Dynamics of long range network interactions in focal epilepsy

项目总结 癫痫是世界上最突出的严重脑部疾病，全世界有近5000万人受到影响。为 在这些患者中，约30%的癫痫发作仍然控制不佳，尽管有最佳的医疗管理， 随之而来的对健康和生活质量的影响。为了能够在治疗管理方面取得进展 癫痫，彻底了解导致癫痫发作的细胞过程是如何与大规模的 网络效应是必要的。虽然癫痫发作会影响大片大脑区域，通常还会影响多个脑叶，但驾驶 过程跨越毫米级的区域。这些已经在动物模型中得到了很好的描述， 但与人类癫痫发作的相关性，即癫痫发作是如何由来自小规模过程的大脑信号驱动的 目前仍不清楚。相反，认为自然发作可能是大规模癫痫发作的观点 神经团效应(即癫痫网络)是一个持续争论的主题。以前，我们定义了一个键 环绕抑制在形成发作地点和小空间尺度上的脑电记录中的作用。 我们现在提出，环绕抑制具有双重作用。在毫米尺度上，它的突然故障允许 癫痫发作的预付款。在距离癫痫灶很远的地方，强烈的局部抑制作用是为了掩盖 癫痫发作的兴奋效应，可能有助于加速终止癫痫发作，而减弱的抑制作用可能 允许在遥远的、不连续的癫痫发作部位出现发作活动。多个癫痫灶可能会消失 无法用标准的脑电解释方法识别，可能是癫痫手术中的关键因素 失败。我们假设，一旦建立，多个发作发生器就像延迟耦合振荡器一样， 表现出同步的、甚至是暂时颠倒的活动。这导致了复杂的，有时甚至 违反直觉的网络行为，从脑电记录进行反向工程可能具有挑战性。通常， 然而，即使是颅内脑电记录也只能提供有限的神经活动视图。在这个项目中，一个 跨学科研究小组，具有癫痫、临床神经生理学、计算等方面的综合专长 建模，数学将对癫痫的神经元贡献者进行全面研究 利用同时记录微电极和大电极的唯一组合数据集的网络 人类癫痫发作的证据。使用机器学习方法，我们将应用这些信息来开发 多变量脑电生物标记物，基于脑电放电、高频振荡和 甚低频(DC)移位并评估其对术后手术结果的预测价值。我们 预计该项目将导致合理开发创新战略的理论框架 用于开发控制癫痫发作的干预措施。

PROJECT SUMMARY Epilepsy is the world’s most prominent serious brain disorder, affecting nearly 50 million people worldwide. For about 30% of these patients, seizures remain poorly controlled despite optimal medical management, with attendant effects on health and quality of life. In order to enable advances in the therapeutic management of epilepsy, a thorough understanding of how cellular processes that drive seizures are linked to large-scale network effects is needed. While seizures impact large brain areas and often multiple lobes, the driving processes span regions on the scale of millimeters. These have been well characterized in animal models, but the relevance to human seizures, i.e. how seizures are driven by brain signals from small-scale processes remains unclear. Instead, the view that naturally-occurring seizures may be attributable instead to large-scale neural mass effects (i.e., the epileptic network) is a subject of ongoing debate. Previously, we defined a key role for surround inhibition in shaping EEG recordings of seizures at the onset site and on small spatial scales. We now propose that surround inhibition has a dual role. On a millimeter scale, its abrupt failure permits the advance of a seizure. At long distances from the seizure focus, strong local inhibition serves to mask the excitatory effects of seizures and may help to hasten seizure termination, while weakened inhibition may permit emergence of ictal activity at a distant, noncontiguous seizure site. Multiple seizure foci may go unrecognized with standard EEG interpretation methods, and are likely a critical factor in epilepsy surgery failures. We hypothesize that once established, multiple ictal generators behave as delay-coupled oscillators, demonstrating activity that is synchronized or even temporally reversed. This results in complex and at times counterintuitive network behavior that can be challenging to reverse engineer from EEG recordings. Typically, however, even intracranial EEG recordings provide only a limited view of neural activity. In this project, an interdisciplinary research group with combined expertise in epilepsy, clinical neurophysiology, computational modeling, and mathematics will conduct a comprehensive study of the neuronal contributors to epileptic networks utilizing a unique combined dataset of simultaneous microelectrode and macroelectrode recordings of human seizures. Using a machine learning approach, we will apply this information to develop a multivariate EEG biomarker based on the inferred source of EEG discharges, high frequency oscillations, and very low frequency (DC) shifts and assess its predictive value for post-resection surgical outcome. We anticipate that the project will lead to a theoretical framework for rational development of innovative strategies for developing interventions to control seizures.