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.

项目摘要和摘要 基本震颤（ET）是美国最常见的运动障碍，影响了所有成年人的4％ 超过40岁。对于运动症状对药物的难治性且严重损害的个体 他们的日常生活，深脑刺激（DBS）被认为是唯一的双边治疗选择。尽管 DBS技术的最新进展，大部分DBS植入物的ET患者将接受 由于位置较差的DBS导线，震颤控制不足，而其他人会失去治疗的功效 1 - 2年后，部分原因是僵化的神经刺激器编程选项。有一个强大而增长的 对可植入的DBS铅设计和编程算法的临床需求，可以使临床医生更好 雕刻大脑内的电场，尤其是在刺激不良的DBS铅的情况下 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， 纤维途径的组织学神经接触，DBS的计算建模和单细胞电生理学 进一步开发和实验估算的方法，新型的半自动化机器学习算法 这有助于假设驱动的主体特异性神经刺激器设置的确定 DBS铅。具体而言，我们将：1）确定降低动作和姿势的神经途径 使用定向DBS导线和基于主题特异性的新型粒子群优化算法的震颤 解剖学; 2）量化与震颤相关的信息如何调节单细胞，人口和网络 在Harmline引起的震颤的临床前大动物模型中，通过治疗DBS的水平； 3）调查 如何治疗窗口（即姿势和动作震颤之间的阈值差异和 副作用出现）随着时间的流逝而随着人类DBS疗法的目标而改变 小脑 - thalamoc-cortical网络。该项目一起（a）实验评估和翻译 新型的DBS编程算法对人类ET患者，（b）提供了更详细的神经图 DBS（对姿势和动作震颤）和DBS的副作用的治疗作用的潜在途径（On 构造障碍，异常，共济失调），（c）严格研究用于处理震颤的DBS如何机械地工作 大脑内的单细胞和网络水平，以及（d）探测涉及恶化的神经途径 ET患者随着时间的推移的震颤症状。