Patient Specific Parameter Optimization of Thalamic Stimulation for Treatment of Epilepsy

用于治疗癫痫的丘脑刺激的患者特定参数优化

• 批准号: 10700113
• 负责人: Robert A McGovern
• 金额: $ 52.15万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10700113
• 关键词: Affect; Algorithms; Animal Model; Anterior; Binding; Biological Markers; Cell Nucleus; Clinic; Clinical; Clinical Trials; Computer Analysis; Computer Models; Computers; Data; Deep Brain Stimulation; Development; Disease; Epilepsy; Event; Excision; Freedom; Frequencies; Goals; Home; Implant; Link; Measurement; Measures; Medical; Mental Depression; Methods; Modeling; Monitor; Operative Surgical Procedures; Outcome; Pain; Parkinson Disease; Patients; Phase; Physicians; Physiologic pulse; Process; Randomized, Controlled Trials; Recommendation; Refractory; Research; Schedule; Seizures; Signal Transduction; Stream; System; Testing; Thalamic structure; Time; Traumatic Brain Injury; Treatment Efficacy; Validation; Width; Work; biomarker discovery; biomarker validation; data streams; density; diaries; efficacy trial; epileptiform; implantable device; improved; individualized medicine; innovation; machine learning algorithm; neural; neural implant; neurotransmission; operation; response; safety and feasibility; secondary outcome; verification and validation; wearable sensor technology

): Deep brain stimulation (DBS) of the anterior nucleus of the thalamus (ANT) is clinically approved for treatment of epilepsy resulting in an average decrease in seizure frequency of 40%, but few patients achieve seizure freedom. Implantable neural stimulators have many parameters, such as stimulation amplitude, frequency and pulse width, which could potentially be tuned to improve efficacy. However, there is no systematic process to guide epileptologists through optimization. Stimulation of ANT in animal models has shown almost immediate changes in excitability in the loop of Papez, which we hypothesize is a biomarker that could be used to optimize stimulation parameters. Medtronic’s DBS Percept system allows for recording during stimulation and streaming the data to a computer for further analysis, which can be used in an optimization loop. Bayesian optimization (BayesOpt) is a machine learning algorithm that is widely used for efficient optimization over a bounded parameter range when acquiring data is expensive and computational time is relatively cheap. We have used BayesOpt for optimizing stimulation settings in animal models and clinical trials. Here we propose to develop an optimization platform where stimulation settings are programmed by a physician using recommended settings from a BayesOpt algorithm to minimize power measured from the patient’s thalamus in the clinic using Percept. Three aims are proposed to develop, test, and validate this approach in an exploratory clinical trial. Aim 1: Develop and test BayesOpt clinical interface with hardware in the loop system. Aim 2: Apply BayesOpt to 20 epilepsy patients treated with the Percept system in a clinical setting to optimize stimulation settings to minimize thalamic activity. Aim 3: Validate optimized settings at home by programming patients with optimized setting and their physician selected setting to test if seizure frequency or power spectral density is significantly lower in the optimized setting. The outcome of this clinical trial will be to establish safety and feasibility of optimization and validation. If successful, this study will be used to power a phase II efficacy trial. The broader impact of this work is that this platform could be used to tune the Percept system, based on different biomarkers, in other diseases, such as Parkinson’s disease, pain, and depression.

）：丘脑（ANT）的前核刺激的深脑刺激（DB）临床批准用于治疗癫痫病，导致癫痫发作频率平均降低40％，但很少有患者获得癫痫发作自由。可植入的中性刺激器具有许多参数，例如刺激放大器，频率和脉冲宽度，可能会调整以提高效率。但是，没有系统的过程来指导癫痫学家通过优化。在动物模型中刺激ANT的刺激显示了Papez环中的兴奋几乎立即改变，我们假设这是一种生物标志物，可用于优化刺激参数。 Medtronic的DBS感知系统允许在刺激期间记录和将数据流式传输到计算机以进行进一步分析，该分析可用于优化循环。贝叶斯优化（Bayesopt）是一种机器学习算法，当获取数据昂贵且计算时间相对便宜时，该算法在有限的参数范围内广泛用于优化。我们已经使用了贝内斯普来优化动物模型和临床试验中的刺激环境。在这里，我们建议开发一个优化平台，在该平台中，使用贝诺诺比特算法的建议设置对刺激设置进行了编程，以最大程度地减少使用感知从患者的丘脑中测量的功率。在探索性临床试验中提出了三个目标来开发，测试和验证这种方法。 AIM 1：在LOOP系统中使用硬件开发和测试贝内斯特临床接口。 AIM 2：将贝叶斯对在临床环境中接受感知系统治疗的20名癫痫患者应用，以优化刺激设置以最大程度地减少丘脑活性。 AIM 3：通过编程具有优化设置的患者及其物理选定设置，以测试在优化的设置中，在家中验证优化的设置，在优化的设置中是否显着降低。这项临床试验的结果将是建立优化和验证的安全性和可行性。如果成功，这项研究将用于为II期有效试验提供动力。这项工作的更广泛的影响是，该平台可用于根据其他疾病（例如帕金森氏病，疼痛和抑郁症）的不同生物标志物来调整感知系统。