Abstracts

Accuracy of Predicting Surgical Outcomes Using Ictal Electrical Source Imaging in Patients with Drug-resistant Epilepsy

Abstract number : 2.09
Submission category : 3. Neurophysiology / 3G. Computational Analysis & Modeling of EEG
Year : 2023
Submission ID : 380
Source : www.aesnet.org
Presentation date : 12/3/2023 12:00:00 AM
Published date :

Authors :
Presenting Author: Boyoung Kim, MD – Asan medical center

Hyojin Nam, MD – Asan medical center; Sang-Ahm Lee, MD – Asan medical center; Yong Seo Koo, MD – Asan medical center

Rationale: Electrical Source Imaging (ESI) allows the evaluation of the brain source corresponding to a specific scalp electric field. ESI is subdivided into two categories: interictal ESI (II-ESI) and ictal ESI (IC-ESI). While there is ample literature focusing on II-ESI, studies dedicated to IC-ESI are notably scarce. The limited research on IC-ESI can be attributed to the technical complexities in pinpointing the precise electrographic seizure onset and the potential for clinical seizure movements to generate EEG artifacts. Therefore, we investigated the accuracy of IC-ESI in identifying the epileptogenic zone in patients with drug-resistant epilepsy (DRE) who were undergoing epilepsy surgery.

Methods: We retrospectively reviewed the medical records of 544 DRE patients who underwent presurgical evaluations at Asan Medical Center between 2000 and 2021. The inclusion criteria for our study were: patients having the following: 1) at least two recorded seizures; 2) undergone video EEG monitoring using 64 channels; 3) high-resolution 3D-T1 MRI imaging data; and 4) a one-year follow-up outcome after surgery. The earliest occurrence of rhythmic ictal activity was determined using time-frequency analysis and the selection of identical ictal waves was based on the similarity of their topographic maps. For the ESI analysis, we applied individual head models and used a distributed source model (sLORETA). The evaluation of patient outcomes was conducted using Engel’s classification at the one-year post-operative stage.

Results: Among the 54 DRE patients who underwent surgery and fulfilled the inclusion criteria, ictal scalp EEG data from 232 seizures in 45 patients (83%) were suitable for ESI analysis. Out of these, 28 patients (62.2%) achieved favorable outcomes as defined by Engel's Classification I and II. The accuracy of IC-ESI in localizing the epileptogenic zone was 64.4%. This performance was not significantly different from that of FDG-PET and ictal SPECT, which had accuracies of 68.9% (p=0.211) and 73.3% (p=0.211), respectively. The accuracy of IC-ESI was not significantly impacted by the presence or absence of a lesion in MRI scans. For instance, among the 15 patients with no detectable abnormal lesions on MRI, the accuracies of ESI, PET, and SPECT were 66.6%, 40%, and 75%, respectively. ESI's accuracy was not significantly different from that of PET and SPECT (p=1.00). Furthermore, among the 30 lesional DRE patients, the accuracies of ESI, PET, and SPECT were 63.3%, 83.3%, and 72.2%, respectively. Once again, ESI's accuracy showed no significant difference from that of PET and SPECT (p=0.228).



Conclusions: The findings of this study reveal that the precision of IC-ESI in localizing the epileptogenic zone is on par with ictal SPECT and brain FDG-PET scans. While IC-ESI detection presents more challenges compared to II-ESI, its superior accuracy underscores its value as a reliable tool for pinpointing the epileptogenic zone.



Funding: This work was supported by grants (Grant No.: 2020IE0013-1, 2022IE0001-1, 2023IE0002-1) from Asan Medical Center, Asan Institute for Life Sciences, Seoul, Republic of Korea

Neurophysiology