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，其特征是突然反复发作的异常电活动 这种疾病困扰着全球超过 6000 万人，他们也承受着同样的负担。 女性乳腺癌和男性肺癌等疾病的一线治疗。 是抗癫痫药物 (AED) 如果 AED 不能有效抑制癫痫发作，那么患者可以考虑使用。 替代疗法包括手术切除大脑中的致痫区、电脑 刺激或迷走神经刺激有多种治疗选择，人们可能会认为癫痫是一种疾病。 然而，这远非准确诊断癫痫并找到有效的治疗方法。 治疗可能需要数年甚至一生的时间，在此期间，患者及其家人承受着癫痫的耻辱、副作用和痛苦。 无效的 AED 的影响、长期且昂贵的住院时间、不可逆转的手术治疗的不良结果， 和/或不太令人满意的刺激疗法，其功效在生理上是无法测量的。 提出一项建立新型脑电图生物标志物和计算工具的计划，以实现快速 癫痫的准确诊断以及有效治疗的快速途径。 需要在关于癫痫皮层网络在癫痫期间如何表现和变化的概念知识方面取得重大进展 从脑电图动态网络模型（DNM）中收集的刺激治疗有很多。 当人们考虑临床工作流程开始时，诊断和治疗癫痫的挑战就会显现出来 对于患者的第一次癫痫发作，准确诊断癫痫可能需要数月至数年的时间，其中头皮脑电图。 可以用来确认诊断，但是金标准是寻找脑电图异常。 其次，它需要数月至数年的时间。 由于没有生理学指标来衡量这两个痛点，因此需要找到有效的 AED 治疗方法。 我们将利用我们的实验室从颅内脑电图发现的一种新的生物标志物，称为源汇指标 其目的是捕获始终仅存在于癫痫患者中的病理网络特性。 对于 30% 的患者群体，AED 不起作用，他们的替代治疗包括手术治疗 致癫痫区（EZ）和电刺激治疗然而耐药性的手术成功率。 患者平均为 50%，目前尚无神经刺激治疗疗效的衡量标准，剩下一半 对于这些耐药患者，我们将利用源库指数， 源自 DNM 和 EEG，帮助更准确地定位 EZ，从而提高手术成功率，并 跟踪 FDA 批准的 RNS 设备的刺激治疗效果。拟议的 R35 将解决主要问题。 如果挑战来自动态网络模型的新型脑电图生物标记，则该计划将成功。 带来突破性技术，使所有癫痫病都能得到最佳的准确诊断和治疗 患者的治疗速度更快（从几年到几周），包括服务不足的耐药人群。