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ORIGINAL RESEARCH article

Front. Netw. Physiol.
Sec. Networks in the Brain System
Volume 4 - 2024 | doi: 10.3389/fnetp.2024.1425625
This article is part of the Research Topic The Network Theory of Epilepsy at Twenty View all 8 articles

Virtual stimulation of interictal EEG network localizes the EZ as a measure of cortical excitability

Provisionally accepted
Sophia R. Zhai Sophia R. Zhai 1*Sridevi V. Sarma Sridevi V. Sarma 1,2Kristín Gunnarsdottir Kristín Gunnarsdottir 1,2Nathan E. Crone Nathan E. Crone 3Adam G. Rouse Adam G. Rouse 3Jennifer J. Cheng Jennifer J. Cheng 4Michael J. Kinsman5 Michael J. Kinsman5 4Patrick Landazuri Patrick Landazuri 4Utku Uysal Utku Uysal 5Carol M. Ulloa Carol M. Ulloa 5Nathaniel Cameron Nathaniel Cameron 4Sara K. Inati Sara K. Inati 6Kareem Zaghloul Kareem Zaghloul 6Varina L. Boerwinkle Varina L. Boerwinkle 7Sarah Wyckoff Sarah Wyckoff 7Niravkumar Barot Niravkumar Barot 8Jorge A. Gonzalez-Martinez Jorge A. Gonzalez-Martinez 9Joon Y. Kang Joon Y. Kang 3*Rachel J. Smith Rachel J. Smith 10*
  • 1 Biomedical Engineering, Johns Hopkins University, Baltimore, United States
  • 2 Institute for Computational Medicine, School of Medicine, Johns Hopkins Medicine, Baltimore, Maryland, United States
  • 3 Department of Neurology and Neurosurgery, The Johns Hopkins Hospital, Johns Hopkins Medicine, Baltimore, Maryland, United States
  • 4 Department of Neurosurgery, University of Kansas Medical Center, Kansas City, Kansas, United States
  • 5 Department of Neurology, University of Kansas Medical Center, Kansas City, Kansas, United States
  • 6 Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, Bethesda, Maryland, United States
  • 7 Barrow Neurological Institute (BNI), Phoenix, Arizona, United States
  • 8 Department of Neurology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
  • 9 Department of Neurosurgery, University of Pittsburgh, Pittsburgh, PA, United States
  • 10 Department of Electrical and Computer Engineering, School of Engineering, University of Alabama at Birmingham, Birmingham, Alabama, United States

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

    For patients with drug-resistant epilepsy, successful localization and surgical treatment of the epileptogenic zone (EZ) can bring seizure freedom. However, surgical success rates vary widely because there are currently no clinically validated biomarkers of the EZ. Highly epileptogenic regions often display increased levels of cortical excitability, which can be probed using singlepulse electrical stimulation (SPES), where brief pulses of electrical current are delivered to brain tissue. It has been shown that high-amplitude responses to SPES can localize EZ regions, indicating a decreased threshold of excitability. However, performing extensive SPES in the epilepsy monitoring unit (EMU) is time-consuming. Thus, we built patient-specific in silico dynamical network models from interictal intracranial EEG (iEEG) to test whether virtual stimulation could reveal information about the underlying network to identify highly excitable brain regions similar to physical stimulation of the brain. We performed virtual stimulation in 69 patients that were evaluated at five centers and assessed for clinical outcome one year post surgery. We further investigated differences in observed SPES iEEG responses of 14 patients stratified by surgical outcome. Clinically-labeled EZ cortical regions exhibited higher excitability from virtual stimulation than non-EZ regions with most significant differences in successful patients and little difference in failure patients. These trends were also observed in responses to extensive SPES performed in the EMU. Finally, when excitability was used to predict whether a channel is in the EZ or not, the classifier achieved an accuracy of 91%. This study demonstrates how excitability determined via virtual stimulation can capture valuable information about the EZ from interictal intracranial EEG.

    Keywords: epileptogenic zone, Dynamic network model, intracranial EEG, Virtual stimulation, cortical excitability, Single-pulse electrical stimulation, cortico-cortical evoked potentials

    Received: 30 Apr 2024; Accepted: 24 Jul 2024.

    Copyright: © 2024 Zhai, Sarma, Gunnarsdottir, Crone, Rouse, Cheng, Kinsman5, Landazuri, Uysal, Ulloa, Cameron, Inati, Zaghloul, Boerwinkle, Wyckoff, Barot, Gonzalez-Martinez, Kang and Smith. 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:
    Sophia R. Zhai, Biomedical Engineering, Johns Hopkins University, Baltimore, United States
    Joon Y. Kang, Department of Neurology and Neurosurgery, The Johns Hopkins Hospital, Johns Hopkins Medicine, Baltimore, 21218, Maryland, United States
    Rachel J. Smith, Department of Electrical and Computer Engineering, School of Engineering, University of Alabama at Birmingham, Birmingham, AL 35294, Alabama, United States

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