Spatiotemporal Optimization of Deep Brain Stimulation for Parkinson's Disease

项目摘要和摘要 基底节有丰富的躯体解剖和功能地形图，由以下运动亚路组成 被认为对帕金森病(PD)的病理生理学至关重要并成功应用 深部脑刺激疗法(DBS)治疗帕金森病的每一个主要运动体征。有很强的临床性 需要更好地了解这些过程，进而利用它们来提供量身定制的治疗 患者自己的症状和运动控制需要随时进行。这个项目将 研究DBS设置的时空优化如何以不同方式影响神经通路激活 通过大脑的运动控制网络，以及这些结果如何转化为改善四个基本的 帕金森病的运动体征(运动迟缓、僵硬、震颤和姿势不稳)。目标1将调查单人 小区、集合和网络级别DBS的时空参数如何影响信息传输， 更重要的是，运动控制网络如何能够在没有信息的情况下产生自然的动作 高频刺激引起的损伤。AIM 2将开发和应用贝叶斯对偶自适应控制 研究时空DBS设置对电诱发复合动作电位影响的算法 以及这些特征是如何映射到调制单个运动信号上的。目标3将利用地面事实 来自DBS现场和DBS内高密度微电极阵列记录的电生理数据 用于验证神经通路激活计算模型中关键参数的运动控制网络 接受星展银行治疗。拟议的研究整合了创新的高密度微电极阵列记录， 闭环优化算法、微米分辨率解剖路径成像和特定对象 星展银行的计算模型。综上所述，这项计划将加强我们对脑血管疾病病理生理学的了解。 并为翻译下一代个性化和响应性DBS疗法提供关键数据。

PROJECT SUMMARY AND ABSTRACT The basal ganglia have a rich somatotopy and functional topography composed of motor subcircuits that are thought to be critically important to the pathophysiology of Parkinson's disease (PD) and successful application of deep brain stimulation therapy (DBS) for managing each cardinal motor sign of PD. There is a strong clinical need to better understand these processes and in turn harness them to deliver therapy that is tailored to a patient's own symptomatology and motor control needs on a moment by moment basis. This project will investigate how spatiotemporal optimization of DBS settings can differentially affect neural pathway activation through the brain's motor control network and how those results translate to improving each of the four cardinal motor signs of PD (bradykinesia, rigidity, tremor, and postural instability). Aim 1 will investigate at the single cell, ensemble, and network levels how spatiotemporal parameters of DBS influence information transmission, and critically how the motor control network is able to produce naturalistic movements despite information lesions induced by high-frequency stimulation. Aim 2 will develop and apply a Bayesian Dual Adaptive Control algorithm to investigate how spatiotemporal DBS settings affect electrically-evoked compound action potentials and how those features map onto modulating individual motor signs. Aim 3 will leverage the ground-truth electrophysiological data from high-density microelectrode array recordings at the site of DBS and within the motor control network to validate key parameters used in computational models of neural pathway activation with DBS therapy. The proposed study integrates innovative high-density microelectrode array recordings, closed-loop optimization algorithms, micron-resolution anatomical pathway imaging, and subject-specific computational models of DBS. Together, this project will enhance our understanding of the pathophysiology of PD and provide critical data towards translating next generation personalized and responsive DBS therapies.