The future of therapeutic peripheral nerve stimulation for chronic pain

• Published in: Annual reviews in control 2022, Vol.54, p.377-385
• Main Authors: Beauchene, Christine, Zurn, Claire A., Duan, Wanru, Guan, Yun, Sarma, Sridevi V.
• Format: Article
• Language: English
• Subjects: Chronic pain; H-infinity control; Linear-time-invariant model; Model matching; Peripheral nerve stimulation; Wide-dynamic range neuron
• ISSN: 1367-5788
• DOI: 10.1016/j.arcontrol.2022.08.001

Chronic pain affects about 100 million adults in the US and is primarily treated with drugs, which can have significant side effects. A promising alternative therapy is electrical peripheral nerve stimulation (PNS) treatment. However, it has been associated with suboptimal efficacy since its modulation mechanisms are unclear, and the current therapies are primarily open-loop (i.e. manually adjusting the stimulation parameters). Critical to advancing PNS treatment is a deeper understanding of pain processing, but the pain system is difficult to probe experimentally and analyze via mathematical models. Previously, mechanistic models of pain transmission have been developed to investigate modulation mechanisms but are nonlinear and high-dimensional. In this work, we construct linear phenomenological models of spinal cord activity in chronic pain and healthy conditions derived from state-of-the-art electrophysiological recordings from rats. We can then accurately predict the neural responses to electrical stimulation of the peripheral nerve in both conditions. We then apply H∞ control systems techniques to drive the dynamics of the chronic pain model into normal ranges of physiological pain by using closed-loop control of the PNS. This proof-of-concept computational framework will guide the development of new closed-loop PNS therapies for chronic pain, one of the most prevalent diseases on earth.