Rationale: Epilepsy is a common neurological disease characterized by recurring seizures, and roughly 1 in 26 people will develop epilepsy at some point in their life (Holland, K, Healthline, 2019). Electroencephalography (EEG) is an invaluable tool to diagnose epilepsy and localize epileptogenic regions. However, it is difficult to determine changes in EEG during epileptogenesis, the process of developing epilepsy, as patients appear in the clinic after seizures have already presented. This research examines changes to EEG biomarkers during epileptogenesis in the intra-amygdala kainic acid mouse model of mesial temporal lobe epilepsy.
Methods: For this analysis, we used data collected from 22 mice over 90 days using 24/7 Video/EEG to track epileptogenesis after kainic acid injection into the basolateral amygdala from the University of Utah (West et al., Exp Neurol 2022; 349:113954). We analysed a biomarker measuring the inhibitory/excitatory balance from intracranial EEG known as the aperiodic exponent, which measures the slope of the EEG signal’s power spectrum (Donoghue et al., Nat Neurosci 2020; 23:1655-65). Frequency analysis of EEG signals was conducted using the FOOOF python toolbox during both ictal and non-ictal periods. Using a generalised mixed effects model, we quantified changes and alterations of rhythmicity in this biomarker relative to logarithmic seizure burden and time since kainic acid injection to track changes over the course of epileptogenesis.
Results: The mean seizure burden was 32.18 (n = 22, med = 16, s.d. = 61.76) over 90 days. We found an increase in the aperiodic exponent—an epileptic biomarker where a higher value corresponds to higher epileptogenicity—trended, though not significantly, with the number of seizures a mouse had over the 90-day study (p = 0.068; z = 1.825, Coef.= 0.612) We also found that the aperiodic exponent significantly increased throughout the course of epileptogenesis (p < 0.001 , z = 9.853, Coef. 0.019 ).
Conclusions: Prior work indicates that the aperiodic exponent positively correlates with epileptogenic regions (Charlebois et al., Epilepsia 2024; 65:1360-1373), however, not as much is known regarding how it changes throughout epileptogenesis. As time progressed, the aperiodic exponent increased, indicating a shift in the inhibitory/excitatory network to a more epileptogenic brain state. This may indicate a more profound network functional reorganization over time after repeated seizures, that may also correspond to increased risk of drug-resistance. More work is necessary to determine the relationship between seizure burden and changes to the aperiodic exponent during non-ictal states. Understanding the role of rhythmicity in epilepsy, particularly in how it changes throughout development, could be helpful to clinicians, whether by guiding treatment options or opening new research pathways.
Funding:
- University of Sydney Faculty of Engineering Research Scholarship
- Dr. Daria Anderson’s start-up package (The University of Sydney, School of Biomedical Engineering)