CRCNS: Hitting the spot: Optimizing Placement of Deep Brain Stimulation Electrode

CRCNS：切中要害：优化深部脑刺激电极的放置

• 批准号: 8301582
• 负责人: Ming L Cheng
• 金额: $ 32.71万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2015-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8301582
• 关键词: Action Potentials; Algorithms; Anatomy; Architecture; Area; Automation; Behavior; Behavioral; Benchmarking; Biological Factors; Biological Models; Biological Neural Networks; Biophysical Process; Brain; Caring; Chronic Disease; Climacteric; Clinical; Code; Collaborations; Communities; Complex; Computer software; Contralateral; Cues; Data; Data Analyses; Data Collection; Data Correlations; Databases; Deep Brain Stimulation; Dependence; Development; Devices; Dorsal; Educational Curriculum; Electrodes; Engineering; Experimental Designs; Family; Family member; Feedback; Frequencies; Functional disorder; Future; Hand; Home environment; Hostage; Human; Implanted Electrodes; Individual; Industry; Joystick; Lateral; Lead; Learning; Linear Models; Location; Manufacturer Name; Measures; Medical Students; Methods; Microelectrodes; Modeling; Motor; Movement; Neuroanatomy; Neurologic; Neurons; Neurosurgeon; Noise; Operative Surgical Procedures; Outcome; Parkinson Disease; Pathology; Patients; Pattern; Pharmaceutical Preparations; Physicians; Physiological; Physiology; Population; Positioning Attribute; Postoperative Period; Principal Investigator; Probability; Procedures; Process; Property; Recording of previous events; Research; Research Personnel; Sampling; Scientist; Shapes; Signal Transduction; Site; Spottings; Statistical Methods; Statistical Models; Structure; Structure of subthalamic nucleus; System; Techniques; Technology; Testing; Thalamic Nuclei; Therapeutic; Training; Treatment Efficacy; Tungsten; Universities; Variant; analog; arm; base; clinical efficacy; computerized data processing; cost; design; digital; extracellular; graduate student; implantation; improved; insight; neural model; neurophysiology; neurosurgery; next generation; programs; relating to nervous system; theories; tool; voltage

DESCRIPTION (provided by applicant): Intellectual Merit: Deep brain stimulation (DBS) is a highly promising therapy for Parkinson's disease (PD). Yet most patients do not get full therapeutic benefit from DBS due to its critical dependence on electrode location, a "sweet spot" in the dorsolateral posterior sensorimotor subunit of the sub-thalamic nucleus (STN), for therapeutic efficacy. PI Cheng was trained at a center where 70% of DBS patients obtained full therapeutic benefit, improving so markedly that they no longer require any PD medications. Such efficacy is atypical even in academic centers because DBS electrode placement is not standardized, scientific, or systematic. We propose to construct a neural modeling, estimation and control framework for STN, which will enable the development of a new surgical tool that will standardize DBS placement: an automated intraoperative closed-loop DBS localization system. Development of this transformative technology requires: 1) neurophysiologic characterization of the "sweet spot". In PD patients, microelectrode recordings will measure single unit spiking activity (action potentials) of STN neurons at different distances from the "sweet spot" and from within it. Point process models will be estimated from this data and will capture complex stochastic relationships between extrinsic (e.g. behavior) and intrinsic (local neural network activity) factors and STN spiking activity. Principled inferential methods will confirm the "sweet spot's" existence and characterize its electrophysiological properties; and computational conductance-based modeling will elucidate the ionic mechanisms underlying the "sweet spot's" physiology. 2) construction of neural estimation and control algorithms for STN DBS. Signal processing and control will derive a robust feature set from STN spiking activity which will reliably predict where the electrode is and will then guide the electrode to the sweet spot. This transformative project requires collaborations between physicians, scientists, mathematicians and engineers with expertise in neurosurgery, neurophysiology, neural signal processing, estimation and modeling, and control theory. For these reasons automation of DBS localization remains largely untapped, giving us the opportunity to lead the scientific development of this next-generation technology. Broader Impact: Due to cost, less than 10% of PD patients worldwide receive DBS. Automating surgical implantation and obviating complex postoperative DBS programming from suboptimal electrode placement would decrease cost, and thus increase patient access. Even greater societal impact, however, would come from improved DBS efficacy, which is life-changing for PD patients. DBS patients of Dr. Cheng have stated that they have been returned to their pre-PD status, and that not just their lives but also the lives of their family members, so long held hostage by a debilitating chronic disease process, have been returned to them. Our proposal attempts to extrapolate these benefits to the larger PD population. Even more importantly, DBS is a nascent procedure holding great promise for many future neurological and psychiatric indications. A technology that improves DBS targeting fidelity and efficacy would hold the potential to improve the lives of millions of patients and their families worldwide. This project will be integrated into curricula in the home and affiliated departments of the PIs. Coursework for signal processing and neuronal spike modeling in the senior undergraduate and graduate levels will gain from our proposal. A graduate level modern control theory course with applications to neural systems will also be developed and offered. Traditional courses in neuroanatomy and neurophysiology will be enhanced by our proposal's insights into the relationships between physiology, anatomy, and function. The PIs also plan to reach out to the academic community by providing representative samples of rare neurophysiological data and analysis code. When cultivated, such a database will provide a platform for investigators around the world to benchmark software algorithms, optimize analog and digital components for new hardware platforms that will process neural signals, and develop a more complete understanding of the mechanisms of DBS. PI Cheng has strong relationships with industry companies including Medtronic, the manufacturer of DBS hardware. We will leverage this to expedite the development and testing of our concept. Our project's outcome may thus have a substantial impact on how DBS systems are designed.

