Synaptic inhibitory dynamics drive benzodiazepine response in pediatric status epilepticus

Journal article

Objective: Pediatric status epilepticus (SE) is a medical emergency associated with significant morbidity. Benzodiazepines (BZPs) are the current first‐line treatment, but do not work in more than one third of children presenting with SE. Animal studies have shown that SE can cause changes in synaptic inhibition signaling that can ultimately lead to BZPs becoming ineffective. However, the relevance of these mechanisms in pediatric patients with SE remains unknown. Methods: To test this hypothesis, we combine clinical electroencephalographic (EEG) recordings with dynamic causal modeling (DCM). This approach allows model‐based inference of cortical synaptic coupling parameters based on EEG recorded across distinct oscillatory states. Results: Our DCM revealed that dynamic changes in inhibitory synaptic coupling explain differences in EEG power spectra associated with BZP treatment responsiveness and guide the transition from ictal to interictal state. Furthermore, in silico simulations demonstrate that there are alternative routes to seizure termination even in cortical circuit models unresponsive to BZPs. Significance: Together, our findings confirm that alterations in synaptic inhibition underlie BZP response during pediatric SE. More broadly, this work further demonstrates the utility of computational modeling to validate insights from basic science in clinically accessible recordings in neurological disorders characterized by abnormal brain states.

Authors: Fedele, T; Burman, RJ; Steinberg, A; Selmin, G; Ramantani, G

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Wiley
• Journal: Epilepsia: Official journal of the International League Against Epilepsy
• Publication date: 2025-04-15
• Acceptance date: 2025-03-21
• DOI: 10.1111/epi.18398
• EISSN: 1528-1167
• ISSN: 0013-9580
• Language: English
• Keywords: status epilepticus; synaptic inhibition; pediatrics; EEG; dynamic causal modeling