrospectively collected longitudinal EEG data at the Children's Hospital of Orange County from 15 children who were diagnosed with IESS and later with LGS between 2012 and 2021. Electroencephalography studies were from IESS and LGS diagnoses, between the two diagnoses, and following LGS diagnosis. Functional connectivity networks were calculated using a cross-correlation-based method and assessed relative to diagnostic timepoint, treatment response, and the presence of clinical markers of disease, age, and amplitude of interictal spikes.

resultsConnectivity strength was high at LGS diagnosis and decreased after favorable response to treatment, but it remained stable or increased when response was unfavorable. In all subjects, connectivity strength was higher at the time of LGS diagnosis than at the preceding timepoint. The presence of clinical markers of LGS was associated with high connectivity strength, but no single marker predicted connectivity strength. SIGNIFICANCE: Computational EEG analysis can be used to map the evolution from IESS to LGS. Changes in connectivity may enable prediction of impending LGS and treatment response monitoring, thus facilitating earlier LGS treatment and guiding medical management.

Indexed as

ElectroencephalographyLennox Gastaut SyndromeNerve NetSpasms, InfantileChildChild, PreschoolDisease ProgressionFemaleHumansInfantLongitudinal StudiesMaleRetrospective Studiesbiomarkerdevelopmental epileptic encephalopathydiagnosislongitudinaltreatment response

Identifiers

PMID41283772
PMCPMC12927670

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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.