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: 9816407
• 负责人: NOAM HAREL
• 金额: $ 58.37万
• 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/9816407
• 关键词: Acute; Adult; Affect; Age; Algorithms; Anatomy; Animal Model; Area; Ataxia; Bilateral; Brain; Brain region; Cell Nucleus; Cells; Cerebellar Nuclei; Cerebellar cortex structure; Clinical; Computer Simulation; 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; 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.

项目概要和摘要 特发性震颤 (ET) 是美国最常见的运动障碍，影响 4% 的成年人 40岁以上。对于运动症状对药物无效且严重损害的个体 对于他们的日常生活来说，深部脑刺激（DBS）被认为是唯一的双边治疗选择。尽管 DBS 技术的最新进展，很大一部分植入 DBS 的 ET 患者将接受 由于 DBS 导线放置不当而导致震颤控制不足，而其他人则会失去治疗效果 1-2年后，部分原因是神经刺激器编程选项不灵活。有一种强大且不断成长的力量 临床需要植入式 DBS 导联设计和编程算法，使临床医生能够更好地 塑造大脑内的电场，特别是在通过位置不当的 DBS 导线进行刺激的情况下 导致低阈值副作用。我们提出的研究将整合高场磁共振成像， 纤维通路的组织学神经追踪、DBS 计算模型和单细胞电生理学 进一步开发和实验验证新型半自动机器学习算法的方法 通过定向促进假设驱动的特定受试者神经刺激器设置的确定 星展银行领先。具体来说，我们将：1）确定参与减少动作和姿势的神经通路 使用定向 DBS 导联和基于特定主题的新型粒子群优化算法进行震颤 解剖学; 2）量化震颤相关信息在单细胞、群体和网络上的调节方式 在骆驼蓬碱诱发震颤的临床前大型动物模型中通过治疗性 DBS 达到的水平； 3）调查 治疗窗口（即姿势性震颤和动作性震颤消除之间的阈值差异）如何 副作用的出现）随着时间的推移而变化，人类 DBS 疗法针对的是体内的一种或多种途径。 小脑-丘脑-皮质网络。该项目将共同（a）通过实验评估和翻译 为人类 ET 患者提供新颖的 DBS 编程算法，(b) 提供更详细的神经网络图 DBS（对姿势性震颤和动作性震颤）的治疗效果和 DBS 副作用（对姿势性震颤和动作性震颤）的潜在途径 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 大脑内的单细胞和网络水平，以及（d）探测与恶化有关的神经通路 ET 患者随时间的震颤症状。