Network model of nociceptive processing in the superficial spinal dorsal horn reveals mechanisms of hyperalgesia, allodynia, and spinal cord stimulation.

The spinal dorsal horn (DH) processes sensory information and plays a key role in transmitting nociception to supraspinal centers. Loss of DH inhibition during neuropathic pain unmasks a pathway from nonnociceptive Aβ-afferent inputs to superficial dorsal horn (SDH) nociceptive-specific (NS) projection neurons, and this change may contribute to hyperalgesia and allodynia. We developed and validated a computational model of SDH neuronal circuitry that links nonnociceptive Aβ-afferent inputs in lamina II/III to a NS projection neuron in lamina I via a network of excitatory interneurons. The excitatory pathway and the NS projection neuron were in turn gated by inhibitory interneurons with connections based on prior patch-clamp recordings. Changing synaptic weights in the computational model to replicate neuropathic pain states unmasked a low-threshold excitatory pathway to NS neurons similar to experimental recordings. Spinal cord stimulation (SCS) is an effective therapy for neuropathic pain, and accumulating experimental evidence indicates that NS neurons in the SDH also respond to SCS. Accounting for these responses may inform therapeutic improvements, and we quantified responses to SCS in the SDH network model and examined the role of different modes of inhibitory control in modulating NS neuron responses to SCS. We combined the SDH network model with a previously published model of the deep dorsal horn (DDH) and identified optimal stimulation frequencies across different neuropathic pain conditions. Finally, we found that SCS-generated inhibition did not completely suppress model NS activity during simulated pinch inputs, providing an explanation of why SCS does not eliminate acute pain. Chronic pain is a severe public health problem that reduces the quality of life for those affected and exacts an enormous socio-economic burden worldwide. Spinal cord stimulation (SCS) is an effective treatment for chronic pain, but SCS efficacy has not significantly improved over time, in part because the mechanisms of action remain unclear. Most preclinical studies investigating pain and SCS mechanisms have focused on the responses of deep dorsal horn (DDH) neurons, but neural networks in the superficial dorsal horn (SDH) are also important for processing nociceptive information. This work synthesizes heterogeneous experimental recordings from the SDH into a computational model that replicates experimental responses and that can be used to quantify neuronal responses to SCS under neuropathic pain conditions.