CRCNS: Computational Model of Chronic Pain Analgesia via Closed-Loop Peripheral Nerve Stimulation

Acute pain is important to survival, however, if the pain system becomes hypersensitive to non-painful and painful stimuli this can result in conditions called allodynia and hyperalgesia, respectively, and with time, chronic pain. Chronic pain is a significant burden on society, with an estimated prevalence of 11.2% in the U.S., and is a significant contributor to the opioid epidemic. Neuromodulation, via electrical stimulation of nerve fibers, has shown promise as an alternative pain treatment to pharmaceuticals with less side effects. but is still limited in efficacy for many patients. The programming (selective delivery of pulse width, frequency, and amplitude) of the stimulation is often performed by trial-and-error, and is kept constant (i.e., is open loop) between programming sessions. Closed-loop (CL) stimulation, in contrast, adapts over time to the system needs by automatically adjusting the parameters in response to a measured pain signal in the body. CL approaches in engineering systems are often designed based on models that mathematically characterize how a system responds to an actuation signal. Current CL approaches for pain, however, are model-free and simply wait for measured pain activity in the spinal cord to cross a threshold before activating suppressive stimulation. This acts as a local anesthetic, suppressing pathological pain, but unfortunately it also suppresses acute pain that alerts the body to damaging stimuli. In the proposed program, we will address these limitations by building a computational framework for a novel adaptive, model-based closed-loop peripheral nerve stimulation (PNS) approach for the correction of the dysfunctional pain system back to a normal physiological state. This will be accomplished by designing “model-matching” feedback PNS strategies, which match the response to exogenous stimuli (e.g. paw rub) of the CL pain system in a nerveinjured animal to that of a naïve, healthy animal. In order to match responses, we propose to build pseudolinear time invariant (pLTI) models of the response to stimulation in healthy and nerve injured conditions by collecting data and performing system identification. We will then optimize controllers to minimize the error between the responses. These controllers will be designed and optimized in silico then tested in vivo by continuously recording the electrophysiological response and responding by changing the amplitude and polarity of PNS pulses held at a constant frequency. This framework will be developed and tested using novel electrophysiological recordings from wide dynamic range (WDR) neurons in the dorsal horn of the spinal cord in naïve and nerve-injured rats in response to PNS and stimuli (e.g. stroke of a paw). WDR neurons are a cell type selected for its well documented deviation from its baseline in pain syndromes and role as a relay station between pain receptors in the periphery and the thalamus in the brain. The thalamus is the gateway for pain information to enter the brain for perception and can be accessed and recorded from using deep brain stimulation (DBS) electrodes in humans, making it an ideal location for pain therapies designed for translation in the future. Thus, we will simultaneously record from WDR neurons and pain sensitive populations of neurons in the thalamus.