The Pathophysiology of Network Synchrony in Parkinson's Disease

项目总结/摘要 帕金森病（PD）运动症状的生理基础仍不完全清楚。 我们认为，基底神经节丘脑皮质（BGTC）网络活动的动态性质， 对于理解PD的动态病理学和疾病的病理生理学至关重要。我们认为 未能关注和研究BGTC生理学和运动学的非平稳性， 这在很大程度上造成了公布结果的不一致，并阻碍了进展。我们提出并 研究一种新的模型，该模型解释了未充分探索的生理学的时间动态级联 BGTC电机电路中节点之间发生的事件。我们假设，短暂的夸张， 导致信息流受损的网络级耦合触发了神经系统的病理生理和运动后遗症， PD，包括僵硬和运动迟缓，允许并区分病理性和非病理性 同步性我们假设病理性同步导致信息流受损的可能性 取决于“运动”状态，占不成比例的困难与运动开始在PD。我们 还假设治疗（多巴胺能和脑深部电刺激[DBS]）降低了 同步触发的病理级联，在网络中具有一些共同的最终变化（例如，皮质期 振幅耦合），但由于治疗作用的不同部位而具有生理效应的特定差异。 我们将建立在先前成功研究接受DBS植入患者的PD网络生理学的基础上 通过同时评估多个BGTC节点的群体水平活动，包括运动皮质， 背侧运动前皮质（到苍白球接收丘脑区域投射的地方）、丘脑底核（subthalamic nucleus，简称NNA），以及 苍白球（GPi，在不同的患者），与临床症状和行为。我们现在也整合了 单单位生理和同步动态任务来测试我们的模型。在目标1中，我们将建立动态 不同条件下网络同步、局部振荡和病理生理后遗症之间的关系 治疗条件，包括DBS和GPi DBS和多巴胺能治疗。我们假设 在“关闭”状态下同步导致病理后遗症的概率，并检验关于两者的特定假设 不同疗法的共同和独特的生理效应，取决于作用部位。在目标2中， 假设并旨在证明与运动相关大脑状态影响网络同步的后遗症 在生理和行为上，差异性地影响运动开始和正在进行的活动。最后， 在目标3中，我们将区分正常和病理性同步（在治疗和运动条件下） 使用一种新的信息理论框架，重点是关键性，复杂性匹配， 信息流的障碍。这项工作将通过定义 帕金森病中网络同步的病理生理学意义。解决这一差距将有助于治疗 创新，包括识别适应性DBS的信号，并指导药理学创新。

Project Summary/Abstract The physiological underpinning of motor symptoms in Parkinson disease (PD) remains incompletely understood. We propose that the dynamic nature of basal ganglia thalamocortical (BGTC) network activity accounts for and is critical for understanding the dynamic symptomatology of PD and the pathophysiology of disease. We believe that the failure to focus on and investigate the non-stationarity of BGTC physiology and movement kinematics significantly contributes to inconsistency in published results and has impeded progress. We propose and investigate a novel model that accounts for the underexplored temporally dynamic cascade of physiological events occurring between nodes in the BGTC motor circuit. We hypothesize that transient exaggerations in network-level coupling that result in impaired information flow trigger pathophysiological and motor sequelae of PD, including rigidity and bradykinesia, allowing for and differentiating pathological and non-pathological synchrony. We hypothesize that the likelihood of pathological synchrony resulting in impaired information flow depends on the “movement” state, accounting for disproportionate difficulty with movement initiation in PD. We also hypothesize that treatment (dopaminergic and deep brain stimulation [DBS]) decreases the probability of a synchrony-triggered pathological cascade, with some common final changes in the network (e.g., cortical phase amplitude coupling) but with specific differences in physiological effects due to distinct sites of therapeutic action. We will build on prior success of investigating PD network physiology in patients undergoing DBS implantation surgery by simultaneously assessing population level activity from multiple BGTC nodes, including motor cortex, dorsal premotor cortex (to where pallidal-receiving thalamic regions project), subthalamic nucleus (STN), and globus pallidus (GPi, in separate patients), in relation to clinical symptoms and behavior. We now also integrate single unit physiology and synchronized dynamic tasks to test our model. In Aim 1, we will establish the dynamic relationship between network synchronization, local oscillations, and pathophysiologic sequelae under different therapeutic conditions, including STN and GPi DBS and dopaminergic therapy. We hypothesize an increased probability of synchrony leading to pathologic sequelae in the “off” state and test specific hypotheses about both common and distinct physiological effects of the different therapies, depending on site of action. In Aim 2, we hypothesize and aim to demonstrate that movement-related brain states affect sequelae of network synchrony both physiologically and behaviorally, differentially impacting movement initiation and ongoing activity. Finally, in Aim 3, we will distinguish normal and pathologic synchrony (across therapeutic and movement conditions) using a novel information theoretic frameowrk, with a focus on the impact of criticality, complexity matching, and impairments in information flow. This work will enhance the BGTC functional wiring diagram by defining the pathophysiologic significance of network synchrony in PD. Addressing this gap will facilitate therapeutic innovations, including identification of signals for adaptive DBS and to guide pharmacologic innovation.