Pathophysiology-based approaches to deep brain stimulation for Parkinson's disease

摘要（项目1） 帕金森病（PD）是一种进行性神经退行性疾病，影响全球1000多万人， 宽它可以是一种使人衰弱的疾病，尽管研究了几十年，但基础脑组织中的生理变化 丘脑皮质神经节（BGTC）回路，其发展的基础仍然是有争议的。脑深部电刺激 (DBS)丘脑底核（subthalamic nucleus，简称STN）和内苍白球（internal globus pallidus，简称GPi）的毁损是一种高效的治疗方法， 然而，对于许多PD患者来说，结果是高度可变的，可能与认知功能有关。 在一些病人中妥协。为了推进帕金森病的DBS治疗，我们需要更深入地了解当地的 和网络范围的电路动态及其与运动信号和认知功能的关系。这 理解将提供优化的基本原理，以特定区域内的GPi DBS 以及基于患者运动体征和认知特征的患者特定DBS的开发。 本研究的目的是促进我们对BGTC（皮质下-皮质）的作用的理解， 皮质-皮质回路在PD发展中的作用，DBS和L-多巴发生的变化，以及 利用这一认识来推进当前的DBS治疗方法并开发新的DBS治疗方法。我们将 在更广泛的BGTC内定义同步振荡、相干性和连通性之间的关系 PD运动神经元的发育与运动神经回路（运动神经元、GPi、感觉神经元、运动神经元、运动前区和背外侧前额叶皮质）的关系 体征，定义其在运动表现（SA 1，2）、认知功能（SA 1，2，3）和相应变化中的作用 DBS、L-多巴和DBS+ L-多巴（SA 2）。通过定义静态连接模式的强度和方向 在运动过程中，我们将描述BGTC网络中各个回路的作用，并定义它们的 在运动表现和认知功能中各自的作用，为未来的发展铺平了道路， DBS的优化算法，利用这种理解（SA 1，2，3，4）。通过关联程度 多个单细胞和局部场电位（LFP）活动之间的一致性，我们还将推进我们的 理解尖峰相位锁定对运动信号发展的作用。通过高分辨率 通过影像学技术和包裹分析，我们将确定DBS的最佳部位， (SA2 3，4）将运动和认知结果与生物标志物活性和导联位置相关联，导致患者- 特定DBS和基于每个患者表型的自动编程算法的开发， 铅位置。将使用定向DBS电极导线、多个独立电流 对照（MICC）器械，高分辨率成像和电生理记录， 患者术后接受微电极标测（SA 1，3）的皮质电图（ECoG）阵列 使用ECoG阵列和外部化导联（SA 2，3），并遵循DBS参数的优化（SA 4）。

ABSTRACT (Project 1) Parkinson’s disease (PD) is a progressive neurodegenerative disease affecting over 10 million people world- wide. It can be a debilitating disorder and although studied for decades the physiological changes in the basal ganglia thalamocortical (BGTC) circuit that underlie its development remain under debate. Deep brain stimulation (DBS) of the subthalamic nucleus (STN) and internal globus pallidus (GPi) has been a highly effective therapy for many patients with PD, however, the results have been highly variable and may be associated with cognitive compromise in some patients. To advance DBS therapies for PD we require a deeper understanding of the local and network-wide circuit dynamics and their relationship to motor signs and cognitive function. This understanding will provide the rationale for optimizing STN and GPi DBS, targeting specific regions within the STN and GPi, and development of patient-specific DBS based on the patients’ motor signs and cognitive profile. The goals of this study are to advance our understanding of the role of BGTC (subcortical-cortical) and cortical-cortical circuits in the development of PD, the changes that occur with DBS and L-dopa, and to use this understanding to advance current and develop new DBS approaches for its treatment. We will define the relationship between synchronized oscillations, coherence and connectivity within the broader BGTC circuit (STN, GPi, sensory, motor, premotor and dorsolateral prefrontal cortices) to the development of PD motor signs, define their role in motor performance (SA1,2), cognitive function (SA1,2,3), and corresponding changes with DBS, L-dopa and DBS+L-dopa (SA2). By defining the strength and direction of connectivity patterns at rest and during movement we will characterize the role of individual circuits within the BGTC network and define their respective roles in motor performance and cognitive function paving the way for future development of optimization algorithms for DBS that take advantage of this understanding (SA1,2,3,4). By correlating the degree of coherence between multiple single cells and local field potential (LFP) activity we will also advance our understanding of the role of spike-phase locking to the development of motor signs. Through high resolution imaging techniques and parcellation analyses we will define the optimal site for DBS within the STN and GPi (SA2,3,4) correlating motor and cognitive outcomes to biomarker activity and lead location, leading to patient- specific DBS and development of automated programming algorithms based on each patient’s phenotype and lead location. The proposed aims will be conducted using directional DBS leads, multiple independent current controlled (MICC) devices, high resolution imaging and electrophysiological recordings in PD patients with electrocorticography (ECoG) arrays undergoing microelectrode mapping (SA1,3), postoperatively in patients with ECoG arrays and externalized leads (SA2,3), and following optimization of DBS parameters (SA4).