Abstracts

Characterization of Seizure Onset and Offset Dynamics in the Presence of Anti-Seizure Medications in an Animal Model of Temporal Lobe Epilepsy

Abstract number : 3.447
Submission category : 3. Neurophysiology / 3A. Video EEG Epilepsy-Monitoring
Year : 2023
Submission ID : 1432
Source : www.aesnet.org
Presentation date : 12/4/2023 12:00:00 AM
Published date :

Authors :
Presenting Author: Ashley Zachery-Savella, BS – University of Utah

Daria Anderson, PhD – University of Sydney; Michelle Le, BS – University of Utah; Sally Scofield, other – University of Utah; Gerald Saunders, other – University of Utah; Peter West, PhD – University of Utah; Karen Wilcox, PhD – University of Utah

Rationale:
Recurrent epileptic seizures interrupt normal brain activity, and this abnormal electrographic activity can be visualized using electroencephalograms (EEGs). Epilepsy treatment is complicated by the fact that patients with similar phenotypes and seizure classifications do not respond similarly to the same treatments, suggesting subtler differences exist that cannot be accounted for by our currently-limited classification system. Investigating seizure onset and offset patterns in models of epilepsy and their response to anti-seizure medications may provide additional metrics to quantify drug efficacy and could lead to improvements in how epilepsy is classified and treated.

Methods:
A total of 54 mice were injected with kainic acid into the basolateral amygdala and observed for 90 days with 24/7 video and EEG recordings (West et al., 2022, Exp. Neurol.). We analyzed seizure frequency, duration, and dynamotype for 639 spontaneous seizures from 14 animals in the 240 mg/kg valproic acid (VPA) cohort. Onset and offset patterns, or “dynamotypes,” of each seizure were visually categorized in a randomized, blinded fashion. There are three onset dynamotypes: SupH, characterized by increasing amplitude; SNIC, characterized by increasing frequency; and SubH, characterized by arbitrary patterns in amplitude and frequency. There are three offsets: SupH—decrease in amplitude, SNIC—decrease in frequency, and FLC—arbitrary amplitude and frequency.

Results:
Seizure frequency (mean and SD) decreased from 3.3 ± 4.1 seizures per day during baseline to 1.1 ± 2.2 during VPA dosing (Wilcoxon Signed-Rank Test, p = 0.0001). Mean seizure duration significantly decreased between baseline and drug periods from 49.2 ± 22.3 to 37.2 ± 11.2 seconds (Wilcoxon Signed-Rank Test, p = 2.1E-5). Figure 1 summarizes average onset and offset dynamotypes. Dominant onset was SubH at baseline, and did not significantly change following VPA. FLC offset was the dominant dynamotype during baseline, but SNIC significantly increased (ANOVA, p = 0.04) and dominated during VPA dosing.

Conclusions:
While no changes in onset patterns were observed, SNIC offset, characterized by decreasing spike frequency before termination, became the dominant dynamotype during VPA dosing. Coincidingly, seizure offsets with arbitrary patterns in frequency and amplitude decreased. Interestingly, seizure duration also significantly decreased during VPA dosing, which may be related to VPA modulating offset dynamics and facilitating seizure termination for breakthrough seizures. We hypothesize that VPA alters seizure offset dynamics, resulting in shorter seizures.

Funding:
This project has been partly funded by Federal funds from the National Institute of Neurological Disorders and Stroke, Epilepsy Therapy Screening Program, National Institutes of Health, and Department of Health and Human Services, under Contract No. HHS 75N95022C00007. Also by: the Undergraduate Research Opportunities Program at the University of Utah, and NIH NINDS: F32 NS114322 awarded to Daria Anderson.



Neurophysiology