Preictal High-gamma Power in the Hippocampus and Amygdala Predicts Variability in Seizure Severity
Abstract number :
3.035
Submission category :
1. Basic Mechanisms / 1C. Electrophysiology/High frequency oscillations
Year :
2024
Submission ID :
870
Source :
www.aesnet.org
Presentation date :
12/9/2024 12:00:00 AM
Published date :
Authors :
Presenting Author: Sarah Gascoigne, BSc, PGDip, AFHEA – Newcastle University
Nathan Evans, Mphys – Newcastle University
Gerard Hall, PhD – Newcastle University
Csaba Kozma, BSc – Newcastle University
Mariella Panagiotopoulou, PhD – Newcastle University
Gabrielle M. Schroeder, PhD – Newcastle University
Callum Simpson, BSc – Newcastle University
Christopher Thornton, PhD – Newcastle University
Frances Turner, PhD – Newcastle University
Heather Woodhouse, MSc – Newcastle University
Jessica Blickwedel, MSc – Newcastle University
Fahmida A. Chowdhury, MD – University College London
Beate Diehl, MD – University College London
John Duncan, MD – University College London
Rhys Thomas, MD – Newcastle University
Ryan Faulder, MD – Newcastle University
Kevin Wilson, PhD – Newcastle University
Peter Taylor, PhD – Newcastle University
Yujiang Wang, PhD – Newcastle University
Rationale: Spontaneous seizures in epilepsy are extremely disabling. Seizure prediction algorithms have been suggested in previous work; however, such work focusses on prediction of a certain seizure type with no consideration for its severity. It has been shown that seizure features, including severity, vary on an individual basis. Therefore, an estimation of the clinical impact or severity of a seizure could allow an individual to adequately prepare. Here we used preictal markers of activity in two subcortical regions to predict seizure severity.
Methods: Subjects with intracranial EEG implantation in the hippocampus and/or amygdala (10 subjects, 71 seizures) were retrospectively assessed. We computed eight markers of preictal activity (line length, energy, and six band-powers), summarised across regions using the Lausanne-72 atlas. Stepwise regression was used to select relevant preictal markers.
Results: Using the line length peak marker (see Gascoigne et al., 2023) as a quantitative marker of seizure severity, we created two stepwise linear regression models. Using markers in the amygdala and hippocampus as explanatory variables, 35.1% and 24.1% of variability in severity could be explained respectively. The variables remaining in the model differed for each region (Amygdala: theta, beta, low gamma, high gamma. Hippocampus: line length, energy, theta, high gamma).
Conclusions: Seizure prediction algorithms may offer warnings of impending seizures but do not provide information on potential severity. This work demonstrates that quantitative markers of preictal activity in the hippocampus and amygdala are associated with the severity of the impending seizure. Only the high gamma marker was a significant predictor in both regions, further research is required to further understand this relationship. Future work could apply these methods to other epilepsy types and regions to further establish our understanding of the association between seizure severity and preceding interictal activity.
References
Gascoigne, S. J., Waldmann, L., Schroeder, G. M., Panagiotopoulou, M., Blickwedel, J., Chowdhury, F., ... & Wang, Y. (2023). A library of quantitative markers of seizure severity. Epilepsia, 64(4), 1074-1086.
Funding: The Engineering and Physical Sciences Research Council, Centre for Doctoral Training in Cloud Computing for Big Data (grant number EP/L015358/1), Wellcome Trust (208940/Z/17/Z), UKRI Future Leaders Fellowship (MR/V026569/1) and (MR/T04294X/1).
Basic Mechanisms