CRCNS: Towards Pain Control: Synergizing Computational and Biological Approaches

CRCNS：迈向疼痛控制：协同计算和生物学方法

• 批准号: 9323301
• 负责人: Sridevi V. Sarma
• 金额: $ 39.6万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2020-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9323301
• 关键词: Acupuncture Therapy; Address; Adult; Adverse effects; Affect; Afferent Neurons; Animals; Assimilations; Back; Behavior; Biological; Brain; Clinical; Complementary therapies; Computer Simulation; Data; Electric Stimulation; Electrophysiology (science); Estimation Techniques; Fiber; Future; Hand; Hyperalgesia; In Vitro; Individual; Injury; Instruction; Interneurons; Massage; Measures; Methods; Modeling; Mus; Nerve; Nerve Fibers; Neurons; Nociception; Output; Pain; Pain management; Pattern; Periodicity; Peripheral Nerve Stimulation; Pharmaceutical Preparations; Physiological; Physiology; Play; Population Heterogeneity; Posterior Horn Cells; Process; Property; Regulation; Role; Sensory; Signal Transduction; Spinal; Stimulus; System; Systems Theory; Tactile; Techniques; Testing; Therapeutic; Transgenic Mice; Treatment Efficacy; addiction; alternative treatment; base; chronic pain; control theory; conventional therapy; dorsal horn; excitatory neuron; experimental study; genetic approach; high dimensionality; improved; in vitro activity; in vivo; inhibitory neuron; injured; innovation; nerve injury; neuroregulation; novel; novel therapeutics; pain inhibition; patch clamp; programs; response; sensory input; sensory stimulus; spontaneous pain; success; tool; transmission process

Chronic pain affects -100 million adults in the US, and is inadequately treated with drugs, that are often toxic and have side effects (e.g., addiction). Electrical stimulation in targeted nerve fibers is a promising new therapy, but has had suboptimal efficacy and limited long-term success as its mechanisms of action are unclear. Complementary therapies, such as acupuncture and massage that also involve neuromodulation as a mode of action, have not been quantitatively assessed. Critical to advancing pain therapy is a deeper mechanistic understanding of how a nociceptive signal is processed and modulated in spinal dorsal horn (DH), the first central relay station of nociceptive signaling. There are 3 major functionally distinct subsets of neurons in the DH that play different roles in pain transmission. Excitatory neurons and inhibitory neurons form important local pain circuitry that modulates activity of projection neurons that send ascending pain signals to the brain. It is critical to understand the specific roles for each neuron subset and the therapeutic actions of neurostimulation, tactile inputs, and drugs. For example, do they respond differently to different therapies? Can certain patterns of stimulation selectively inhibit or excite any subset neurons to maximize pain inhibition? These fundamental questions could not be easily addressed in a quantitative manner before this study. First, experimental barriers limit probing the DH to uncover the circuit topology, because it has been difficult to differentiate different subsets of DH neurons while simultaneously studying their physiological properties. Computational models of the DH, on the other hand, can predict how changes in sensory inputs influence pain transmission, but current models are hand-tuned, assume a fixed circuitry, nonlinear, high dimensional and thus intractable for sensitivity analysis - rendering a computational barrier. We will break these barriers and will construct a tractable data-driven computational model of the DH that enables powerful predictions on how different treatments alter neuronal activity in the DH. State-of-the-art electrophysiological techniques and powerful mouse genetic approaches will delineate the effects of sensory stimuli and stimulation on various subsets of DH neurons, and these data will be used to estimate the parameters and circuit topology of a mechanistic model of the DH. Model reduction will then be applied to generate a tractable characterization of the DH enabling sensitivity analysis. Developing and validating this innovative model will allow predictions that may differentiate various pain treatments and integrative approaches that can be readily tested in animals. RELEVANCE (See instructions): Chronic pain affects about 100 million adults in the US, but remains inadequately treated. Critical to advancing pain therapy is a deeper mechanistic understanding of how a nociceptive signal is processed and modulated in spinal dorsal horn (DH), the first central relay station of nociceptive signaling. We will combine state-of-the-art electrophysiological techniques and mouse genetic approaches with system identification tools to construct a tractable computational model of the DH that will enable powerful predictions on how different treatments alter neuronal activity in the DH.

慢性疼痛影响着美国约 1 亿成年人，且药物治疗效果不佳，而这些药物通常是有毒的 并且有副作用（例如成瘾）。目标神经纤维的电刺激是一种有前途的新方法 疗法，但其疗效不佳且长期成功有限，因为其作用机制是 不清楚。补充疗法，例如针灸和按摩，也涉及神经调节 的作用方式，尚未进行定量评估。推进疼痛治疗的关键是更深层次的治疗 对脊髓背角如何处理和调节伤害性信号的机制理解 (DH)，第一个伤害性信号传导的中央中继站。有 3 个主要功能不同的子集 DH 中的神经元在疼痛传递中发挥不同的作用。兴奋性神经元和抑制​​性神经元 形成重要的局部疼痛回路，调节发送上行疼痛的投射神经元的活动 向大脑发出信号。了解每个神经元亚群的具体作用和治疗方法至关重要 神经刺激、触觉输入和药物的作用。例如，他们对不同的情况有不同的反应吗？ 疗法？某些刺激模式能否选择性地抑制或兴奋任何子集神经元以最大化 疼痛抑制？这些基本问题之前无法轻易地以定量的方式解决。 这项研究。首先，实验障碍限制了探测 DH 来揭示电路拓扑，因为它有 很难区分 DH 神经元的不同亚群，同时研究它们的 生理特性。另一方面，DH 的计算模型可以预测 感觉输入影响疼痛传递，但当前模型是手动调整的，假设固定电路， 非线性、高维，因此难以进行敏感性分析 - 造成计算障碍。 我们将打破这些障碍，构建一个易于处理的数据驱动的 DH 计算模型， 能够有力地预测不同治疗如何改变 DH 中的神经元活动。最先进的 电生理学技术和强大的小鼠遗传方法将描述感觉的影响 DH 神经元各个子集的刺激和刺激，这些数据将用于估计 DH机械模型的参数和电路拓扑。然后将模型简化应用于 生成 DH 的易于处理的表征，从而实现敏感性分析。开发和验证这个 创新模型将允许进行预测，从而区分各种疼痛治疗和综合治疗 可以很容易地在动物身上进行测试的方法。 相关性（参见说明）： 在美国，慢性疼痛影响着大约一亿成年人，但仍未得到充分治疗。至关重要 推进疼痛治疗是对伤害性信号如何处理和处理的更深入的机械理解 在脊髓背角（DH）中进行调节，这是伤害性信号传导的第一个中央中继站。我们将结合 最先进的电生理学技术和具有系统识别的小鼠遗传方法 构建易于处理的 DH 计算模型的工具将能够对如何进行强有力的预测 不同的治疗会改变 DH 中的神经元活动。