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.

慢性疼痛在美国影响了4亿成年人，并且经常用药物治疗，而这些疼痛通常是有毒的 并具有副作用（例如，成瘾）。靶向神经纤维中的电刺激是一种有希望的新的 治疗，但由于其作用机理的机制是次优疗效和长期成功的有限 不清楚。补充疗法，例如针灸和按摩，也涉及神经调节 尚未定量评估的作用方式。对疼痛疗法的前进至关重要 对在脊髓角中如何处理和调节伤害性信号的机械理解 （DH），是伤害性信号传导的第一个中央继电器。有3个主要在功能上不同的子集的 DH中的神经元在疼痛传播中扮演着不同的角色。兴奋性神经元和抑制​​神经元 形成重要的局部疼痛电路，调节投射神经元的活性，这些神经元的活性发送上升疼痛 向大脑的信号。了解每个神经元子集和治疗性的特定作用至关重要 神经刺激，触觉输入和药物的作用。例如，他们对不同的反应不同吗 治疗？某些刺激模式是否可以选择性地抑制或激发任何子集神经元以最大化 疼痛抑制？这些基本问题之前不容易以定量的方式解决 这项研究。首先，实验障碍限制了探测DH以发现电路拓扑的，因为它具有 很难区分DH神经元的不同子集，同时研究其 生理特性。另一方面，DH的计算模型可以预测 感觉输入会影响疼痛的传播，但是当前模型是手工调整的，假设固定电路， 非线性，高维，因此对于灵敏度分析而言是棘手的 - 具有计算屏障。 我们将打破这些障碍，并将构建一个可易于数据驱动的DH的计算模型 关于不同治疗方法如何改变DH中的神经元活性的有力预测。最先进的 电生理技术和强大的小鼠遗传方法将描述感觉的影响 刺激和对DH神经元各种子集的刺激，这些数据将用于估计 DH机械模型的参数和电路拓扑。然后，降低模型将应用于 生成DH启用灵敏度分析的可拖动表征。开发和验证这一点 创新模型将允许预测可以区分各种疼痛治疗和综合性 可以在动物中容易测试的方法。 相关性（请参阅说明）： 慢性疼痛在美国影响约1亿成年人，但治疗不足。对 促进疼痛疗法是对如何处理伤害性信号的更深入的理解，并且 在脊柱背角（DH）中调节，这是伤害感受信号的第一个中央继电器。我们将结合 具有系统识别的最先进的电生理技术和小鼠遗传方法 构建DH的可易换计算模型的工具，该模型将对如何进行有关如何的预测 不同的治疗可以改变DH中的神经元活性。