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: 10657620
• 负责人: Yun Guan
• 金额: $ 39万
• 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/10657620
• 关键词: Absence of pain sensation; Acute Pain; Anesthesia procedures; Animals; Back; Brain; Computer Models; Data; Data Set; Deep Brain Stimulation; Electric Stimulation; Electrodes; Electrophysiology (science); Engineering; Exhibits; Feedback; Frequencies; Future; Human; Hyperalgesia; Injury; Local Anesthetics; Location; Measures; Modeling; Nerve Fibers; Neurons; Pain; Pain management; Painless; Pathologic; Pathway interactions; Patients; Perception; Peripheral Nerve Stimulation; Peripheral nerve injury; Pharmaceutical Preparations; Pharmacologic Substance; Physiologic pulse; Physiological; Population; Prevalence; Rattus; Research; Role; Signal Transduction; Societies; Spinal Cord; Spinal cord posterior horn; Stimulus; Stroke; Syndrome; System; Techniques; Testing; Thalamic structure; Therapeutic; Time; Translations; Update; Width; allodynia; alternative treatment; cell type; chronic neuropathic pain; chronic pain; computer framework; design; effective therapy; in silico; in vivo; in vivo evaluation; mathematical model; mechanical stimulus; model design; nerve injury; neuroregulation; novel; opioid epidemic; pain receptor; pain signal; painful neuropathy; predictive modeling; programs; response; restoration; sciatic nerve; side effect; technology platform; 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.

然而，如果疼痛系统对非疼痛和疼痛过度敏感，急性疼痛对于生存很重要。 疼痛刺激这可能分别导致称为异常性疼痛和痛觉过敏的情况，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，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，已显示出作为副作用较小的药物的替代疼痛治疗方法的前景， 但对许多患者来说疗效仍然有限。编程（选择性传送脉冲宽度、频率、 刺激的幅度和幅度通常通过反复试验来执行，并保持恒定（即开环） 编程会话之间。相比之下，闭环 (CL) 刺激会随着时间的推移适应系统 通过根据体内测量到的疼痛信号自动调整参数来满足需求。化学发光 工程系统中的方法通常是基于数学特征的模型来设计的 系统如何响应驱动信号。然而，目前针对疼痛的 CL 方法是无模型的，并且 只需等待测量到的脊髓疼痛活动超过阈值，然后再激活抑制功能 刺激。这起到局部麻醉剂的作用，抑制病理性疼痛，但不幸的是它也 抑制急性疼痛，提醒身体注意有害刺激。在拟议的计划中，我们将解决 通过为新型自适应、基于模型的闭环构建计算框架来克服这些限制 周围神经刺激 (PNS) 方法可将功能失调的疼痛系统纠正至正常状态 正常生理状态。这将通过设计“模型匹配”反馈 PNS 来实现 策略，将神经损伤动物的 CL 疼痛系统对外源刺激（例如爪子摩擦）的反应与幼稚的健康动物的反应相匹配。为了匹配响应，我们建议构建 健康人和神经损伤者对刺激反应的伪线性时不变 (pLTI) 模型 通过收集数据和执行系统识别来确定条件。然后我们将优化控制器 最小化响应之间的误差。然后将在计算机中设计和优化这些控制器 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 脉冲的幅度和极性保持在恒定频率。该框架将被开发并 使用来自背侧宽动态范围（WDR）神经元的新型电生理记录进行测试 幼稚大鼠和神经损伤大鼠的脊髓角对三七总皂苷和刺激（例如爪子中风）的反应。 WDR 神经元是一种因其在疼痛综合征中偏离基线而被选中的细胞类型 以及作为外周疼痛感受器和大脑丘脑之间的中继站的作用。这 丘脑是疼痛信息进入大脑进行感知的门户，可以被访问和获取 通过在人体中使用深部脑刺激 (DBS) 电极记录下来，使其成为疼痛的理想位置 为未来的翻译而设计的疗法。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。