Evidence map›Paper›PMID 38703698›Full record

ArticleClinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology2024

Computational EEG attributes predict response to therapy for epileptic spasms.

Rajsekar R Rajaraman, Rachel J Smith, Shingo Oana, Atsuro Daida, Daniel W Shrey, Hiroki Nariai, Beth A Lopour, Shaun A Hussain

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Article in Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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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

5 citing papers in PubMed.

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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

8 authors.

Rajsekar R RajaramanDivision of Pediatric Neurology, UCLA Mattel Children's Hospital and University of California, Los Angeles, Los Angeles, CA, USA.
Rachel J SmithDepartment of Electrical and Computer Engineering, University of Alabama at Birmingham, Birmingham, AL, USA.
Shingo OanaDivision of Pediatric Neurology, UCLA Mattel Children's Hospital and University of California, Los Angeles, Los Angeles, CA, USA.
Atsuro DaidaDivision of Pediatric Neurology, UCLA Mattel Children's Hospital and University of California, Los Angeles, Los Angeles, CA, USA.
Daniel W ShreyDivision of Pediatric Neurology, University of California, Irvine, Irvine, CA, USA; Department of Neurology, Children's Hospital of Orange County, Orange, CA, USA.
Hiroki NariaiDivision of Pediatric Neurology, UCLA Mattel Children's Hospital and University of California, Los Angeles, Los Angeles, CA, USA.
Beth A LopourDepartment of Biomedical Engineering, University of California, Irvine, Irvine, CA, USA.
Shaun A HussainDivision of Pediatric Neurology, UCLA Mattel Children's Hospital and University of California, Los Angeles, Los Angeles, CA, USA. Electronic address: shussain@mednet.ucla.edu.

Funding

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
NINDS NIH HHS K23 NS128318
6 · The paper itself

Abstract

objectiveWe set out to evaluate whether response to treatment for epileptic spasms is associated with specific candidate computational EEG biomarkers, independent of clinical attributes.

methodsWe identified 50 children with epileptic spasms, with pre- and post-treatment overnight video-EEG. After EEG samples were preprocessed in an automated fashion to remove artifacts, we calculated amplitude, power spectrum, functional connectivity, entropy, and long-range temporal correlations (LRTCs). To evaluate the extent to which each feature is independently associated with response and relapse, we conducted logistic and proportional hazards regression, respectively.

resultsAfter statistical adjustment for the duration of epileptic spasms prior to treatment, we observed an association between response and stronger baseline and post-treatment LRTCs (P = 0.042 and P = 0.004, respectively), and higher post-treatment entropy (P = 0.003). On an exploratory basis, freedom from relapse was associated with stronger post-treatment LRTCs (P = 0.006) and higher post-treatment entropy (P = 0.044).

conclusionThis study suggests that multiple EEG features-especially LRTCs and entropy-may predict response and relapse. SIGNIFICANCE: This study represents a step toward a more precise approach to measure and predict response to treatment for epileptic spasms.

Indexed as

ElectroencephalographySpasms, InfantileAnticonvulsantsChildChild, PreschoolFemaleHumansInfantMalePredictive Value of TestsTreatment OutcomeAnticonvulsantsEntropyFunctional connectivityHypsarrhythmiaLong-range temporal correlationsWest syndrome

Identifiers

PMID38703698
PMCPMC12525723

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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.