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Virtual stimulation of the interictal EEG network localizes the EZ as a measure of cortical excitability.
Zhai, Sophia R; Sarma, Sridevi V; Gunnarsdottir, Kristin; Crone, Nathan E; Rouse, Adam G; Cheng, Jennifer J; Kinsman, Michael J; Landazuri, Patrick; Uysal, Utku; Ulloa, Carol M; Cameron, Nathaniel; Inati, Sara; Zaghloul, Kareem A; Boerwinkle, Varina L; Wyckoff, Sarah; Barot, Niravkumar; González-Martínez, Jorge A; Kang, Joon Y; Smith, Rachel June.
Affiliation
  • Zhai SR; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.
  • Sarma SV; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.
  • Gunnarsdottir K; Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States.
  • Crone NE; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.
  • Rouse AG; Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States.
  • Cheng JJ; Department of Neurology, Johns Hopkins University, Baltimore, MD, United States.
  • Kinsman MJ; Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS, United States.
  • Landazuri P; Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS, United States.
  • Uysal U; Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS, United States.
  • Ulloa CM; Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS, United States.
  • Cameron N; Department of Neurology, University of Kansas Medical Center, Kansas City, KS, United States.
  • Inati S; Department of Neurology, University of Kansas Medical Center, Kansas City, KS, United States.
  • Zaghloul KA; Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS, United States.
  • Boerwinkle VL; Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States.
  • Wyckoff S; Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States.
  • Barot N; Barrow Neurological Institute, Phoenix Children's Hospital, Phoenix, AZ, United States.
  • González-Martínez JA; Barrow Neurological Institute, Phoenix Children's Hospital, Phoenix, AZ, United States.
  • Kang JY; Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States.
  • Smith RJ; Department of Neurosurgery, University of Pittsburgh, Pittsburgh, PA, United States.
Front Netw Physiol ; 4: 1425625, 2024.
Article in En | MEDLINE | ID: mdl-39229346
ABSTRACT

Introduction:

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 single-pulse 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.

Methods:

We performed virtual stimulation in 69 patients that were evaluated at five centers and assessed for clinical outcome 1 year post surgery. We further investigated differences in observed SPES iEEG responses of 14 patients stratified by surgical outcome.

Results:

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%.

Discussion:

This study demonstrates how excitability determined via virtual stimulation can capture valuable information about the EZ from interictal intracranial EEG.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Front Netw Physiol Year: 2024 Document type: Article Affiliation country: United States Country of publication: Switzerland

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Front Netw Physiol Year: 2024 Document type: Article Affiliation country: United States Country of publication: Switzerland