Identifying Circuit Dynamics Underlying Motor Dysfunction in Parkinsons Disease Using Real-Time Neural Control

使用实时神经控制识别帕金森病运动功能障碍背后的电路动力学

• 批准号: 10734559
• 负责人: David Escobar Sanabria
• 金额: $ 63.59万
• 依托单位: CLEVELAND CLINIC LERNER COM-CWRU
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10734559
• 关键词: Address; Affect; Algorithms; Animal Model; Basal Ganglia; Bradykinesia; Brain; Clinical; Clinical Trials; Computer Models; Data; Dedications; Deep Brain Stimulation; Development; Devices; Electric Stimulation; Electroencephalography; Electrophysiology (science); Elements; Engineering; Etiology; Feedback; Frequencies; Functional disorder; Future; Generations; Health Care Costs; Human; Image; Implant; Incidence; Infrastructure; Knowledge; Lead; Levodopa; Link; Magnetic Resonance Imaging; Measures; Mediating; Modeling; Motor; Motor Cortex; Motor Manifestations; Movement; National Institute of Neurological Disorders and Stroke; Neurons; Parkinson Disease; Parkinsonian Disorders; Pathway interactions; Patients; Persons; Pharmaceutical Preparations; Physiology; Population; Predisposition; Process; Recommendation; Research; Resolution; Role; STN stimulation; Severities; Shapes; Structure of subthalamic nucleus; System; Techniques; Technology; Time; Variant; alternative treatment; brain circuitry; clinical translation; clinically relevant; electric field; insight; motor disorder; nervous system disorder; neural; neural circuit; neurophysiology; neuroregulation; novel therapeutics; programmed cell death protein 1; response; sensor; sensor technology; theories; tool; treatment optimization

PROJECT SUMMARY While much research has been dedicated to understanding the pathophysiology of Parkinson’s disease (PD), the neural dynamics underlying the manifestation of motor signs remain unclear. Studies over the past two decades have shown a correlation between the amplitude and incidence of beta band oscillations in the subthalamic nucleus (STN) and changes in bradykinesia and rigidity mediated by levodopa or deep brain stimulation (DBS) therapies. Yet, no study has conclusively or deductively demonstrated a causal link. A limitation to establishing causality is the lack of available neuromodulation tools capable of predictably and precisely controlling neural oscillatory activity in the human brain in real-time without introducing confounding factors. Establishing these tools and clarifying whether the relationship of beta band oscillations with PD motor signs is causal or epiphenomenon are critical steps to better understand PD pathophysiology and advance personalized DBS technology in PD and other conditions. This project aims to address these technology and knowledge gaps by leveraging feedback control engineering and patient-specific computational modeling tools. We will employ a new neural control approach developed in our group (evoked interference closed-loop DBS, eiDBS) to characterize the degree by which controlled suppression or amplification of beta oscillations in the STN influences bradykinesia and rigidity in PD (Specific Aim 1, SA1). In SA2, we will employ levodopa medication to characterize how changes in bradykinesia and rigidity relate to variations in the amplitude, natural frequency, and resonance of neural responses in the STN and primary motor cortex (MC) evoked by STN stimulation. The results from SA2 will help us gain a greater understanding of intrinsic circuit dynamics associated with PD and identify strategies to optimize closed-loop DBS algorithms (e.g., eiDBS) in the face of concurrent levodopa therapy, a necessary step to bring this technology to clinical trials. Combining electrophysiological data with high- resolution (7T) magnetic resonance (MR) imaging and computational modeling, we will identify which specific neuronal pathways connected with the STN need to be activated to evoke frequency-specific neural responses in the STN and MC (SA3). The data from SA3 will shed light on which sub-circuits are involved in the generation of stimulation-evoked and spontaneous beta oscillations in PD, and inform how we can use directional DBS leads to shape electric fields in the STN to selectively modulate the STN or MC via eiDBS. We will address the three aims of this project with 25 PD patients implanted with DBS leads in the STN, whose DBS lead extensions will be externalized and connected to our recording and closed-loop stimulation infrastructure. This project is well aligned with the NINDS Parkinson’s Disease 2014 Research Recommendations, as we “use a combination of sensor technologies and imaging to develop a more precise understanding of the neural circuit dynamics in PD to enable the development of next-generation therapeutic devices.”

项目摘要 尽管很多研究都致力于了解帕金森氏病（PD）的病理生理学，但 运动迹象表现表现为基础的神经动力学尚不清楚。过去两个研究 几十年来显示了β带振荡的放大器和在 丘脑下核（STN）以及左旋多巴或深脑介导的僵硬的变化和刚性的变化 刺激（DBS）疗法。然而，尚无最终或演绎的因果关系。一个 限制建立卡理的是缺乏可预测和的可预测性神经调节工具 精确控制人脑中的神经振荡活性，而无需引入混杂 因素。建立这些工具，并阐明Beta频段振荡与PD电机之间的关系是否 迹象是因果或epiphenomenon是更好地理解PD病理生理学和进步的关键步骤 PD和其他条件下的个性化DBS技术。该项目旨在解决这些技术和 通过利用反馈控制工程和特定于患者的计算建模工具来实现知识差距。 我们将采用我们小组中开发的新神经控制方法（诱发干扰闭环DB， EIDB）表征控制抑制或扩增β振荡的程度 STN影响PD的铁趋化度和刚度（特定AIM 1，SA1）。在SA2中，我们将使用左旋多巴药物 为了表征黄铁菌的变化以及与放大器的变化，固有频率的变化有关的如何变化 STN刺激引起的STN和原发性运动皮层（MC）中神经反应的共振。这 SA2的结果将有助于我们对与PD相关的内在电路动力学有更深入的了解 确定优化闭环DBS算法（例如EIDB）的策略 治疗，这是将该技术带入临床试验的必要步骤。将电生理数据与高 分辨率（7T）磁共振（MR）成像和计算建模，我们将确定哪个特定 与STN相关的神经元途径需要激活以引起频率特异性神经元反应 在STN和MC（SA3）中。来自SA3的数据将阐明该一代涉及哪些亚电路 PD中的刺激诱发和赞助的β振荡，并告知我们如何使用定向DBS 导致STN中的电场形成电场，通过EIDB选择性调节STN或MC。我们将解决 该项目的三个目标是25名PD患者，其中植入了DBS Lead在STN中，其DBS铅扩展 将被外部化并连接到我们的记录和闭环模拟基础架构。这个项目是 与Ninds帕金森氏病2014年的研究建议非常吻合，因为我们“结合了 传感器技术和成像，以更精确地了解神经电路动态 PD可以开发下一代治疗设备。”