Network models incorporating chloride dynamics predict optimal strategies for terminating status epilepticus

Seizures that continue for beyond five minutes are classified as status epilepticus (SE) and constitute a medical emergency. Benzodiazepines, the current first-line treatment, attempt to terminate SE by increasing the conductance of chloride-permeable type-A GABA receptors (GABAARs). Despite their widespread use, benzodiazepines are ineffective in over a third of cases. Previous research in animal models has demonstrated that changes in intraneuronal chloride homeostasis and GABAAR physiology may underlie the development of benzodiazepine resistance in SE. However, there remains a need to understand the effect of these changes at a network level to improve translation into the clinical domain. Therefore, informed by data from human EEG recordings of SE and experimental brain slice recordings, we used a large spiking neural network model that incorporates chloride dynamics to investigate and address the phenomenon of benzodiazepine resistance in SE. We found that the GABAAR reversal potential (EGABA) sets SE-like bursting and determines the response to GABAAR conductance modulation, with benzodiazepines being anti-seizure at low EGABA and ineffective or pro-seizure at high EGABA. The SE-like activity and EGABA depended on a non-linear relationship between the strength of Cl- extrusion and GABAAR conductance, but not on the initial EGABA of neurons. Independently controlling Cl- extrusion in the pyramidal and interneuronal cell populations revealed the critical role of pyramidal cell Cl- extrusion in determining the severity of SE activity and the response to simulated benzodiazepine application. Finally, we demonstrate the models utility for considering improved therapeutic approaches for terminating SE in the clinic.