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

总结 癫痫是一种神经系统疾病，其特征是脑内异常电活动的突然反复发作， 也就是癫痫发作这种疾病困扰着全球6 000多万人， 女性的乳腺癌和男性的肺癌癫痫患者的一线治疗 抗癫痫药物（AEDs）。如果抗癫痫药物不能有效抑制癫痫发作，那么患者可以考虑 替代治疗包括手术切除大脑中的致痫区， 刺激或迷走神经刺激。有几种治疗方案可供选择，人们可能会认为癫痫是 一切尽在掌握然而，事实远非如此。准确诊断癫痫，然后找到一个有效的 治疗可能需要数年甚至一生的时间，在此期间，患者和家庭遭受癫痫的耻辱， 无效AED的影响，长期和昂贵的住院时间，不可逆手术治疗的不良结果， 和/或不太令人满意的刺激疗法，其功效在生理上是不可测量的。我们 提出一项计划，建立新的脑电图生物标志物和计算工具，使快速 和准确的诊断癫痫其次是一个快速的路径，以有效的治疗。这样的程序 需要在癫痫皮层网络如何表现和变化的概念知识方面取得重大进展， 从EEG的动态网络建模（DNM）中收集的刺激治疗。有很多 诊断和治疗癫痫的挑战，随着临床工作流程的开始， 病人第一次癫痫发作的时候首先，癫痫的准确诊断可能需要数月至数年的时间， 可以用来确诊然而，金标准是寻找脑电图异常， 癫痫的指标（例如，尖峰），其通常未被捕获或误读。第二， 寻找有效的AED治疗方法，因为没有药物疗效的生理测量。针对这两个痛点， 我们将利用我们实验室从颅内脑电图中发现的一种新的生物标志物， 其被设计为捕获总是仅存在于癫痫患者中的病理网络特性。 对于30%的患者人群，没有AED工作，他们的替代治疗包括手术治疗， 致痫灶（EZ）和电刺激治疗。然而，耐药患者的手术成功率 患者平均50%，目前还没有衡量神经刺激治疗的疗效， 无反应的患者。对于这些耐药患者，我们将利用源汇指数， 从DNM和EEG中提取，以帮助更准确地定位EZ，提高手术成功率， 跟踪FDA批准的RNS器械刺激治疗的疗效。R35将解决主要问题 来自动态网络模型的新型脑电生物标志物的挑战。如果成功，该计划将 导致突破性的技术，使获得准确的诊断和最佳治疗所有癫痫 患者更快（从数年到数周），包括服务不足的耐药队列。

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.