Temporal Patterns of Deep Brain Stimulation

 描述(申请人提供)：慢性高频脑部电刺激，称为脑深部刺激(DBS)，已成为一种治疗运动障碍的成熟疗法，包括特发性震颤和帕金森氏病(PD)。尽管DBS的临床益处已经有了很好的证明，但关于作用机制的基本问题仍然存在，这种缺乏了解将限制这一有希望的治疗方法的充分发展和优化。DBS的特点之一是症状缓解对刺激频率的强烈依赖。高频DBS(&gt；100赫兹)可以缓解运动障碍的症状，而低频刺激(&lt；50赫兹)通常并不是无效的。在之前的赠款期间，我们确定DBS的效果也强烈依赖于刺激的时间模式。现在，我们试图利用这一发现-DBS的效果强烈依赖于刺激的时间模式-既可以了解神经活动的时间模式与帕金森病运动症状之间的关系，也可以通过设计新的最佳刺激时间模式来提高DBS的有效性和效率。我们将在帕金森病的动物模型中结合计算建模、量化行为和单单位神经记录，以及在人类和帕金森病患者中的翻译实验，以促进对DBS的理解和应用。首先，我们将测量DBS的渐进性时间模式对震颤和运动迟缓的影响，旨在产生以θ频率或β频率振荡为代表的神经，并确定这些神经活动的时间模式与帕金森病运动症状之间的因果关系。其次，我们将使用基于模型的优化设计新的时间刺激模式，旨在最大限度地抑制theta和beta频段的异常同步振荡，并测量具有这些模式的DBS对帕金森病大鼠模型以及帕金森病和STN DBS患者震颤和运动迟缓的影响。第三，我们将测量DBS的频率和时间模式对基底节和皮质神经活动的影响。我们将使用创新的硬件，能够在DBS应用期间记录局部场电位，并将帕金森病患者和STN DBS患者以及帕金森病大鼠模型的神经振荡活动的变化与症状变化联系起来。DBS的时间模式是一个新的和重要的参数，我们将利用它来了解神经活动模式与PD运动症状之间的关系，并作为一种新的方法来提高DBS的疗效和效率。这项研究的结果将有助于了解神经元活动模式与运动障碍症状之间的关系，有助于改进DBS对帕金森症状的治疗，并揭示DBS的作用机制。

 DESCRIPTION (provided by applicant): Chronic high-frequency electrical stimulation of the brain, called deep brain stimulation (DBS), has emerged as a well-established therapy for the treatment of movement disorders, including essential tremor and Parkinson's disease (PD). Although the clinical benefits of DBS are well documented, fundamental questions remain about the mechanisms of action, and this lack of understanding will limit the full development and optimization of this promising treatment. One of the hallmarks of DBS is the strong dependence of symptom relief on the frequency of stimulation. High frequency DBS (> 100 Hz) relieves the symptoms of movement disorders, while low frequency stimulation (< 50 Hz) is generally not ineffective. During the prior grant period we established that the effects of DBS are also strongly dependent on the temporal pattern of stimulation. Now, we seek to exploit this finding - that the effects of DBS are strongly dependent on the temporal pattern of stimulation - both to understand the relationship between temporal patterns of neural activity and the motor symptoms of PD and to improve the effectiveness and efficiency of DBS through the design of novel optimal temporal patterns of stimulation. We will combine computational modeling, quantitative behavior and single unit neural recording in an animal model of PD, and translational experiments in humans with PD to advance both the understanding and application of DBS. First, we will measure the effects on tremor and bradykinesia of symptogenic temporal patterns of DBS, designed to generate neural typified by either theta-frequency or beta-frequency oscillations, and determine the causality between these temporal patterns of neural activity and the motor symptoms of PD. Second, we will use model-based optimization to design novel temporal patterns of stimulation intended to suppress maximally the abnormal synchronous oscillations in the theta- and beta-frequency bands, and measure the effects of DBS with these patterns on tremor and bradykinesia in a rat model of PD and in persons with PD and STN DBS. Third, we will measure the effects of the frequency and temporal pattern of DBS on neural activity the basal ganglia and cortex. We will use innovative hardware that enables recording of local field potentials during the application of DBS and correlate the changes in neural oscillatory activity with changes in symptoms in persons with PD and STN DBS, as well as in a rat model of PD. The temporal pattern of DBS is a novel and important parameter that we will exploit, both to understand the relationship between the patterns of neural activity and motor symptoms of PD, and as a novel way to improve the efficacy and efficiency of DBS. The outcomes of the proposed research will contribute to understanding the relationship between patterns of neuronal activity and the symptoms of movement disorders, to improving the treatment of Parkinsonian symptoms with DBS, and to uncovering the mechanisms of action of DBS.