Dynamics of long range network interactions in focal epilepsy

局灶性癫痫中远程网络相互作用的动态

• 批准号: 10198042
• 负责人: Catherine A Schevon
• 金额: $ 61.24万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10198042
• 关键词: Address; Affect; Animal Model; Area; Automobile Driving; Award; Behavior; Biological Markers; Biophysics; Brain; Brain Diseases; Cell physiology; Clinical; Collection; Complement; Complex; Computer Models; Coupled; Data; Data Set; Decision Making; Development; Distant; Electroencephalography; Electrophysiology (science); Engineering; Epilepsy; Event; Excision; Failure; Focal Seizure; Foundations; Frequencies; Generations; Goals; Health; High Frequency Oscillation; Human; Impact Seizures; In Vitro; Incidence; Interdisciplinary Study; Intervention; Link; Lobe; Location; Machine Learning; Masks; Mathematics; Medical; Methods; Microelectrodes; Monitor; Neurons; Operative Surgical Procedures; Partial Epilepsies; Pathologic; Pathway Analysis; Patients; Predictive Value; Procedures; Process; Quality of life; Research Personnel; Role; Sampling; Seizures; Shapes; Signal Transduction; Site; Source; Specificity; Study models; Techniques; Terminology; Testing; Therapeutic; Time; Travel; Universities; Weight; base; improved; innovation; millimeter; minimally invasive; multi-scale modeling; neurophysiology; parallel computer; relating to nervous system; support vector machine; surgery outcome; voltage

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.

项目概要 癫痫是世界上最严重的脑部疾病，影响着全世界近 5000 万人。 尽管采取了最佳的医疗管理，但其中约 30% 的患者癫痫发作仍控制不佳， 从而促进治疗管理的进步。 癫痫症，全面了解驱动癫痫发作的细胞过程如何与大规模癫痫发作相关 虽然癫痫发作会影响大面积的大脑区域，并且通常会影响多个脑叶，但驱动因素却是必要的。 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小规模过程的大脑信号驱动的 相反，认为自然发生的癫痫发作可能归因于大规模的观点仍然不清楚。 神经质量效应（即癫痫网络）是一个持续争论的主题，之前我们定义了一个关键。 周围抑制在形成癫痫发作部位和小空间尺度的脑电图记录中的作用。 我们现在提出，环绕抑制在毫米尺度上具有双重作用，它的突然失效允许。 在距癫痫病灶较远的地方，强烈的局部抑制可以掩盖癫痫发作的进展。 癫痫发作的兴奋作用，可能有助于加速癫痫发作终止，而抑制作用减弱可能 允许在远处、不连续的癫痫发作部位出现发作活动。 标准脑电图解释方法无法识别，并且可能是癫痫手术的关键因素 我们认为，一旦建立，多个发作发生器就会充当延迟耦合振荡器， 展示同步甚至暂时逆转的活动，这会导致复杂且有时的结果。 违反直觉的网络行为可能很难从脑电图记录中进行逆向工程。 然而，即使是颅内脑电图记录也只能提供有限的神经活动视图。 跨学科研究小组，拥有癫痫、临床神经生理学、计算方面的综合专业知识 建模和数学将对癫痫的神经影响因素进行全面的研究 利用同时微电极和宏电极记录的独特组合数据集的网络 使用机器学习方法，我们将应用这些信息来开发一个 基于脑电图放电、高频振荡的推断源的多变量脑电图生物标志物 极低频（DC）变化并评估其对切除后手术结果的预测价值。 预计该项目将为合理制定创新战略提供理论框架 制定控制癫痫发作的干预措施。