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

急性疼痛对生存很重要，然而，如果疼痛系统对非疼痛和 疼痛刺激这会导致分别称为超感痛觉和痛觉过敏的情况，随着时间的推移， 慢性疼痛。慢性疼痛是社会的一个重大负担，据估计，在美国的患病率为11.2%。 美国，是阿片类药物流行的重要贡献者。神经调节，通过电刺激 神经纤维，已经显示出作为一种副作用较小的药物的替代疼痛治疗的前景， 但对许多患者的疗效仍然有限。编程(脉冲宽度，频率， 和幅度)的刺激通常通过反复试验来执行，并且保持恒定(即，是开环) 在编程会话之间。相比之下，闭环系统(CL)刺激会随着时间的推移而适应系统 需要自动调整参数，以响应身体中测量的疼痛信号。电子邮件 工程系统中的方法通常是基于数学特征的模型来设计的 系统对激励信号的响应方式。然而，目前用于疼痛的CL方法是无模型的，并且 只需等待脊髓中测量到的疼痛活动超过阈值，就可以激活抑制 刺激。这作为一种局部麻醉剂，抑制病理性疼痛，但不幸的是，它还 抑制剧烈的疼痛，提醒身体注意破坏性的刺激。在拟议的计划中，我们将解决 通过为一种新的自适应的、基于模型的闭环系统构建计算框架来实现这些限制 周围神经刺激(PNS)方法纠正功能性疼痛系统恢复正常 正常的生理状态。这将通过设计“模型匹配”的反馈PN来实现 这些策略将神经损伤动物的CL疼痛系统对外部刺激(如爪子摩擦)的反应与幼稚、健康的动物的反应相匹配。为了匹配响应，我们建议构建 健康人和神经损伤患者刺激反应的伪线性时不变量模型 通过收集数据和执行系统识别来确定条件。然后，我们将优化控制器以 将响应之间的误差降至最低。这些控制器将在Silico中设计和优化 通过连续记录电生理反应和通过改变 保持恒定频率的PNS脉冲的幅度和极性。这一框架将被开发并 使用新的来自背侧宽动态范围(WDR)神经元的电生理记录进行测试 幼稚和神经损伤的大鼠对三叉神经痛和刺激(如爪子划动)做出反应的脊髓角。 WDR神经元是一种细胞类型，因为它在疼痛综合征中偏离基线有充分的证据 并在外周疼痛感受器和大脑丘脑之间扮演中继站的角色。这个 丘脑是疼痛信息进入大脑进行感知的门户，可以访问和 在人类中使用脑深部刺激(DBS)电极进行记录，使其成为疼痛的理想位置 为未来的翻译而设计的治疗方法。因此，我们将同时记录WDR神经元和 丘脑中对疼痛敏感的神经元群体。

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.