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

CRCNS：通过闭环周围神经刺激进行慢性疼痛镇痛的计算模型

• 批准号: 10395722
• 负责人: Yun Guan
• 金额: $ 40.94万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10395722
• 关键词: Absence of pain sensation; Acute Pain; Address; Anesthesia procedures; Animals; Back; Brain; Computer Models; Data; Data Set; Deep Brain Stimulation; Electric Stimulation; Electrodes; Electrophysiology (science); Engineering; Exhibits; Feedback; Frequencies; Human; Hyperalgesia; Hypersensitivity; Injury; Local Anesthetics; Location; Measures; Mechanics; Modeling; Nerve Fibers; Neurons; Pain; Pain management; Painless; Pathologic; Pathway interactions; Patients; Perception; Peripheral Nerve Stimulation; Peripheral nerve injury; Pharmacologic Substance; Physiologic pulse; Population; Prevalence; Rattus; Research; Role; Signal Transduction; Societies; Spinal Cord; Spinal cord posterior horn; Stimulus; Stroke; Syndrome; System; Techniques; Technology; Testing; Thalamic structure; Therapeutic; Time; Translations; Update; Width; base; cell type; chronic neuropathic pain; chronic pain; computer framework; design; effective therapy; in silico; in vivo; in vivo evaluation; mathematical model; model design; nerve injury; neuroregulation; novel; opioid epidemic; pain receptor; pain signal; painful neuropathy; predictive modeling; programs; response; restoration; sciatic nerve; side effect; therapy design

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.

急性疼痛对生存很重要，但是，如果疼痛系统变得对非疼痛和疼痛敏感， 疼痛刺激这可分别导致称为异常性疼痛和痛觉过敏的状况，并且随着时间的推移， 慢性疼痛慢性疼痛是社会的一个重大负担，估计患病率为11.2%， 美国，并且是阿片类药物流行的重要促成因素。神经调节，通过电刺激 神经纤维，已经显示出作为药物的替代疼痛治疗的希望，副作用较小， 但对许多患者的疗效仍然有限。编程（选择性地提供脉冲宽度、频率、 和振幅）经常通过试错法来执行，并且保持恒定（即，是开环） 在编程会话之间。相比之下，闭环（CL）刺激随着时间的推移而适应系统 通过响应于身体中测量的疼痛信号自动调节参数来满足需要。CL 工程系统中的方法通常基于数学上表征 系统如何响应致动信号。然而，目前用于疼痛的CL方法是无模型的， 在激活抑制性疼痛之前，只需等待脊髓中测量到的疼痛活动超过阈值即可。 刺激.这是一种局部麻醉剂，抑制病理性疼痛，但不幸的是， 抑制急性疼痛，警告身体对有害刺激。在计划中，我们将 通过建立一个新的自适应，基于模型的闭环计算框架， 外周神经刺激（PNS）方法用于矫正功能失调的疼痛系统， 正常的生理状态。这将通过设计“模型匹配”反馈PNS来实现 策略，其将神经损伤动物中CL疼痛系统对外源性刺激（例如，爪子摩擦）的反应与幼稚健康动物的反应相匹配。为了匹配响应，我们建议建立 健康和神经损伤对刺激的响应的伪线性时不变（pLTI）模型 通过收集数据和执行系统识别来控制条件。然后，我们将优化控制器， 最小化响应之间的误差。这些控制器将在硅片上设计和优化， 通过连续记录电生理反应并通过改变电生理反应来进行体内测试 PNS脉冲的振幅和极性保持在恒定频率。将制定这一框架， 使用来自背侧宽动态范围（WDR）神经元的新电生理记录进行测试， 对PNS和刺激（例如，爪子中风）作出反应的幼稚和神经损伤大鼠的脊髓角。 WDR神经元是一种细胞类型，其在疼痛综合征中与基线的偏离得到了充分的证明 并且在外周的疼痛感受器和大脑中的丘脑之间起着中继站的作用。的 丘脑是疼痛信息进入大脑感知的门户，可以被访问， 记录从使用深部脑刺激（DBS）电极在人类，使其成为一个理想的位置疼痛 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。