Patient Specific Parameter Optimization of Thalamic Stimulation for Treatment of Epilepsy

):丘脑前核（ANT）的脑深部电刺激（DBS）在临床上被批准用于治疗癫痫，导致癫痫发作频率平均降低40%，但很少有患者实现癫痫发作自由。植入式神经刺激器具有许多参数，例如刺激幅度、频率和脉冲宽度，这些参数可能被调整以提高功效。然而，没有系统的过程来指导癫痫医生进行优化。在动物模型中刺激ANT已经显示出Papez环路中的兴奋性几乎立即发生变化，我们假设这是一种可用于优化刺激参数的生物标志物。Medtronic的DBS Percept系统允许在刺激过程中进行记录，并将数据传输到计算机进行进一步分析，可用于优化循环。贝叶斯优化（BayesOpt）是一种机器学习算法，广泛用于在获取数据昂贵且计算时间相对便宜的情况下在有界参数范围内进行有效优化。我们使用BayesOpt优化动物模型和临床试验中的刺激设置。在这里，我们建议开发一个优化平台，其中刺激设置由医生使用BayesOpt算法的推荐设置进行编程，以最大限度地减少在诊所使用Percept从患者丘脑测量的功率。提出了三个目标，以开发，测试和验证这种方法的探索性临床试验。目标1：开发和测试BayesOpt临床接口与硬件在环系统。目标二：将BayesOpt应用于在临床环境中使用Percept系统治疗的20名癫痫患者，以优化刺激设置，从而最大限度地减少丘脑活动。目标3：通过对患者进行优化设置和其医生选择的设置编程，在家中测试优化设置中的癫痫发作频率或功率谱密度是否显著较低，从而验证优化设置。本临床试验的结果将确定优化和验证的安全性和可行性。如果成功，该研究将用于II期疗效试验。这项工作的更广泛影响是，该平台可用于根据不同的生物标志物调整Percept系统，用于其他疾病，如帕金森病，疼痛和抑郁症。

): 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.