CRCNS: Towards Pain Control: Synergizing Computational and Biological Approaches

在美国，慢性疼痛影响着约1亿成年人，并且药物治疗不足，这些药物通常是有毒的 并且具有副作用（例如，成瘾）。靶向神经纤维的电刺激是一种有前途的新方法。 治疗，但具有次优疗效和有限的长期成功，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， 一种作用方式，尚未进行定量评估。推进疼痛治疗的关键是更深层次的 理解伤害性信号在脊髓背角中的加工和调制机制 (DH)是第一个伤害性信号传递的中枢中继站。有3个主要的功能不同的子集， 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，调节传递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是至关重要的。 神经刺激、触觉输入和药物的作用。例如，他们是否对不同的 治疗？某些刺激模式能否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在过去很难以定量的方式加以解决。 本研究首先，实验障碍限制了探测DH以揭示电路拓扑，因为它 很难区分DH神经元的不同子集，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，但目前的模型是手动调整的，假设一个固定的电路， 非线性，高维，因此难以进行灵敏度分析-造成计算障碍。 我们将打破这些障碍，并将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的治疗如何改变DH中的神经元活动。State-of-the-art 电生理学技术和强大的小鼠遗传学方法将描绘感觉的影响， 刺激和刺激的DH神经元的各种子集，这些数据将用于估计 DH的机械模型的参数和电路拓扑。然后将模型降阶应用于 生成能够进行敏感性分析的DH的易处理的表征。开发和验证此 创新模型将允许进行预测， 这些方法很容易在动物身上进行测试。 相关性（参见说明）： 在美国，慢性疼痛影响着大约1亿成年人，但仍然没有得到充分的治疗。的关键 推进疼痛治疗是对伤害性信号如何处理的更深入的机械理解， 在脊髓背角（DH）中调制，脊髓背角是伤害性信号传导的第一个中枢中继站。我们将联合收割机 最先进的电生理技术和具有系统识别的小鼠遗传方法 工具来构建一个易于处理的DH计算模型，这将使强大的预测如何 不同的处理改变了DH中的神经元活性。

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.