Evidence map›Paper›PMID 39936165›Full record

ArticleFrontiers in network physiology2024

Combining interictal intracranial EEG and fMRI to compute a dynamic resting-state index for surgical outcome validation.

Varina L Boerwinkle, Kristin M Gunnarsdottir, Bethany L Sussman, Sarah N Wyckoff, Emilio G Cediel, Belfin Robinson, William R Reuther, Aryan Kodali, Sridevi V Sarma

Abstract read
In one paragraph

Article in Frontiers in network physiology, 2024. 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

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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

9 authors.

Varina L Boerwinkle *Division of Child Neurology, University of North Carolina in Chapel Hill, Chapel Hill, NC, United States.
Kristin M Gunnarsdottir *Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.
Bethany L SussmanNeuroscience Research, Barrow Neurological Institute at Phoenix Children's Hospital, Phoenix, AZ, United States.
Sarah N WyckoffBrainbox Inc., Baltimore, MD, United States.
Emilio G CedielDivision of Child Neurology, University of North Carolina in Chapel Hill, Chapel Hill, NC, United States.
Belfin RobinsonDivision of Child Neurology, University of North Carolina in Chapel Hill, Chapel Hill, NC, United States.
William R ReutherDivision of Child Neurology, University of North Carolina in Chapel Hill, Chapel Hill, NC, United States.
Aryan KodaliDivision of Child Neurology, University of North Carolina in Chapel Hill, Chapel Hill, NC, United States.
Sridevi V SarmaDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.

Funding

CRCNS: Following the BOLD lightening at rest strikes the seizure onset zone!R01NS125897 · NINDS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI BOERWINKLE, VARINA · 2021 to 2025
$1.7M
NINDS NIH HHS R01 NS125897
6 · The paper itself

Abstract

Introduction: Accurate localization of the seizure onset zone (SOZ) is critical for successful epilepsy surgery but remains challenging with current techniques. We developed a novel seizure onset network characterization tool that combines dynamic biomarkers of resting-state intracranial stereoelectroencephalography (rs-iEEG) and resting-state functional magnetic resonance imaging (rs-fMRI), vetted against surgical outcomes. This approach aims to reduce reliance on capturing seizures during invasive monitoring to pinpoint the SOZ. Methods: We computed the source-sink index (SSI) from rs-iEEG for all implanted regions and from rs-fMRI for regions identified as potential SOZs by noninvasive modalities. The SSI scores were evaluated in 17 pediatric drug-resistant epilepsy (DRE) patients (ages 3-15 years) by comparing outcomes classified as successful (Engel I or II) versus unsuccessful (Engel III or IV) at 1 year post-surgery. Results: Of 30 reviewed patients, 17 met the inclusion criteria. The combined dynamic index (im-DNM) integrating rs-iEEG and rs-fMRI significantly differentiated good (Engel I-II) from poor (Engel III-IV) surgical outcomes, outperforming the predictive accuracy of individual biomarkers from either modality alone. Conclusion: The combined dynamic network model demonstrated superior predictive performance than standalone rs-fMRI or rs-iEEG indices. Significance: By leveraging interictal data from two complementary modalities, this combined approach has the potential to improve epilepsy surgical outcomes, increase surgical candidacy, and reduce the duration of invasive monitoring.

Indexed as

drug-resistant epilepsydynamic network modelinginterictal intracranial EEGresting-state fMRIseizure onset zone

Identifiers

PMID39936165
PMCPMC11811083

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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.