AUTHOR=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
TITLE=Virtual stimulation of the interictal EEG network localizes the EZ as a measure of cortical excitability
JOURNAL=Frontiers in Network Physiology
VOLUME=4
YEAR=2024
URL=https://www.frontiersin.org/journals/network-physiology/articles/10.3389/fnetp.2024.1425625
DOI=10.3389/fnetp.2024.1425625
ISSN=2674-0109
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.