Patient Specific Parameter Optimization of Thalamic Stimulation for Treatment of Epilepsy

)：丘脑前核(ANT)深部脑刺激(DBS)被临床批准用于治疗癫痫，导致癫痫发作频率平均减少40%，但很少有患者获得癫痫发作自由。植入式神经刺激器有许多参数，如刺激幅度、频率和脉冲宽度，这些参数可能会被调整以提高疗效。然而，目前还没有一个系统的过程来指导癫痫医生进行优化。在动物模型中，对ANT的刺激几乎立即显示出Papez环中兴奋性的变化，我们假设它是一个生物标志物，可以用来优化刺激参数。美敦力的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，该计算机可用于优化循环。贝叶斯优化(BayesOpt)是一种机器学习算法，被广泛用于在数据获取昂贵而计算时间相对便宜的情况下在有界参数范围内进行高效优化。我们已经在动物模型和临床试验中使用了BayesOpt来优化刺激设置。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算法中的推荐设置来编程，以最大限度地减少使用Percept在临床上从患者丘脑测量的功率。在探索性临床试验中，提出了开发、测试和验证该方法的三个目标。目的1：开发和测试带硬件在环系统的BayesOpt临床接口。目的：将BayesOpt应用于20例临床环境下使用Percept系统治疗的癫痫患者，以优化刺激环境以最小化丘脑活动。目的3：在家中用优化设置和医生选择的设置对患者进行编程，以测试在优化设置下癫痫发作频率或功率谱密度是否显著降低，从而验证优化设置。这项临床试验的结果将是确定优化和验证的安全性和可行性。如果成功，这项研究将被用于第二阶段疗效试验。这项工作的更广泛的影响是，这个平台可以用于调整感知系统，基于不同的生物标记物，用于其他疾病，如帕金森氏症、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.