Abstract Details

Title External Validation of Novel Method of Seizure Onset Zone Localization Based on Neural Resonance

Topic Epilepsy/Clinical Neurophysiology (EEG)

Presentation(s) P2 - Poster Session 2 (11:45 AM-12:45 PM)

Poster/Presentation
Number 9-006

Objective We aimed to validate a recently published technique that used neural resonance to localize seizure onset zones (SOZ) with a retrospective dataset collected at an external institution. Further, we critically examined the need for a larger, tailored prospective validation study.

Background Accurate localization of the SOZ could improve surgical outcomes in medically refractory epilepsy patients. A recent study successfully used a novel metric of neural resonance to retrospectively identify SOZ regions and predict surgical success. The technique leverages distinct electrophysiological features of evoked responses during single pulse electrode stimulation (SPES) and incorporates them into a dynamical model. We aimed to validate these findings with a retrospective cohort of six patients to assess whether neural resonance predicted surgical outcomes.

Design/Methods We collected intracranial electroencephalographic (iEEG) data from six patients that underwent intracranial monitoring, single-pulse electrical stimulation protocol, and resection surgery between June 2020 and May 2021. Four out of six patients were stimulated in both SOZ regions and non-SOZ regions and thus were tested using the logistic regression model trained on the original dataset at the primary study institution.

Results Three of the four patients had successful surgical outcomes. Of the four patients tested with the logistic regression model, only one out of four was predicted correctly; the model predicted success for one true success and the failure patient.

Conclusions We hypothesize that the non-reproducibility of the original findings are resultant of differences in a research-driven SPES protocol and a clinically driven stimulation protocol. The external validation dataset stimulated a fewer number of sites, used a different clinical EEG and stimulation system, and varied stimulation parameters in a different subspace. However, this work highlights the need for a large, prospective validation dataset that is multi-center and encompasses a wide range of parameters; the gathering of this dataset is currently underway.

Authors/Disclosures
Derrick Barnagian, MD, BS PRESENTER	Mr. Barnagian has nothing to disclose.
Rachel Smith (University of Alabama at Birmingham)	Rachel Smith has nothing to disclose.
Joon Y. Kang, MD (Thomas Jefferson University)	Dr. Kang has nothing to disclose.
Mark Hays	No disclosure on file
	No disclosure on file
	No disclosure on file
Niravkumar Barot, MD (University of PIttsburgh)	Dr. Barot has nothing to disclose.

��ɫ��

��ɫ��