Abstract
Objective
Variability in the frequency, timing, and pattern of seizures may influence the assessment of the effect of antiseizure medications (ASMs) when measuring seizure frequency, especially in patients with infrequent seizures. A low seizure rate is an exclusion criterion for enrollment of patients with epilepsy in clinical trials and requires prolonged periods of seizure monitoring, thus delaying appropriate treatment interventions. We investigated whether an electroencephalogram (EEG)–based complexity measure of seizure susceptibility of epileptic mice provides a reliable alternative to seizure frequency for evaluating the efficacy of ASMs.
Methods
We used a mouse model of acquired epilepsy characterized by variability in seizure frequency over time and among mice, as observed in humans. We analyzed EEG recordings from chronic epileptic mice (n = 106) at baseline and during treatment with phenobarbital, valproate, carbamazepine, or phenytoin. We used recurrence quantification analysis to detect increased autocorrelation and critical slowing-down, two signatures of criticality that together contribute to estimate the dimension of phase–space of the EEG signals. The measurements of dimension and seizure frequency were compared as proxies for seizure susceptibility by correlation tests and evaluation of ASM efficacy.
Results
Dimension provided a statistically robust (inverse) estimate of seizure susceptibility of mice, including mice with low seizure frequency or no seizures during the observation periods. In contrast, seizure frequency provided a reliable measure only in mice with a high seizure rate. Consistently, evaluation of ASM efficacy using dimension variations accurately reproduced seizure responsiveness patterns in this mouse model.
Significance
EEG-based dimension provides a reliable measure of mouse propensity to experience seizures as well as ASM efficacy, regardless of seizure rates. Measuring dimension variation should facilitate the inclusion of subjects with low seizure rate in preclinical and clinical trials while also shortening the periods of monitoring. This could accelerate both the development of new treatments and therapeutic decisions in the medical field.
ABR