Abstracts

Rationale: Epilepsy, one of the most common neurological disorders, is characterized by spontaneous, seemingly random seizures interleaved with seizure-free periods. Personalized treatments for seizure prevention may be possible but depend critically on the ability to anticipate seizure timing. Methods: In a recent analysis of 37 patients with epilepsy implanted with a device that provides chronic electrocorticography (ECoG; RNS® System, NeuroPace, Inc.), we uncovered multidien (multi-day) rhythms of interictal epileptiform activity (IEA) that are biomarkers for seizure risk (Baud et al., 2018). Here, using similar methodology, we extend these findings to a larger cohort of RNS System patients (N=230) who charted their seizures on a daily basis over years (median: 9.5 years). Results: First, we characterized multidien rhythms of IEA in chronic ECoG. Using a measure of spectral entropy against surrogate time-series, we found that more than 80% of patients had multidien rhythms of IEA for at least 70% of the recording duration. Second, we classified patients by their multidien IEA periodicity and found five clusters, centered around 7 days, 9–10 days, 14–15 days, 25–30 days, and 35 days. Third, we found that the timing of clinical seizures reported by patients depended on multidien IEA phase (phase-locking values: 0.2–0.8). Conclusions: The high prevalence of multidien IEA rhythms in this large cohort suggests that seizure risk estimation using these potential biomarkers may have broad applicability. Our findings underscore the power of chronic recordings of brain activity to reveal patterns that influence seizure timing. Funding: No funding