Lethal Interactions of neuronal networks in epilepsy mediated by both synaptic and volume transmission indicate approaches to prevention

Abstract

Neuronal network interactions are important in normal brain physiology and also in brain disorders. Many mesoscopic networks, including the auditory and respiratory network, mediate a single brain function. Macroscopic networks, including the locomotor network, central autonomic network (CAN), and many seizure networks involve interactions among multiple mesoscopic networks. Network interactions are mediated by neuroactive substances, acting via synaptic transmission, which mediate rapid interactions between networks. Slower, but vitally important network interactions, are mediated by volume transmission. Changes in the interactions between networks, mediated by neuroactive substances, can significantly alter network function and interactions. The acoustic startle response involves interactions between auditory and locomotor networks, and also includes brainstem reticular formation (BRF) nuclei, which participate in many different networks. In the fear-potentiated startle paradigm this network interacts positively with the amygdala, induced by conditioning. Seizure networks can interact negatively with the respiratory network, which becomes lethal in sudden unexpected death in epilepsy (SUDEP), a tragic emergent property of the seizure network. SUDEP models that exhibit audiogenic seizures (AGSz) involve interactions between the auditory and locomotor networks with BRF nuclei. In the DBA/1 mouse SUDEP model the AGSz network interacts negatively with the respiratory network, resulting in postictal apnea. The apnea is lethal unless the CAN is able to initiate autoresuscitation. These network interactions involve synaptic transmission, mediated by GABA and glutamate and volume transmission mediated by adenosine, CO₂ and serotonin. Altering these interaction mechanisms may prevent SUDEP. These epilepsy network interactions illustrate the complex mechanisms that can occur among neuronal networks.