Temporal Patterns of Spinal Cord Stimulation

脊髓电刺激（SCS）是一种用于治疗顽固性慢性疼痛的植入式医疗器械。然而， 超过2/3的患者通常经历至少50%的疼痛减轻，尽管几十年的研究， 然而，随着时间的推移，获得临床成功的患者比例并没有增加。我们 追求新的刺激参数维度-刺激的时间模式-以及这一目的 该项目旨在设计和测试优化的刺激时间模式，以提高SCS治疗的疗效 慢性神经性疼痛。我们将使用经验证的基于生物药理学的SCS对感觉神经元影响的模型。 脊髓背角中的信号处理以设计SCS的优化时间模式。的 优化的时间模式旨在利用脊髓背角中抑制机制的动力学， 脊髓SCS产生传递疼痛的脊髓神经元的兴奋和抑制，并且颞叶神经元的兴奋和抑制是由脊髓神经元的兴奋和抑制引起的。 模式将被优化以减弱SCS介导的兴奋，同时放大SCS介导的抑制。我们将 使用灵敏度分析来确定刺激模式对电极中潜在变化的稳健性 定位、刺激的选择性和背角神经网络的生物物理特性，特别是 慢性疼痛中存在的受损抑制机制。随后，我们将测量 SCS对保留神经损伤（SNI）大鼠模型中疼痛相关行为结果的时间模式， 慢性神经性疼痛。我们预计，新的时间模式的SCS将产生更大的抑制， 异常性疼痛和痛觉过敏，以及自发性疼痛，将比频率匹配的控制模式。我们在 计算机和体内电生理数据表明，SCS的时间模式产生了超过 投射神经元放电率的抑制比传统SCS高50%，这表明， 疼痛结果的改善将超过有意义变化的30%阈值。我们还将量化 优化的脊髓刺激时间模式对SNI大鼠脊髓投射神经元活动的影响 慢性疼痛模型我们预计，SCS的优化时间模式将产生更大的减少， 放电率和发条的脊髓宽动态范围和伤害性特异性神经元比频率匹配 控制模式。SCS的时间模式是一个新的和重要的参数，我们将利用扩展 SCS的设计空间从刺激的空间分布到时间模式 刺激.其结果将是评估使用SCS的优化时间模式的可行性， 治疗神经性疼痛，并将为慢性疼痛患者的转化研究提供基础。 重要的是，这种方法具有明确且相对较短的临床转化之路，因为现有的植入物 可以对脉冲发生器进行重新编程以递送SCS的优化时间模式。

Spinal cord stimulation (SCS) is an implanted medical device therapy for refractory chronic pain. However, fewer than 2/3 of patients typically experience at least a 50% reduction in pain, and despite several decades of research and development, the proportion of patients achieving clinical success has not increased over time. We are pursuing a new stimulation parameter dimension – the temporal pattern of stimulation – and the purpose of this project is to design and test optimized temporal patterns of stimulation to improve the efficacy of SCS to treat chronic neuropathic pain. We will use a validated biophysically-based model of the effects of SCS on sensory signal processing in the dorsal horn of the spinal cord to design optimized temporal patterns of SCS. The optimized temporal patterns are intended to exploit the dynamics of inhibitory mechanisms in the dorsal horn of the spinal cord. SCS produces both excitation and inhibition of pain transmitting spinal neurons, and the temporal patterns will be optimized to weaken SCS-mediated excitation while amplifying SCS-mediated inhibition. We will use sensitivity analyses to determine the robustness of stimulation patterns to potential variations in electrode positioning, selectivity of stimulation, and the biophysical properties of the dorsal horn neural network, especially the compromised inhibitory mechanisms present in chronic pain. Subsequently, we will measure the effects of the temporal pattern of SCS on pain-related behavioral outcomes in the spared nerve injury (SNI) rat model of chronic neuropathic pain. We expect that novel temporal patterns of SCS will produce greater suppression of allodynia and hyperalgesia, as well as spontaneous pain, than will frequency-matched control patterns. Our in silico and in vivo electrophysiological data demonstrate that temporal patterns of SCS produced a more than 50% greater suppression of projection neuron firing rates than conventional SCS, and this suggests that resulting improvements in in pain outcomes will exceed the 30% threshold for meaningful change. We will also quantify the effects of optimized temporal pattern of SCS on the activity of spinal cord projection neurons in the SNI rat model of chronic pain. We expect that optimized temporal patterns of SCS will produce greater reductions in firing rates and wind up of spinal wide dynamic range and nociceptive specific neurons than frequency matched control patterns. The temporal pattern of SCS is a novel and important parameter that we will exploit to expand the design space for SCS from the spatial distribution–where–of stimulation to the temporal pattern–when–of stimulation. The outcome will be an assessment of the feasibility of using optimized temporal patterns of SCS to treat neuropathic pain, and will provide the foundation for translational studies in patients with chronic pain. Importantly, this approach has a clear and comparatively short road to clinical translation, as existing implanted pulse generators could be re-programmed to deliver optimized temporal patterns of SCS.