Thalamocortical Responsive Neurostimulation for the Treatment of Lennox-Gastaut Syndrome

丘脑皮质反应性神经刺激治疗 Lennox-Gastaut 综合征

• 批准号: 10414867
• 负责人: MARTHA J MORRELL
• 金额: $ 259.68万
• 依托单位: NEUROPACE, INC.
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10414867
• 关键词: 12 year old; Adverse effects; Bilateral; Biological Markers; Blinded; Brain; Centromedian Thalamic Nucleus; Childhood; Clinical; Clinical Research; Clonic Seizure; Computer Models; Crossover Design; Data; Decision Support Systems; Deep Brain Stimulation; Detection; Device Designs; Devices; Diffusion Magnetic Resonance Imaging; Disease; Electrodes; Electroencephalography; Electrophysiology (science); Elements; Enrollment; Epilepsy; Etiology; Evaluation; FDA approved; Feasibility Studies; Follow-Up Studies; Frequencies; Functional disorder; Future; Gastaut syndrome; Generalized Epilepsy; Genetic Crossing Over; Goals; Impaired cognition; Implant; Injury; Ipsilateral; Lead; Longterm Follow-up; Machine Learning; Magnetic Resonance Imaging; Manuals; Medial; Medical; Medical Imaging; Mental Depression; Modeling; Neurons; Online Systems; Operative Surgical Procedures; Outcome; Parkinson Disease; Pathway interactions; Patients; Pharmaceutical Preparations; Physicians; Pilot Projects; Policies; Population; Post-Traumatic Stress Disorders; Prefrontal Cortex; Randomized; Research; Safety; Seizures; Selection for Treatments; Single-Blind Study; System; Testing; Time; Tonic Seizures; Update; addiction; atonic seizure; base; biomarker discovery; biophysical model; blind; clinical efficacy; clinical practice; cohort; computational network modeling; design; diaries; early experience; effective therapy; improved; interactive therapy; lead optimization; machine learning method; mortality; neuroimaging; neurophysiology; neuroregulation; novel; open label; potential biomarker; predict clinical outcome; prototype; recruit; response; safety testing; tool

Project Summary / Abstract Lennox-Gastaut Syndrome (LGS) is a devastating form of childhood onset epilepsy with cognitive dysfunction and very frequent generalized onset seizures (GOS) often leading to injury. Driven by the lack of effective therapies and the demonstrated safety and efficacy of brain-responsive stimulation for medically intractable focal onset seizures (FOS), this study will test whether brain-responsive neurostimulation of thalamocortical networks (RNS-TCN) is a feasible strategy to treat LGS. Specifically, the project aims to: (1) acquire preliminary evidence for safety and efficacy in treating GOS of LGS with RNS-TCN; (2) create an interactive therapy-decision support system based on patient-specific computational network models and machine learning to identify optimal lead placement and stimulation parameters. Using the RNS® System, which is FDA approved for FOS, an early feasibility IDE study will be conducted at 6 epilepsy centers in 20 patients with LGS and medically intractable GOS. The patients will be enrolled in two cohorts of 10, with safety and efficacy milestones in the first cohort governing the enrollment of the second cohort. Patients will have two depth leads placed bilaterally in the centromedian nucleus of the thalamus and two subdural strip leads placed bilaterally on the medial prefrontal cortex. These targets are selected because they are implicated in the onset and spread of GOS in LGS. Leads will be located within each target such that stimulation maximally engages the thalamocortical network, guided by finite-element biophysical models created from structural magnetic resonance imaging and diffusion weighted imaging. The finite-element biophysical models will also be used to identify the initial RNS-TCN stimulation pathway and current amplitude. During the blinded evaluation period patients will be randomized to receive either high-frequency short burst (HFSB) or low-frequency long burst (LFLB) RNS-TCN, then enter a washout period before crossing over to receive the other treatment condition. During the open label period stimulation parameters can be modified at the discretion of the physician. Parameter adjustments will be informed by using a Bayesian optimization model developed for specifically for each patient. All clinical and electrophysiological data collected during the study will be used to identify a biomarker of clinical response; if found, these will aid future epilepsy research and clinical practice. If safety is favorable and there is preliminary evidence for efficacy, then this early experience will inform the design of a future larger feasibility study. In addition to treating a population in need, this project engages in fundamental discovery of biomarkers in generalized network epilepsies, and develops novel automated therapy selection policies that have the potential to improve the lives of patients with LGS and other seizure disorders.

项目概要/摘要 Lennox-Gastaut 综合征 (LGS) 是一种破坏性的儿童期癫痫发作，伴有认知障碍 功能障碍和非常频繁的全身性癫痫发作（GOS）通常会导致受伤。在缺乏的推动下 有效的治疗方法以及已证实的脑反应刺激在医学上的安全性和有效性 顽固性局灶性癫痫发作（FOS），本研究将测试大脑反应性神经刺激是否 丘脑皮质网络（RNS-TCN）是治疗 LGS 的可行策略。具体来说，该项目旨在： (1) 获得 RNS-TCN 治疗 LGS 的 GOS 的安全性和有效性的初步证据； (2) 创建一个 基于患者特定计算网络模型的交互式治疗决策支持系统 机器学习来识别最佳引线放置和刺激参数。 使用经 FDA 批准用于 FOS 的 RNS® 系统，将进行早期可行性 IDE 研究 在 6 个癫痫中心治疗了 20 名 LGS 和难治性 GOS 患者。患者将被纳入 两组各 10 人，第一组的安全性和有效性里程碑控制第二组的入组 队列。患者将在两侧丘脑中心正中核放置两根深度导线， 两条硬膜下带状引线双侧放置在内侧前额皮质上。选择这些目标是因为 它们与 LGS 中 GOS 的发病和传播有关。线索将位于每个目标内，以便 在有限元生物物理模型的指导下，刺激最大程度地参与丘脑皮质网络 由结构磁共振成像和扩散加权成像创建。有限元 生物物理模型还将用于识别初始 RNS-TCN 刺激路径和电流幅度。 在盲法评估期间，患者将被随机分配接受高频短脉冲 （HFSB）或低频长突发（LFLB）RNS-TCN，然后在交叉到之前进入清洗期 接受其他治疗条件。在开放标签期间，可以修改刺激参数 医生的酌情权。将通过使用贝叶斯优化来通知参数调整 专为每位患者开发的模型。期间收集的所有临床和电生理数据 研究将用于确定临床反应的生物标志物；如果找到的话，这些将有助于未来的癫痫研究 和临床实践。如果安全性良好并且有初步证据证明疗效，那么尽早 经验将为未来更大规模的可行性研究的设计提供信息。 除了治疗有需要的人群外，该项目还致力于生物标志物的基础发现 广泛性网络癫痫，并开发了新颖的自动化治疗选择策略 改善 LGS 和其他癫痫病患者的生活的潜力。