Modeling Effects of Variable preBötzinger Complex Network Topology and Cellular Properties on Opioid-Induced Respiratory Depression and Recovery

The preBotzinger complex (preBotC), located in the medulla, is the essential rhythm -generating neural network for breathing. The actions of opioids on this network impair its ability to generate robust, rhythmic output, contributing to life -threatening opioid-induced respiratory depression (OIRD). The occurrence of OIRD varies across individuals and internal and external states, increasing the risk of opioid use, yet the mechanisms of this variability are largely unknown. In this study, we utilize a computational model of the preBotC to perform several in silico experiments exploring how differences in network topology and the intrinsic properties of preBotC neurons influence the sensitivity of the network rhythm to opioids. We find that rhythms produced by preBotC networks in silico exhibit variable responses to simulated opioids, similar to the preBotC network in vitro. This variability is primarily due to random differences in network topology and can be manipulated by imposed changes in network connectivity and intrinsic neuronal properties. Our results identify features of the preBotC network that may regulate its susceptibility to opioids.