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ORIGINAL RESEARCH article

Front. Netw. Physiol.
Sec. Networks in the Brain System
Volume 4 - 2024 | doi: 10.3389/fnetp.2024.1491967

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

Provisionally accepted
Varina Louise Boerwinkle Varina Louise Boerwinkle 1*Kristin M Gunnarsdottir Kristin M Gunnarsdottir 2Bethany Sussman Bethany Sussman 3Sarah N. Wyckoff Sarah N. Wyckoff 4Emilio G. Cediel Emilio G. Cediel 1Belfin Robinson Belfin Robinson 1William R Reuther William R Reuther 1Aryan Kodali Aryan Kodali 1Sridevi V Sarma Sridevi V Sarma 2
  • 1 Division of Child Neurology, University of North Carolina, School of Medicine, Chapel Hill, NC, United States, Chapel Hill, Nebraska, United States
  • 2 Department of Biomedical Engineering, Johns Hopkins University, Baltimore, United States
  • 3 Barrow Neurological Institute at Phoenix Children's Hospital, Phoenix, Arizona, United States
  • 4 Brainbox Inc, 2101 E Biddle St, Baltimore, MD 21213, Baltimore, United States

The final, formatted version of the article will be published soon.

    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.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. for the SSI scores were evaluated in 17 pediatric drug-resistant epilepsy (DRE) patients (ages 3-15 years) by comparing outcomes classified as successful (Engel 1 or 2) versus unsuccessful (Engel 3 or 4) at one year postsurgery.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 1-2) from poor (Engel 3-4) surgical outcomes, outperforming the predictive accuracy of individual biomarkers from either modality alone. The combined dynamic network model demonstrated superior predictive performance compared to 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.

    Keywords: Drug-resistant epilepsy, Seizure onset zone (SOZ), interictal intracranial EEG, resting state fMRI, Dynamic network modeling

    Received: 05 Sep 2024; Accepted: 30 Dec 2024.

    Copyright: © 2024 Boerwinkle, Gunnarsdottir, Sussman, Wyckoff, Cediel, Robinson, Reuther, Kodali and Sarma. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Varina Louise Boerwinkle, Division of Child Neurology, University of North Carolina, School of Medicine, Chapel Hill, NC, United States, Chapel Hill, Nebraska, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.