EEG Biomarkers Derived from Dynamical Network Models Enable Rapid Paths to Accurate Diagnosis and Effective Treatment of Epilepsy

源自动态网络模型的脑电图生物标志物为癫痫的准确诊断和有效治疗提供了快速途径

• 批准号: 10665213
• 负责人: Sridevi V. Sarma
• 金额: $ 48.01万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-16 至 2031-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10665213
• 关键词: Address; Antiepileptic Agents; Biological Markers; Brain; Clinical; Derivation procedure; Devices; Diagnosis; Disease; Drug resistance; Electric Stimulation Therapy; Electrical Stimulation of the Brain; Electroencephalography; Epilepsy; Excision; FDA approved; Family; Glean; Hospital Costs; Knowledge; Length of Stay; Malignant neoplasm of lung; Measures; Operative Surgical Procedures; Outcome; Pain; Pathologic; Patients; Persons; Pharmaceutical Preparations; Pharmacotherapy; Physiological; Positioning Attribute; Property; Recurrence; Scalp structure; Seizures; Source; Technology; Woman; Work; accurate diagnosis; alternative treatment; burden of illness; cohort; computerized tools; design; drug efficacy; effective therapy; improved; indexing; malignant breast neoplasm; men; nervous system disorder; network models; novel; optimal treatments; patient population; programs; rapid diagnosis; side effect; social stigma; stem; success; vagus nerve stimulation

SUMMARY Epilepsy is a neurological disorder that is marked by sudden recurrent episodes of abnormal electrical activity in the brain, known as seizures. This disease plagues more than 60 million people globally, with the same burden of disease as breast cancer in women and lung cancer in men. First line of treatment for patients with epilepsy are anti-epileptic drugs (AEDs). If AEDs are not effective in suppressing seizures, then patients may consider alternative treatments including surgical resection of the epileptogenic zone in the brain, electrical brain stimulation, or vagus nerve stimulation. With several treatment options available, one may think that epilepsy is under control. However, this is far from true. Accurately diagnosing epilepsy and then finding an effective treatment can take years to a lifetime, during which patients and families suffer from the stigma of epilepsy, side- effects of ineffective AEDs, extensive and costly hospital stays, poor outcomes of irreversible surgical treatment, and/or less than satisfactory stimulation therapies whose efficacies are physiologically unmeasurable. We propose a program to establish novel EEG biomarkers and computational tools that will enable rapid and accurate diagnosis of epilepsy followed by a rapid path to an effective treatment. Such a program entails major advances in conceptual knowledge of how epileptic cortical networks behave and change during stimulation treatment that will be gleaned from dynamic network modeling (DNM) of EEG. There are many challenges with diagnosing and treating epilepsy that unfolds as one considers the clinical workflow beginning with a patient’s first seizure. First an accurate diagnosis of epilepsy can take months to years, where scalp EEG can be leveraged to confirm diagnosis. However, the gold standard is to look for EEG abnormalities that are indicators of epilepsy (e.g., spikes), which are often not captured or misread. Second, it takes months to years to find effective AED treatment as there is no physiological measure of drug efficacy. For these two pain points, we will leverage a new biomarker that our lab discovered from intracranial EEG called the source-sink metric which is designed to capture pathological network properties that are always present only in epilepsy patients. For 30% of the patient population, no AEDs work, and their alternative treatments include surgical treatment of the epileptogenic zone (EZ) and electrical stimulation therapy. However surgical success rates for drug resistant patients averages 50%, and there is currently no measure of efficacy of neurostimulation treatment, leaving half of treated patients nonresponsive. For these drug resistant patients, we will leverage the source-sink index, derived from DNMs and EEG, to help more accurately localize the EZ to improve surgical success rates, and to track efficacy of stimulation treatment from the FDA approved RNS device. The proposed R35 will address major challenges with novel EEG biomarkers stemming from dynamic network models. Is successful, the program will lead to breakthrough technologies enabling getting to accurate diagnosis and optimal treatment for all epilepsy patients more rapidly (from years to weeks), including the underserved drug-resistant cohort.

概括 癫痫是一种神经系统疾病，其特征是突然复发性发作异常的电活动。 大脑，称为癫痫发作。这种疾病困扰着全球超过6000万人，负担相同 疾病作为女性的乳腺癌和男性肺癌。癫痫患者的第一道治疗 是抗癫痫药（AED）。如果AED在抑制癫痫发作方面无效，则可以考虑 替代治疗，包括大脑中癫痫发射区的手术切除，电脑 刺激或迷走神经刺激。有了几种治疗选择，人们可能会认为癫痫是 在控制之下。但是，这远非如此。准确诊断癫痫，然后找到有效 治疗可能需要数年的时间，在此期间，患者和家庭患有癫痫，侧面的污名 无效的AED的影响，广泛且昂贵的住院，不可逆的手术治疗结果不佳， 和/或少于满意度工厂刺激疗法，其有效性在物理上是无法衡量的。我们 提出一个计划，以建立新颖的脑电图生物标志物和计算工具，以使快速 并准确诊断癫痫，然后是有效治疗的快速途径。这样的程序 在概念上了解癫痫性皮质网络如何行事和变化，在 EEG的动态网络建模（DNM）将收集的刺激处理。有许多 诊断和治疗癫痫的挑战，它认为是临床工作流程的开始 患者的第一次癫痫发作。首先，准确诊断癫痫可能需要数月到几年，头皮脑 可以利用以确认诊断。但是，黄金标准是寻找脑电图异常 癫痫的指标（例如，尖峰），通常不会被捕获或错过。其次，需要几个月到几年 找到有效的AED治疗，因为没有药物效率的物理测量。对于这两个疼痛点， 我们将利用我们的实验室从颅内脑电图中发现的一种新的生物标志物，称为源 - 链接指标 旨在捕获仅在癫痫患者中始终存在的病理网络特性。 对于30％ 癫痫发作区（EZ）和电刺激疗法。但是药物抗药性的手术成功率 患者平均为50％，目前尚无神经刺激治疗效率的测量，一半 治疗的患者无反应。对于这些抗药性患者，我们将利用源源指数， 源自DNM和EEG，以帮助更准确地定位EZ以提高手术成功率，并 跟踪FDA批准的RNS设备的刺激处理效率。拟议的R35将针对专业 源于动态网络模型的新型脑电图生物标志物的挑战。成功，程序将 导致突破性技术使所有癫痫的准确诊断和最佳治疗 患者（从几年到几周）更快，包括服务不足的耐药队列。