Steering Toward Normative Wide-Dynamic-Range Neuron Activity in Nerve-Injured Rats With Closed-Loop Peripheral Nerve Stimulation

Objectives: Chronic pain is primarily treated with pharmaceuticals, but the effects remain unsatisfactory. A promising alternative therapy is peripheral nerve stimulation (PNS), but it has been associated with suboptimal efficacy because its modulation mechanisms are not clear and the current therapies are primarily open loop (ie, manually adjusting the stimulation parameters). In this study, we developed a proof-of-concept computational modeling as the first step toward implementing closed-loop PNS in future biological studies. When developing new pain therapies, a useful pain biomarker is the wide-dynamic-range (WDR) neuron activity in the dorsal horn. In healthy animals, the WDR neuron activity occurs in a stereotyped manner; however, this response profile can vary widely after nerve injury to create a chronic pain condition. We hypothesized that if injury-induced changes of neuronal response can be normalized to resemble those of a healthy condition, the pathological aspects of pain may be treated while maintaining protective physiological nociception. Materials and Methods: Using an in vivo electrophysiology data set of WDR neuron recordings obtained in nerve-injured rats and naïve rats, we constructed sets of linear phenomenologic models of WDR firing rate during windup stimulation for both conditions. Then, we applied robust control systems techniques to identify a closed-loop PNS controller, which can drive the dynamics of WDR neuron response in neuropathic pain model into ranges associated with normal physiological pain. Results: The sets of identified linear models can accurately predict, in silico, nonlinear neural responses to electrical stimulation of the peripheral nerve. In addition, we showed that continuous closed-loop control of PNS can be used to normalize WDR neuron firing responses in three injured cases. Conclusions: In this proof-of-concept study, we show how tractable, linear mathematical models of pain-related neurotransmission can be used to inform the development of closed-loop PNS. This new application of robust control to neurotechnology may also be expanded and applied across other neuromodulation applications.