Electrographic Seizure Pattern Modulation Biomarkers in Responsive Neurostimulation for Epilepsy

癫痫反应性神经刺激中的电描记癫痫模式调节生物标志物

• 批准号: 10652094
• 负责人: Robert Mark Richardson
• 金额: $ 83.69万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-15 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10652094
• 关键词: Acceleration; Adult; Biological Markers; Categories; Child; Childhood; Chronic; Clinical; Clinical Trials; Computer software; Data; Data Analytics; Data Set; Databases; Detection; Devices; Electric Stimulation; Electroencephalography; Electrophysiology (science); Epilepsy; Event; Exhibits; FDA approved; Frequencies; Implant; Individual; Intractable Epilepsy; Knowledge; Label; Machine Learning; Maintenance; Methods; Neural Network Simulation; Outcome; Patient Outcomes Assessments; Patient Self-Report; Patients; Pattern; Performance; Pharmaceutical Preparations; Phase; Prediction of Response to Therapy; Property; Records; Reporting; Seizures; Series; Signal Transduction; System; Techniques; Technology; Therapeutic; Time; Validation; Visual; Work; attenuation; biomarker validation; brain computer interface; clinical care; cohort; convolutional neural network; deep learning; epileptiform; implantable device; improved; individual patient; neurophysiology; neurotransmission; novel; pediatric patients; predictive marker; premature; prevent; response; response biomarker; signal processing; tool; treatment optimization; treatment response

ABSTRACT The responsive neurostimulation system (RNS) is the first FDA-approved bi-directional brain-computer interface. Developed to treat drug-resistant epilepsy, RNS is an implanted device that automatically records and detects electrographic seizures, then rapidly delivers electrical stimulation to suppress seizure activity. Although the general therapeutic benefit of RNS is well-established, predicting the magnitude and timing of a potential clinical response for each individual patient is difficult. It may take several months for a patient to report a reliable change in seizure status, during which time the programming clinician has no objective guidance regarding whether or not to adjust settings. Although chronic intracranial EEG recordings obtained by the RNS device provide an ongoing window into the neurophysiological state of a patient’s seizure network, there is little knowledge about how to use these recordings in individual patients. Thus, a critical need exists to develop methods for using a patient’s own data to predict when seizure reduction should be expected or to confirm objectively the presence and maintenance of a clinical response. Using RNS recordings, we recently made the first discovery of putative electrophysiological biomarkers that indicate and potentially predict therapeutic response to therapy in individual patients. By visually inspecting the spectral content of >5000 RNS recordings that captured putative seizures, we identified a distinct category of electrographic seizure pattern modulation (ESPM) that was always present in responders and never present in non-responders. In some cases, these ESPMs were observed in RNS recordings prior to patient-reported seizure reduction, suggesting their potential utilization in predicting therapeutic response. These putative biomarkers, however, cannot be identified using the standard RNS clinical user interface. To overcome these data analytic barriers to therapy optimization, we created a software concept for understanding patient-specific RNS performance using intracranial recordings and interpolation of device-recorded data (BRAINStim). Our proposal adds state-of-the- art expertise in machine learning and neural signal processing to develop technology for ESPM detection, characterization, and validation. In the R61 Phase, using recordings from a cohort of 60 subjects (10 pediatric), we will create tools for automatic detection of ESPMs and perform preliminary biomarker validation according to the following Contexts of Use: 1) prediction biomarkers that signal impending clinical response to RNS, prior to patient-reported seizure improvement, which would prevent premature programming decisions, and 2) response biomarkers that can be used to confirm patient-reported outcomes during stimulation and medication adjustments. In the R33 Phase, we will validate ESPM biomarkers in recordings from an extended cohort of 170 subjects (45 pediatric), to justify the use of ESPMs as RNS biomarkers in routine clinical care and novel clinical trials, which will accelerate and improve seizure outcomes, in both adults and children.

摘要 反应神经刺激系统（RNS）是第一个FDA批准的双向脑计算机 接口.研发用于治疗耐药性癫痫，RNS是一种植入式设备， 并检测电描记癫痫发作，然后快速递送电刺激以抑制癫痫发作活动。 虽然RNS的一般治疗益处是公认的，但预测RNS的程度和时间是不确定的。 对于每个个体患者的潜在临床反应是困难的。病人可能需要几个月的时间才能 报告癫痫发作状态的可靠变化，在此期间，程控临床医生没有目标 关于是否调整设置的指导。虽然通过脑电监测获得了慢性颅内脑电图记录， RNS设备提供了进入患者癫痫发作网络的神经生理状态的持续窗口， 关于如何在个体患者中使用这些记录的知识很少。因此，迫切需要 开发使用患者自己的数据来预测何时应该预期癫痫发作减少的方法， 客观地确认临床反应的存在和维持。使用RNS记录，我们最近 首次发现了推定的电生理生物标志物， 个体患者对治疗的治疗反应。通过目视检查>5000 RNS的光谱含量 记录捕获假定的癫痫发作，我们确定了一个独特的类别的电图癫痫发作模式 调节（ESPM），其总是存在于应答者中而从不存在于非应答者中。在一些 例，在患者报告癫痫发作减少之前，在RNS记录中观察到这些ESPM，表明 它们在预测治疗反应中的潜在用途。然而，这些假定的生物标志物不能被 使用标准RNS临床用户界面识别。为了克服这些数据分析障碍， 优化，我们创建了一个软件概念，用于了解患者特定的RNS性能， 颅内记录和器械记录数据的插值（BRAINStim）。我们的建议增加了国家- 在机器学习和神经信号处理方面的专业知识，以开发ESPM检测技术， 表征和验证。在R61阶段，使用来自60名受试者（10名儿科受试者）队列的记录， 我们将创建自动检测ESPM的工具，并根据 用途：1）预测生物标志物，其发出对RNS即将发生的临床反应的信号， 患者报告的癫痫发作改善，这将防止过早的程控决策，以及2） 可用于确认刺激和用药期间患者报告结局的反应生物标志物 调整。在R33阶段，我们将验证ESPM生物标志物在记录从一个扩展的队列， 170例受试者（45例儿科受试者），证明在常规临床护理和新型治疗中使用ESPM作为RNS生物标志物的合理性 临床试验，这将加速和改善癫痫发作的结果，在成人和儿童。