Stronger Pre-Surgical Functional Connectivity Network is Associated with Improved Surgical Outcome in Temporal Lobe Epilepsy
Abstract number :
2.226
Submission category :
3. Neurophysiology / 3G. Computational Analysis & Modeling of EEG
Year :
2025
Submission ID :
776
Source :
www.aesnet.org
Presentation date :
12/7/2025 12:00:00 AM
Published date :
Authors :
Presenting Author: Joshua Abata, MD – UC Irvine Medical Center
Victoria Ho, MD, PhD – UCLA
Sebastian Hanna, MD – UC Irvine Medical Center
Kurt Qing, MD, PhD – UC Irvine Medical Center
Doris Deng, MD – UC Irvine Medical Center
Sumeet Vadera, MD – UC Irvine Medical Center
Lilit Mnatsakanyan, MD – UC Irvine Medical Center
Mona Sazgar, MD – UC Irvine Medical Center
David King-Stephens, MD – University of California Irvine
Daniel Shrey, MD – Children's Hospital of Orange County
Beth Lopour, PhD – UC Irvine
Brian Jung, MD – UC Irvine Medical Center
Rationale: Temporal lobe epilepsy (TLE) is the most common form of epilepsy in adults. While epilepsy surgery is the preferred treatment for medication-resistant cases, about a third of patients continue to have seizures post-surgery for reasons that remain unclear. Recent evidence suggests that focal epilepsy is a disorder of brain-wide neural network. This study aims to investigate network differences in pre-surgical resting-state EEG between individuals with unilateral TLE who become seizure-free and those who continue to have seizures after epilepsy surgery.
Methods: We retrospectively identified 15 individuals with unilateral TLE who were seizure-free (SF) after epilepsy surgery and 13 who continued to have seizures post-surgery (nSF). Functional connectivity was measured using cross-correlation techniques on visually normal, resting-state scalp EEG free of epileptiform abnormalities.
Results: Stronger pre-surgical functional connectivity was associated with favorable post-surgical outcomes. The SF group demonstrated significantly higher connectivity strength, particularly in cross-hemispheric and intra-hemispheric networks of the ictal hemisphere (P = 0.02). Functional connectivity strength predicted post-surgical seizure freedom with 75% accuracy (AUC = 0.77). Notably, individuals with the strongest connectivity were all seizure-free, despite heterogeneity in clinical characteristics or imaging findings. Connectivity strength did not correlate with age, disease duration, hippocampal sclerosis, or bilateral epileptiform abnormalities.
Conclusions: This study demonstrates a network difference between SF and nSF patients, supporting the concept that epilepsy is a disorder of neural networks. Functional connectivity analysis from visually normal EEG provides non-invasive, non-redundant information beyond traditional assessments to predict post-surgical outcomes.
Funding: There was no funding that was received in support of this abstract.
Neurophysiology