Algorithms for programming DBS systems for Essential Tremor

用于特发性震颤 DBS 系统编程的算法

• 批准号: 10447129
• 负责人: NOAM HAREL
• 金额: $ 53.65万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-30 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10447129
• 关键词: Acute; Adult; Affect; Age; Algorithms; Anatomy; Animal Model; Area; Ataxia; Bilateral; Brain; Brain region; Cell Nucleus; Cells; Cerebellar Cortex; Cerebellar Nuclei; Clinical; Computer Models; Deep Brain Stimulation; Dentate nucleus; Diffusion Magnetic Resonance Imaging; Dysarthria; Electric Stimulation; Electrodes; Electrophysiology (science); Essential Tremor; Fiber; Frequencies; Functional Magnetic Resonance Imaging; Funding; Grant; Harmaline; Histologic; Human; Impairment; Implant; Individual; Intention Tremor; Lateral; Lead; Magnetic Resonance Imaging; Maps; Medial; Methods; Modeling; Motor; Movement Disorders; Neural Pathways; Neurons; Output; Paresthesia; Pathway interactions; Patients; Pharmaceutical Preparations; Physiologic pulse; Population; Posture; Primates; Radiation; Refractory; Research; Source; Symptoms; System; Technology; Testing; Thalamic structure; Therapeutic; Therapeutic Effect; Thinness; Time; Translating; Treatment Efficacy; Tremor; United States; Utah; Work; arm; automated algorithm; base; deep brain stimulation array; density; design; electric field; follow-up; functional outcomes; improved; insight; instrument; machine learning algorithm; magnetic field; motor symptom; nonhuman primate; novel; particle; pre-clinical; prospective; research study; side effect; somatosensory; targeted treatment; tool; zona incerta

PROJECT SUMMARY AND ABSTRACT Essential tremor (ET) is the most common movement disorder in the United States, affecting 4% of all adults over the age of 40. For individuals whose motor symptoms are refractory to medication and significantly impair their daily living, deep brain stimulation (DBS) is considered to be the only bilateral therapeutic option. Despite recent advances in DBS technology, a significant portion of ET patients with DBS implants will receive inadequate tremor control because of poorly placed DBS leads, while others will lose efficacy of the therapy after 1-2 years due in part to inflexible neurostimulator programming options. There is a strong and growing clinical need for implantable DBS lead designs and programming algorithms that can enable clinicians to better sculpt electric fields within the brain, especially in cases where stimulation through a poorly placed DBS lead results in low-threshold side-effects. Our proposed study will integrate high-field magnetic resonance imaging, histological neurotracing of fiber pathways, computational modeling of DBS, and single-cell electrophysiology methods to further develop and experimentally-validate a novel semi-automated machine learning algorithm that facilitates hypothesis-driven determination of subject-specific neurostimulator settings through directional DBS leads. Specifically, we will: 1) identify the neural pathways involved in the reduction of action and postural tremor using directional DBS leads and a novel particle swarm optimization algorithm based on subject-specific anatomy; 2) quantify how tremor-related information is modulated on the single-cell, population, and network levels by therapeutic DBS in a preclinical large-animal model of harmline-induced tremor; and 3) investigate how therapeutic windows (i.e. the threshold difference between postural and action tremor abolishment and side effect emergence) change over time with human DBS therapy targeting one or more pathways within the cerebello-thalamoc-cortical network. Together, this project will (a) experimentally evaluate and translate a novel DBS programming algorithm to human ET patients, (b) provide a much more detailed map of the neural pathways underlying the therapeutic effects of DBS (on postural and action tremor) and side effects of DBS (on dysarthria, paresthesia, ataxia), (c) rigorously investigate how DBS for treating tremor works mechanistically at the single cell and network levels within the brain, and (d) probe the neural pathways involved in the worsening of tremor symptoms for ET patients over time.

项目总结和摘要 特发性震颤（ET）是美国最常见的运动障碍，影响4%的成年人 超过40岁对于运动症状对药物治疗无效且严重损害 在他们的日常生活中，脑深部电刺激（DBS）被认为是唯一的双侧治疗选择。尽管 DBS技术的最新进展，植入DBS植入物的ET患者的很大一部分将接受 由于DBS电极导线放置不当导致震颤控制不充分，而其他患者将失去治疗效果 1-2年后，部分原因是神经刺激器程控选项不灵活。有一个强大的和不断增长的 临床需要植入式DBS电极导线设计和编程算法，使临床医生能够更好地 在大脑中塑造电场，特别是在通过放置不当的DBS电极导线进行刺激的情况下 导致低阈值的副作用。我们提议的研究将整合高场磁共振成像， 纤维通路的组织学神经示踪、DBS的计算建模和单细胞电生理学 进一步开发和实验验证新型半自动机器学习算法的方法 通过定向，便于假设驱动确定受试者特定神经刺激器设置 DBS电极导线。具体来说，我们将：1）确定参与减少行动和姿势的神经通路 震颤使用定向DBS导线和一种新的粒子群优化算法的基础上， 解剖学; 2）量化震颤相关信息如何在单细胞、群体和网络上调制 治疗性DBS在临床前大动物模型中诱导震颤的水平;以及3）研究 如何治疗窗口（即姿势和动作震颤消除之间的阈值差， 副作用出现）随时间的变化， 小脑-丘脑-皮质网络总之，这个项目将（a）实验性地评估和翻译一个 新的DBS编程算法，用于人类ET患者，（B）提供了更详细的神经电生理图。 DBS的治疗作用（对姿势性和动作性震颤）和DBS的副作用（对 构音障碍、感觉异常、共济失调），（c）严格研究DBS治疗震颤的机制， 脑内的单细胞和网络水平，以及（d）探测参与恶化的神经通路。 的震颤症状。