Optimizing Deep Brain Stimulation for Parkinson's Disease.

优化帕金森病的深部脑刺激。

• 批准号: 10557617
• 负责人: Benjamin Johnson
• 金额: $ 16.13万
• 依托单位: BOISE STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-06 至 2028-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10557617
• 关键词: Address; Affect; Age; Aged, 80 and over; Animal Disease Models; Behavior assessment; Behavioral; Biological Markers; Bluetooth; Brain; Brain Diseases; Centers of Research Excellence; Clinical; Computer Models; Consumption; Custom; Data; Deep Brain Stimulation; Detection; Devices; Disease; Electric Stimulation; Engineering; Epilepsy; FDA approved; Funding; Goals; Implant; Intelligence; Knowledge; Laws; Machine Learning; Manuals; Measures; Memory impairment; Mental Depression; Modeling; Morphologic artifacts; Motor; Neurotoxins; Operative Surgical Procedures; Outcome; Oxidopamine; Pain; Parkinson Disease; Patients; Policies; Population; Protocols documentation; Radio; Rattus; Research; Research Methodology; Research Personnel; Rodent; Rodent Model; Science; Signal Transduction; Silicon; System; Technology; Testing; Therapeutic; Time; Traumatic Brain Injury; Tremor; United States National Institutes of Health; Update; Work; density; design; effective therapy; energy efficiency; fabrication; flexibility; gait examination; improved; individualized medicine; innovation; integrated circuit; miniaturize; motor symptom; nervous system disorder; neural; neuroregulation; neurotechnology; neurotransmission; next generation; optimal treatments; programs; response; sensor; therapeutically effective; wireless

PROJECT SUMMARY - JOHNSON Deep brain stimulation (DBS) is a promising FDA-approved therapeutic for advanced Parkinson’s disease (PD), a disease that increases with age, affecting 1% of the population over the age of 60 and 10% of the population over the age of 80. There is a critical need to automate DBS parameter selection for optimal therapy and greatly reduce clinician burden. Clinical DBS, however, creates long-lasting stimulation artifacts that obscure and distort the detection of neural biomarkers that could be used to automate therapy. This study will address this challenge by making innovative advancements to our closed-loop neuromodulation technology called WAND (wireless artifact-free neuromodulation device) that can reliably sense neural signals with concurrent electrical stimulation. Our long-term goal is to develop a miniaturized DBS device that closes the loop by sensing biomarkers and computing stimulation parameters for automatic, patient-specific treatment. Our overall objective is to adapt WAND for rodent studies and use a machine learning search strategy to find the best stimulation settings based on biomarkers in a rodent model of PD. Closed-loop DBS, enabled by WAND and by a machine-learning based parameter optimization, can produce longer-lasting and more energy- efficient motor rescue as compared to standard DBS therapy. The central hypothesis will be tested pursuing three specific aims: 1) Optimize WAND for rodent studies to create a next-generation research platform; 2) Validate WAND’s ability to extract neural biomarkers during DBS therapy and gait analysis in a rat model of PD; and 3) Design and evaluate an optimization system to dynamically control neural biomarkers in a rat model of PD. This work is significant because it will make substantive advancements to a next-generation neurotechnology research platform (WAND). This smaller device will be more automated because of our emphasis on the critical technical challenge of improving patient-specific DBS parameter selection. Outcomes not only advance PD research and clinical outcomes, but we can also extend knowledge given that DBS has shown promise as a treatment for many other neurological disorders, including epilepsy, depression, and memory impairment. Further, the effort is well-suited to this COBRE as it addresses the Biomedical Devices, Sensors, and Systems theme.

项目总结--Johnson 深部脑刺激(DBS)是FDA批准的治疗晚期帕金森病的一种有前途的疗法 帕金森病是一种随着年龄增长而增加的疾病，影响1%的60岁以上人口和10%的老年人 80岁以上的人口。迫切需要自动选择DBS参数以实现最佳治疗 大大减轻了临床医生的负担。然而，临床DBS创造了长期的刺激伪像， 模糊和扭曲了可用于自动化治疗的神经生物标志物的检测。这项研究将 通过对我们的闭环神经调节技术进行创新来应对这一挑战 称为Wand(无线无伪影神经调节设备)，可以可靠地感知神经信号 同时进行电刺激。我们的长期目标是开发一种小型化的DBS设备，可以关闭 通过感知生物标志物和计算刺激参数进行循环，以实现针对患者的自动治疗。 我们的总体目标是将魔杖用于啮齿动物研究，并使用机器学习搜索策略来寻找 帕金森病啮齿动物模型中基于生物标志物的最佳刺激设置。闭环式DBS，由 通过基于机器学习的参数优化，魔杖可以产生更持久和更多的能量- 与标准的DBS治疗相比，有效的运动抢救。核心假说将在以下方面得到检验 三个具体目标：1)优化啮齿动物研究的魔杖，打造下一代研究平台；2) 验证Wand在DBS治疗和步态分析过程中提取神经生物标志物的能力 PD；3)设计和评价动态控制大鼠神经生物标志物的优化系统 帕金森病模型。这项工作意义重大，因为它将为下一代带来实质性的进步 神经技术研究平台(Wand)。这款较小的设备将更加自动化，因为我们的 强调改善针对患者的DBS参数选择的关键技术挑战。结果 不仅推动PD研究和临床结果，而且我们还可以扩展知识，因为DBS已经 显示出作为治疗许多其他神经疾病的希望，包括癫痫，抑郁症和 记忆力受损。此外，这一努力非常适合这一科布雷，因为它涉及生物医学设备， 传感器和系统主题。