描述（由申请人提供）： 智力优点：深部脑刺激（DBS）是治疗帕金森病（PD）的一种非常有前途的疗法。然而，大多数患者并没有从 DBS 中获得充分的治疗效果，因为它的治疗效果严重依赖于电极位置，即丘脑下核 (STN) 背外侧后感觉运动亚单位的“最佳点”。 PI Cheng 在一家中心接受培训，70% 的 DBS 患者获得了充分的治疗效果，改善非常显着，以至于他们不再需要任何 PD 药物。即使在学术中心，这种功效也是不典型的，因为 DBS 电极放置不标准化、科学或系统。我们建议构建 STN 的神经建模、估计和控制框架，这将有助于开发一种标准化 DBS 放置的新手术工具：自动化术中闭环 DBS 定位系统。这种变革性技术的开发需要：1）“最佳点”的神经生理学特征。在 PD 患者中，微电极记录将测量距“最佳点”不同距离及其内部的 STN 神经元的单个单位尖峰活动（动作电位）。点过程模型将根据这些数据进行估计，并将捕获外部（例如行为）和内部（局部神经网络活动）因素与 STN 尖峰活动之间的复杂随机关系。有原则的推理方法将确认“最佳点”的存在并表征其电生理特性；基于计算电导的建模将阐明“最佳点”生理学背后的离子机制。 2）STN DBS神经估计和控制算法的构建。信号处理和控制将从 STN 尖峰活动中获得强大的特征集，该特征集将可靠地预测电极的位置，然后引导电极到达最佳位置。这一变革性项目需要具有神经外科、神经生理学、神经信号处理、估计和建模以及控制理论专业知识的医生、科学家、数学家和工程师之间的合作。由于这些原因，DBS 本地化自动化在很大程度上尚未开发，这使我们有机会引领这一下一代技术的科学发展。更广泛的影响：由于成本原因，全球只有不到 10% 的 PD 患者接受 DBS。自动化手术植入并避免因电极放置不理想而进行复杂的术后 DBS 编程将降低成本，从而增加患者的治疗机会。然而，更大的社会影响将来自 DBS 疗效的提高，这将改变 PD 患者的生活。郑医生的 DBS 患者表示，他们已经恢复到 PD 前的状态，不仅是他们的生命，还有长期受慢性疾病折磨的家人的生命。我们的提案试图将这些益处推广到更多的 PD 人群。更重要的是，DBS 是一种新兴的手术，对于未来的许多神经系统和精神疾病适应症具有广阔的前景。一项提高 DBS 目标保真度和功效的技术将有可能改善全球数百万患者及其家人的生活。该项目将被纳入 PI 所在部门和附属部门的课程中。高年级本科生和研究生的信号处理和神经元尖峰建模课程将从我们的提案中受益。还将开发和提供研究生水平的现代控制理论课程及其在神经系统中的应用。我们的提案对生理学、解剖学和功能之间关系的见解将增强神经解剖学和神经生理学的传统课程。 PI 还计划通过提供罕见神经生理学数据和分析代码的代表性样本来接触学术界。一旦建立起来，这样的数据库将为世界各地的研究人员提供一个平台，以对软件算法进行基准测试，优化用于处理神经信号的新硬件平台的模拟和数字组件，并更全面地了解 DBS 的机制。 PI Cheng 与 DBS 硬件制造商美敦力 (Medtronic) 等行业公司有着密切的关系。我们将利用这一点来加快我们概念的开发和测试。因此，我们的项目结果可能会对 DBS 系统的设计方式产生重大影响。