Abstracts

Rationale: Epilepsy is recognised as a dynamic disease, where both seizure susceptibility and seizure characteristics themselves change over time. Recently, we quantified the electrographic spatio-temporal patterns in seizure evolutions within individual patients and demonstrated that seizure evolution is modulated on circadian, or longer timescales. It is, however, unclear how we can measure these temporal modulations directly. Here, we demonstrate that continuously-recorded interictal iEEG features can capture signatures of these modulations with cycles over hours to months.

Methods: We analysed continuous intracranial electroencephalographic (iEEG) recordings from long-term and chronic monitoring. We extracted cycles in several interictal iEEG signal properties on timescales of hours to months, and we associated these with changes in several seizure characteristics.

Results: As expected, and in agreement with previous studies, we find that all subjects show a circadian cycle in their iEEG signal features such as band power and spike rate. We additionally find other cycles of similar magnitude on subject-specific timescales. Importantly, these cycles can explain changes in seizure characteristics in most subjects above chance level. Interestingly, we also found independent seizure characteristics that are associated with cycles on different timescales in the same subject.

Conclusions: These results suggest that subject-specific fluctuations in interictal iEEG signal properties over timescales of hours to months may serve as markers of seizure modulating processes. Importantly, different characteristics of seizures (such as the duration, or the severity) may be modulated by independent fluctuations on different timescales. We hope that future work can link these detected fluctuations to their biological driver(s). There is a critical need to better understand seizure modulating processes, as this will enable the development of novel treatment strategies that could minimise the seizure spread, duration, or severity and therefore the clinical impact of seizures.

Funding: M.P. was supported by the Engineering and Physical Sciences Research Council, Centre for Doctoral Training in Cloud Computing for Big Data (grant number EP/L015358/1). Y.W. gratefully acknowledges funding from Wellcome Trust (208940/Z/17/Z) and was supported by a UKRI Future Leaders Fellowship (MR/V026569/1).