Abstract
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.
Keywords
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Article info
Publication history
Published online: November 16, 2022
Accepted:
September 30,
2022
Received in revised form:
September 8,
2022
Received:
July 2,
2022
Footnotes
Source(s) of financial support: This work was supported by the National Institutes of Health (Bethesda, MD) grants [R01AT009401 (Claire A. Zurna, Sridevi V. Sarma, and Yun Guan), NS110598 (Yun Guan), NS117761 (YG), T32 NSO70201 (Christine Beauchene), T32 GM119998 (Claire A. Zurna)], the Blaustein Pain Research Fund (Claire A. Zurna), the Neurosurgery Pain Research Institute at Johns Hopkins (Christine Beauchene, Michael Caterina), and the Lerner Family Fund for Pain Research (Christine Beauchene).
Site: This work was performed at the Johns Hopkins University, Baltimore, MD, USA.
Conflict of Interest: Yun Guan received research grant support from Medtronic, Inc, Minneapolis, MN. The remaining authors reported no conflict of interest.
Identification
Copyright
© 2022 International Neuromodulation Society. Published by Elsevier Inc. All rights reserved.
