Quantification of seizure termination patterns reveals limited pathways to seizure end

Abstract

Seizures result from a variety of pathologies and exhibit great diversity in their dynamics. Although many studies have examined the dynamics of seizure initiation, few have investigated the mechanisms leading to seizure termination. We examined intracranial recordings from patients with intractable focal epilepsy to differentiate seizure termination patterns and investigate whether these termination patterns are indicative of different underlying mechanisms. Seizures (n=710) were recorded intracranially from 104 patients and visually classified as focal or secondarily generalized. Only two patterns emerged from this analysis: (a) those that end simultaneously across the brain (synchronous termination), and (b) those whose ictal activity terminates in some regions but continues in others (asynchronous termination). Finally, seizures ended with either an intermittent bursting pattern (burst suppression pattern), or continuous activity (continuous bursting). These findings allowed for a classification and quantification of the burst suppression ratio, absolute energy and network connectivity of all seizures and comparison across different seizure termination patterns. We found that different termination patterns can manifest within a single patient, even in seizures originating from the same onset locations. Most seizures terminate with patterns of burst suppression regardless of generalization but that seizure that secondarily generalize show burst suppression patterns in 90% of cases, while only 60% of focal seizures exhibit burst suppression. Interestingly, we found similar absolute energy and burst suppression ratios in seizures with synchronous and asynchronous termination, while seizures with continuous bursting were found to be different from seizures with burst suppression, showing lower energy during seizure and lower burst suppression ratio at the start and end of seizure. Finally, network density was observed to increase with seizure progression, with significantly lower densities in seizures with continuous bursting compared to seizures with burst suppression. Our study demonstrates that there are a limited number of seizure termination patterns, suggesting that, unlike seizure initiation, the number of mechanisms underlying seizure termination is constrained. The study of termination patterns may provide useful clues about how these seizures may be managed, which in turn may lead to more targeted modes of therapy for seizure control.

Multiple mechanisms shape the relationship between pathway and duration of focal seizures

“…One promising avenue to understanding seizure pathways and their durations is developing computational models that capture not only specific stages of a seizure, but also neural dynamics throughout the seizure’s evolution. Many studies have focused on computationally analyzing 8 , 9 , 59–62 or modelling 5 , 13 , 63 , 64 variability in seizure onset and termination dynamics. For example, Jirsa et al 13 characterized seizures by the types of dynamic transitions that occurred at seizure start and end.…”

Seizure pathways and seizure durations can vary independently within individual patients with focal epilepsy

“…One promising avenue to understanding seizure pathways and their durations is developing computational models that capture not only specific stages of a seizure, but also neural dynamics throughout the seizure's evolution. Many studies have focused on computationally analysing (Afra et al, 2015;Alarcon et al, 1995;Jiménez-Jiménez et al, 2015;Lagarde et al, 2019;Salami et al, 2020Salami et al, , 2021 or modelling (Jirsa et al, 2014;Proix et al, 2018;Saggio et al, 2020;Wang et al, 2017) 2020) also developed a model that explains relationships between dynamotypes as well as more complex seizure dynamics such as status epilepticus. Such models can therefore also capture the seizure's full evolution, or dynamical pathway (Karoly et al, 2018;Wendling et al, 2002).…”

“…Many health conditions are challenging to treat due to changes in symptoms and disease severity over time (Barends et al, 2020;Fava et al, 2003;Frey et al, 2011). Recent research has emphasised that temporal fluctuations are also an important consideration in focal epilepsy, as seizures can change over time within the same patient (Cook et al, 2016;Karoly et al, 2018;Saggio et al, 2020;Salami et al, 2020Salami et al, , 2021Schroeder et al, 2020). Specifically, a patient's spatiotemporal seizure dynamics can vary in two main ways.…”

Large-scale neural recordings with single-cell resolution in human cortex using high-density Neuropixels probes

“…The burst suppression ratio (BSR) was computed using an automated method 26,27 (https://github.com/drasros/bs_detector_icueeg). After averaging the LFP across all channels, this method then labels each time sample as either burst or suppression.…”

“…Single unit sorting was performed using Kilosort 3.0 17 (https://github.com/MouseLand/Kilosort) as well as Phy (https://github.com/cortex-lab/phy) Custom Matlab code (version R2020a) and python code in combination with open source code from the Fieldtrip toolbox (http://www.fieldtriptoolbox.org/) was used for the majority of the analyses with some code involving manual alignment available on Github (https://github.com/Center-For-Neurotechnology/CorticalNeuropixelProcessingPipeline). The burst suppression ratio (BSR) was computed using an automated method 26,27 (https://github.com/drasros/bs_detector_icueeg). Reconstruction of electrode locations and the manual tracing was done using the open source, free software Blender (https://www.blender.org/).…”