CRCNS: Neural Populations, High Frequency Oscillations and EEG seizures

CRCNS：神经群体、高频振荡和脑电图癫痫发作

基本信息

批准号：
9047876
负责人：
Catherine A Schevon
金额：
$ 34.49万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-09-30 至 2018-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9047876
关键词：
Accounting Affect Area Biological Markers Brain Cells Clinical Computer Simulation Coupling Data Data Set Electroencephalogram Electrophysiology (science)Epilepsy Event Excision Failure Fire - disasters Frequencies Goals High Frequency Oscillation Hodgkin Disease Human Individual Interneurons Invaded Investigation Lead Linear Models Literature Measurement Methods Microelectrodes Modeling Modification Monitor N-Methylaspartate Neurons Operative Surgical Procedures Outcome Output Partial Epilepsies Patients Physiology Population Probability Property Pyramidal Cells Recruitment Activity Resected Role Seizures Shapes Signal Transduction Site Slice Source Structural defect Structure Synapses Synaptic Transmission Syndrome Techniques Testing Tissues Translating Validation Visual Work abstracting brain tissue driving force excitatory neuron extracellular improved in vivo insight interdisciplinary approach nervous system disorder network models postsynaptic public health relevance receptor relating to nervous system response restraint simulation stem

项目摘要

Abstract Epilepsy is a neurological disease affecting 65 million people worldwide. Patients with medically intractable seizures and with a clearly localized focus can often be successfully treated by surgical resection of the epileptic tissue. However, localization accuracy is limited, especially if overt structural abnormalities are absent. High frequency oscillations have been proposed as a key localizing biomarker. Unfortunately, progress in understanding the dynamics of seizure electrophysiology has been impeded by lack of measurement techniques for detailed monitoring of large networks both the temporal and spatial domains. We propose that multiscale network modeling incorporating known pathological mechanisms can provide useful insights into the circumstances under which high frequency oscillations are generated. We propose an interdisciplinary approach coupling modeling with an existing dataset of unique multiscale recordings, ranging from single-cell to large networks of millions of cells, during seizure activity in human cortical networks. Our proposal is focused around several important clinical questions. First, we seek to define precisely how high frequency spectral components in broadband clinical recordings reflect the pathological activity specific to seizing brain areas. Second, we will identify the neuronal mechanisms permitting localized pathological activity to propagate, and how this propagation affects the properties· of broadband clinical recordings. In contrast to the prior literature in this area, our approach explicitly incorporates the network effects of cellular dynamics known to occur during experimental seizures, I.e. paroxysmal depolarization and depolarization block of specific cell populations. In Aim 1 we use scalable and detailed modeling of individual network nodes to relate single-cell activity to the mesoscopic network scale. The focus of this aim is to determine how network interactions generate high frequency oscillations. Independently, in Aim 2 we test the hypothesis that these small, mesoscopic networks are responsible, via propagation of the high frequency components, for the high-gamma oscillation in macroelectrode signals. We accomplish this by generating the macroelectrode signal with a simple linear model that employs single-cell and small network signals as its input. In Aim 3 we relate the observed seizure propagation, due to failure of the inhibitory veto, to network dynamics associated with the paroxysmal depolarization block. For all aims we will validate simulated results with data recorded during experimental seizures in slices of human cortex (single-cell and local network activity), and in vivo microelectrode recordings during human seizures (multi-unit as well as local network activities).

摘要癫痫是一种神经系统疾病，影响着全球 6500 万人。患有医学上难治性癫痫发作且病灶明确的患者通常可以通过手术切除癫痫组织来成功治疗，但定位准确性有限，尤其是在不存在明显结构异常的情况下。不幸的是，由于缺乏对时间和空间大型网络进行详细监测的测量技术，高频振荡被认为是一种关键的定位生物标志物。我们提出，结合已知病理机制的多尺度网络建模可以为产生高频振荡的情况提供有用的见解，我们提出了一种与现有的独特多尺度记录数据集（从单细胞到大细胞）耦合建模的跨学科方法。在人类皮质网络的癫痫活动期间，由数百万个细胞组成的网络。我们的建议集中于几个重要的临床问题，首先，我们寻求精确定义宽带临床记录中的高频成分如何反映特定于大脑区域的病理活动；其次，我们将确定允许局部病理活动传播的神经机制。，以及这种传播如何影响宽带临床记录的特性与该领域的先前文献相比，我们的方法明确地结合了实验性癫痫发作期间发生的细胞动力学的网络效应，即阵发性癫痫发作。在目标 1 中，我们使用单个网络节点的可扩展且详细的模型来将单细胞活动与介观网络尺度联系起来。独立地，在目标 2 中，我们测试了这样的假设：这些小型介观网络通过高频分量的传播，导致了宏电极信号中的高伽马振荡。通过使用单细胞和小网络信号作为输入的简单线性模型生成大电极信号，我们将由于抑制否决失败而观察到的癫痫传播与阵发性去极化相关的网络动力学联系起来。对于所有目标，我们将使用人类皮层切片实验癫痫发作期间记录的数据（单细胞和局部网络活动）以及人类癫痫发作期间的体内微电极记录来验证模拟结果。（多单位以及本地网络活动）。