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.

抽象的 响应式神经刺激系统（RNS）是第一个由FDA批准的双向脑计算机 界面。为治疗耐药性癫痫的开发，RNS是一种自动记录的植入装置 并检测到电学癫痫发作，然后迅速提供电刺激以抑制癫痫发作活性。 尽管RNS的一般治疗益处是完善的，可以预测 每个患者的潜在临床反应都是困难的。患者可能需要几个月 报告癫痫发作状态的可靠变化，在此期间编程临床医生没有目标 有关是否调整设置的指南。尽管慢性颅内脑电图记录通过 RNS设备为患者癫痫发作网络的神经生理状态提供了一个持续的窗口， 关于如何在个别患者中使用这些录音的知识很少。那是一个关键需求 开发使用患者自己的数据来预测应何时应减少癫痫发作的方法或 客观地确认临床反应的存在和维护。使用RNS记录，我们最近 首先发现了推定的电生理生物标志物，这些生物标志物表明并可能预测 单个患者对治疗的治疗反应。通过视觉检查> 5000 RN的光谱含量 捕获假定癫痫发作的录音，我们确定了一个不同类别的电视癫痫发作模式 调制（ESPM）始终存在于响应者中，从不存在于无反应者中。在某些人中 案例，在患者报告癫痫发作之前，在RNS记录中观察到这些ESPM，这表明 它们在预测治疗反应中的潜在利用。但是，这些假定的生物标志物不能是 使用标准RNS临床用户界面确定。要克服这些数据分析障碍 优化，我们创建了一个软件概念，用于了解使用特定于患者的RNS性能 颅内记录和设备记录数据的插值（Brainstim）。我们的建议增加了 机器学习和神经信号处理方面的艺术专业知识以开发ESPM检测技术， 表征和验证。在R61阶段，使用来自60名受试者（10个小儿）的队列的记录， 我们将创建用于自动检测ESPM的工具，并根据 在以下情况下：1）预测生物标志物，该预测临时临床对RN的反应，先前 为患者报告的癫痫发作改善，这将防止过早的编程决定，2） 可以用来确认刺激和药物治疗患者报告的结果的反应生物标志物 调整。在R33阶段，我们将验证ESPM生物标志物在录音中的录音中。 170名受试者（45个儿科），以证明将ESPM用作RNS生物标志物在常规临床护理和新颖 成人和儿童的临床试验将加速和改善癫痫发作结果。