A Computational Model for Pain Processing in the Dorsal Horn Following Axonal Damage to Receptor Fibers

Abstract

Computational modeling of the neural activity in the human spinal cord may help elucidate the underlying mechanisms involved in the complex processing of painful stimuli. In this study, we use a biologically-plausible model of the dorsal horn circuitry as a platform to simulate pain processing under healthy and pathological conditions. Specifically, we distort signals in the receptor fibers akin to what is observed in axonal damage and monitor the corresponding changes in five quantitative markers associated with the pain response. Axonal damage may lead to spike-train delays, evoked potentials, an increase in the refractoriness of the system, and intermittent blockage of spikes. We demonstrate how such effects applied to mechanoreceptor and nociceptor fibers in the pain processing circuit can give rise to dramatically distinct responses at the network/population level. The computational modeling of damaged neuronal assemblies may help unravel the myriad of responses observed in painful neuropathies and improve diagnostics and treatment protocols.