Data-driven optimization for DBS programming in temporal lobe epilepsy

颞叶癫痫 DBS 编程的数据驱动优化

• 批准号: 10574839
• 负责人: ROBERT E GROSS
• 金额: $ 38.07万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10574839
• 关键词: Ablation; Address; Affect; Algorithms; Animal Model; Animals; Brain; Characteristics; Climacteric; Clinical; Clinical Trials; Consumption; Data; Deep Brain Stimulation; Devices; Disease; Effectiveness; Electric Stimulation; Electrical Stimulation of the Brain; Electrodes; Engineering; Epilepsy; Excision; Face; Frequencies; Goals; Hippocampus (Brain); Implanted Electrodes; Individual; Intelligence; Intractable Epilepsy; Lead; Learning; Machine Learning; Measurement; Measures; Mental Depression; Mental disorders; Modeling; Neurologic; Neurosurgeon; Operative Surgical Procedures; Output; Parkinson Disease; Patients; Pattern; Persons; Phase; Process; Property; Proxy; Rattus; Reporting; Risk; Rodent Model; Seizures; Standardization; Statistical Models; Symptoms; System; Technology; Temporal Lobe Epilepsy; Testing; Tetanus Toxin; Therapeutic; Time; Work; base; clinical implementation; design; exhaustion; experience; experimental study; improved; in vivo; individual patient; nervous system disorder; next generation; novel; prototype; psychologic; relating to nervous system; response; simulation

Brain stimulation therapy is a life changing treatment for patients with neurological and psychiatric disorders, including Parkinson’s disease, depression, and epilepsy. In this treatment, neurosurgeons implant electrodes inside the brain that can deliver a wide range of electrical stimulation patterns. However, the next problem is determining the optimal stimulation setting for a given patient. Given that even basic clinical stimulation devices can be configured to millions of different stimulation settings, finding the right one is challenging. This problem is exacerbated in epilepsy, where patients do not exhibit symptoms between seizures, making the process of evaluating a setting’s effectiveness even more difficult. At its core, this is an optimization problem for which many engineering solutions exist. We have developed a framework for designing optimization systems for neural modulation that can be applied to a broad spectrum of different neural modulation paradigms. In this proposal, we develop an optimization system for automatically and efficiently identifying the optimal stimulation setting to maximally suppress seizures in a rodent model of epilepsy. Our previous work has shown that a particular type of stimulation, asynchronous distributed stimulation, can reduce the frequency of seizures in the rat tetanus toxin model of temporal lobe epilepsy. However, only a limited set of stimulation patterns were evaluated. In Aim 1, we will build on this work to better characterize the differential effects of varying asynchronous distributed stimulation parameters on seizures. These experiments will serve two purposes. First, they will clarify how different subjects are affected by stimulation parameters and determine if, as in other neurological disorders treated by brain stimulation, the best stimulation setting will vary from subject to subject. The second purpose is to use the data collected to create a simulation platform for prototyping optimization systems. In Aim 2, we will use the simulation platform prototype and tune different optimization systems. After determining which optimization system performs best in our simulation platform, the optimization systems will be implemented for real-time in vivo optimization to learn the subject specific stimulation settings that best reduce seizure frequency.

脑刺激疗法是一种改变神经和精神障碍患者生活的治疗方法， 包括帕金森氏症、抑郁症和癫痫。在这种治疗中，神经外科医生植入电极 在大脑内部，可以提供广泛的电刺激模式。然而，下一个问题是 确定给定患者的最佳刺激设置。考虑到即使是基本的临床刺激设备 可以配置为数百万种不同的刺激设置，找到合适的是具有挑战性的。这个问题 在癫痫中加重，患者在癫痫发作之间没有表现出症状，使这个过程 评估一个环境的有效性就更加困难了。从本质上讲，这是一个最优化问题，许多人 工程解决方案是存在的。我们开发了一个用于设计神经网络优化系统的框架 可广泛应用于不同神经调制范例的调制。在这份提案中， 我们开发了一个自动、高效地识别最优刺激设置的优化系统 在癫痫啮齿动物模型中最大限度地抑制癫痫发作。我们之前的工作表明，一种特殊的类型 对刺激，异步分布式刺激，可减少大鼠破伤风毒素惊厥发作的次数 颞叶癫痫模型。然而，只对一组有限的刺激模式进行了评估。在目标1中， 我们将在这项工作的基础上更好地描述变化的异步分布式的差异影响 癫痫发作的刺激参数。这些实验将有两个目的。首先，他们将澄清如何 不同的受试者会受到刺激参数的影响，并决定是否会像其他神经疾病一样 通过脑刺激治疗，最佳刺激设置将因受试者而异。第二个目的是 利用收集到的数据创建优化系统原型的仿真平台。在目标2中，我们将 利用仿真平台样机，对不同的优化系统进行调试。在确定哪一个之后 优化系统在我们的仿真平台上运行得最好，优化系统将在 实时体内优化，以了解受试者特定的刺激设置，最大限度地减少癫痫发作频率。