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 虽然许多研究致力于了解帕金森病（PD）的病理生理学， 运动体征表现背后的神经动力学仍不清楚。过去两年的研究 几十年来，已经显示出β波段振荡的振幅和发生率之间的相关性， 丘脑底核（ESTA）和运动迟缓和僵硬的变化介导的左旋多巴或脑深部 刺激（DBS）疗法。然而，没有任何研究结论性或演绎性地证明了因果关系。一 建立因果关系的局限性是缺乏能够预测和 实时精确控制人脑中的神经振荡活动而不引入混淆 因素建立这些工具，并阐明β带振荡与PD电机的关系 症状是因果关系或偶发现象是更好地了解PD病理生理学和进展的关键步骤 个性化DBS技术在PD和其他条件。该项目旨在解决这些技术和 通过利用反馈控制工程和患者特定的计算建模工具来消除知识差距。 我们将采用我们小组开发的一种新的神经控制方法（诱发干扰闭环DBS， eiDBS），以表征控制抑制或放大β振荡的程度， 帕金森病患者的运动迟缓和僵硬（Specific Aim 1，SA 1）。在SA 2中，我们将使用左旋多巴药物 为了表征运动迟缓和刚性的变化如何与振幅，固有频率， 和初级运动皮层（MC）神经反应的共振。的 来自SA 2的结果将帮助我们更好地理解与PD相关的固有电路动态， 确定优化闭环DBS算法的策略（例如，eiDBS）在面对并发左旋多巴 治疗，这是将这项技术推向临床试验的必要步骤。结合电生理数据和高- 分辨率（7 T）磁共振（MR）成像和计算建模，我们将确定哪些特定的 需要激活与该神经元连接的神经通路以引起频率特异性神经反应 在主动脉和MC（SA 3）。来自SA 3的数据将阐明哪些子电路参与生成 刺激诱发和自发β振荡的PD，并告知我们如何使用定向DBS 导致在MEMS中形成电场以经由eiDBS选择性地调制MEMS或MC。我们将解决 该项目的三个目标是25名在颅内植入DBS电极导线的PD患者，其DBS电极导线延伸导线 将被外部化并连接到我们的记录和闭环刺激基础设施。这个项目是 与NINDS帕金森病2014年研究建议保持一致，因为我们“使用组合 的传感器技术和成像，以发展一个更精确的理解神经回路动态， PD能够开发下一代治疗设备。”