CRCNS: Towards Pain Control: Synergizing Computational and Biological Approaches

慢性疼痛影响--美国有1亿成年人没有得到充分的药物治疗，这些药物往往是有毒的 并有副作用(如上瘾)。靶向神经纤维的电刺激是一种很有前途的新技术 治疗，但疗效不佳，长期成功有限，因为其作用机制是 不清楚。补充疗法，如针灸和按摩，也涉及神经调节，如 一种行动模式，还没有得到量化评估。推进疼痛治疗的关键是更深层次的 脊髓背角伤害性信号处理和调制机制的研究 (Dh)，第一个伤害性信号的中央中继站。有3个主要的功能截然不同的子集 在痛觉传递中扮演不同角色的神经元。兴奋性神经元和抑制性神经元 形成重要的局部痛觉回路，调节发送上行性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特定作用和治疗是至关重要的 神经刺激、触觉输入和药物的作用。例如，他们对不同的 治疗？特定的刺激模式能否选择性地抑制或兴奋任何子集的神经元以使其最大化 疼痛抑制？这些根本问题以前不能很容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以发现电路拓扑，因为它有 很难区分不同的DH神经元亚群，同时研究它们的 生理特性。另一方面，卫生署的计算模型可以预测 感觉输入影响疼痛的传递，但目前的模型是手动调整的，假设有固定的电路， 非线性、高维，因此很难用于敏感性分析-呈现计算障碍。 我们将打破这些障碍，构建一个易于处理的数据驱动的卫生署计算模型 使强大的预测不同的治疗如何改变神经细胞的活动在脱氢酶。最先进的 电生理技术和强大的小鼠遗传方法将描绘出感觉的影响 刺激和刺激，这些数据将被用来估计 建立了双绞线机械模型的参数和电路拓扑图。然后将模型降阶应用于 生成易于处理的DH特征，以便进行敏感性分析。开发和验证此功能 创新的模式将允许预测，可能会区分各种疼痛治疗和综合 可以很容易地在动物身上测试的方法。 相关性(请参阅说明)： 慢性疼痛在美国影响着大约1亿成年人，但仍然没有得到充分的治疗。至关重要的是 推进疼痛治疗是对伤害性信号是如何处理的更深层次的机械理解 在伤害性信号的第一个中央中继站--脊髓背角(DH)中调制。我们将联合起来 最新的电生理技术和小鼠遗传方法与系统识别 构建一个易于处理的卫生署计算模型的工具，这将使强大的预测如何 不同的治疗方法会改变双侧大脑半球的神经元活动。

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.