Abstracts

Functional Mri-identified Resting State Networks Have Potential to Measure the Clinical Response to Epilepsy Surgery

Abstract number : 3.372
Submission category : 5. Neuro Imaging / 5B. Functional Imaging
Year : 2024
Submission ID : 611
Source : www.aesnet.org
Presentation date : 12/9/2024 12:00:00 AM
Published date :

Authors :
Presenting Author: Emilio G. Cediel, MD – University of North Carolina at Chapel Hill

Erika Duran, MD – University of North Carolina at Chapel Hill
Olivia Leggio, BS – University of North Carolina at Chapel Hill
William Reuther, MS – University of North Carolina at Chapel Hill
Belfin Robinson, PhD – University of North Carolina at Chapel Hill
Varina Boerwinkle, MD – UNC

Rationale: Neuroimaging markers have the potential to significantly enhance the assessment of responses to epilepsy interventions. This study investigates the potential of rs-fMRI to assess the response to epilepsy surgery. Current postoperative follow-up methods rely heavily on ictal activity to assess the impact of interventions on seizure control (Rathore et al, 2011), and may require extended periods, prolonging the use of antiseizure medication and increasing the time of epilepsy treatment trials (Mongerson et al, 2017). Rs-fMRI can identify brain network dysfunction during interictal period (Jiang et al, 2022) and detect seizure networks that are spatially correlated with the seizure onset zone (Chakraborty et al, 2020). The study introduces a measure, Load, based on the power spectrum of regular resting state networks (RSN), and hypothesizes that this Load is related to epileptogenic activity and will change in line with the clinical evolution within each patient.

Methods: This study is a retrospective cohort analysis of pediatric patients from two institutions who underwent epilepsy surgery and had pre- and post-surgery rs-fMRI scans. Data was collected on various clinical parameters. Each network’s Load was computed as the integral of the power spectrum curve over the threshold of 0.06 Hz, divided by the integral of the RSN’s total power spectrum. A scan Load was calculated as the average Load of its RSN. Descriptive statistics and Wilcoxon’s rank-sum test were used to compare the pre- to post-surgery scan results.

Results: The study included 57 epilepsy surgery cases. 96% of the subjects showed at least partial improvement at clinical follow-up and 64% were seizure-free. The mean time between the last surgical intervention and the follow-up scan was 10.8 months. There was a statistically significant difference in the RSN’s Load values before and after the surgical intervention (p=0.002). However, there was no association between the Load value and the reported amount of weekly seizures.

Conclusions: The significant reduction in Load observed after surgical treatment suggests that Load is associated with the seizure burden. The lack of association between the Load and reported amount of seizures at the time of each scan might be related to the heterogeneity across epilepsy etiologies or seizure types across patients. Further studies are needed to develop power spectrum-based biomarkers to predict treatment outcomes.

Funding: No funding was secured for this study.

Neuro Imaging