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.

但是，急性疼痛对于生存至关重 痛苦的刺激可能会导致称为异肌和痛觉过敏的疾病，并随着时间的流逝， 慢性疼痛。慢性疼痛对社会的严重燃烧，估计患病率为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 神经纤维已显示出有望作为副作用较少的药物的替代疼痛治疗， 但对于许多患者来说，效率仍然有限。编程（选择性交付脉冲宽度，频率， 刺激的放大器通常是通过反复试验执行的，并且保持恒定（即是开放环） 在编程会话之间。相反，闭环（CL）模拟随着时间的推移适应系统 需要自动调整参数，以响应体内测量的疼痛信号。 Cl 工程系统中的方法通常是基于数学表征的模型设计的 系统如何响应致动信号。然而，当前的CL方法是无模型的，并且 只需等待脊髓中测量的疼痛活动即可越过阈值，然后激活抑制 刺激。这是一种局部麻醉，抑制了病理疼痛，但不幸的是 抑制急性疼痛，使身体受到破坏刺激的提醒。在拟议的计划中，我们将解决 通过为新型自适应，基于模型的闭环建立计算框架来实现这些限制 周围神经刺激（PNS）方法，用于校正功能失调的疼痛系统回到A 正常的身体状态。这将通过设计“模型匹配”反馈PN来实现 策略与神经受伤的动物中CL疼痛系统的外源刺激的反应与幼稚，健康的动物的反应相匹配。为了匹配响应，我们建议建立 对健康和神经损伤刺激的反应的假性时间不变（PLTI）模型 通过收集数据和执行系统标识的条件。然后，我们将优化控制器 最小化响应之间的误差。这些控制器将在硅中设计和优化 通过不断记录电生理反应并通过改变来进行反应，在体内测试 PNS脉冲的放大器和极性保持恒定频率。将开发此框架，并 使用背面的广泛动态范围（WDR）神经元的新型电生理记录进行了测试 响应PN和刺激的天真和神经伤害大鼠的脊髓角（例如，爪子的中风）。 WDR神经元是一种细胞类型，该细胞类型是因为其有据可查的疼痛综合症基线的偏离 以及作为外围疼痛接收器与大脑中丘脑的接力站的角色。 丘脑是疼痛信息进入大脑以进行感知的门户，可以访问和 从人类中使用深脑刺激（DB）电子记录，使其成为疼痛的理想位置 将来设计用于翻译的疗法。那就是，我们将仅从WDR神经元中记录 丘脑中神经元的疼痛敏感人群。