Dynamics of long range network interactions in focal epilepsy

项目摘要 癫痫是世界上最突出的严重脑部疾病，影响全球近5000万人。为 在这些患者中，约30%的患者尽管进行了最佳的医疗管理，但癫痫发作仍然控制不佳， 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，彻底了解如何驱动癫痫发作的细胞过程与大规模的 需要网络效应。虽然癫痫发作会影响大面积的大脑区域，通常是多个脑叶， 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表征， 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， 仍不清楚相反，认为自然发生的癫痫发作可能归因于大规模的 神经质量效应（即，癫痫网络）是一个持续辩论的主题。之前，我们定义了一个键 环绕抑制在形成发作部位和小空间尺度上癫痫发作的EEG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，它的突然失效使得 提前发作。在距癫痫发作病灶较远的地方，强烈的局部抑制作用掩盖了癫痫发作。 癫痫发作的兴奋作用，可能有助于加快癫痫发作终止，而减弱的抑制可能 允许在远处非相邻的癫痫发作部位出现发作活动。多个癫痫灶可能会 标准EEG解释方法无法识别，可能是癫痫手术中的关键因素 失败我们假设，一旦建立，多个发作发生器的行为作为延迟耦合振荡器， 表明活动是同步的，甚至是时间上相反的。这导致了复杂的，有时 违反直觉的网络行为，从EEG记录进行逆向工程可能具有挑战性。典型地， 然而，即使颅内EEG记录也仅提供神经活动的有限视图。在这个项目中，一个 在癫痫、临床神经生理学、计算神经生理学、 建模，数学将进行全面的研究神经元的贡献癫痫 利用同时微电极和宏电极记录的独特组合数据集的网络 人类癫痫发作使用机器学习方法，我们将应用这些信息来开发一个 基于EEG放电、高频振荡的推断源的多变量EEG生物标志物，以及 极低频（DC）偏移，并评估其对切除术后手术结果的预测价值。我们 预计该项目将导致合理发展创新战略的理论框架 制定干预措施来控制癫痫发作。

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.