CRCNS: Neural Populations, High Frequency Oscillations and EEG seizures

摘要 癫痫是一种神经系统疾病，影响全球6500万人。具有医学上难治性癫痫发作和具有明确局部病灶的患者通常可以通过手术切除癫痫组织来成功治疗。然而，定位精度是有限的，特别是如果明显的结构异常是缺席。高频振荡已被提出作为一个关键的定位生物标志物。不幸的是，在理解癫痫发作电生理学的动态进展一直受到阻碍，缺乏测量技术的详细监测大型网络的时间和空间域。我们建议，多尺度网络建模，结合已知的病理机制，可以提供有用的见解的情况下，产生高频振荡。我们提出了一个跨学科的方法耦合建模与现有的数据集的独特的多尺度记录，从单细胞到大型网络的数百万个细胞，在人类皮层网络癫痫发作活动。 我们的建议集中在几个重要的临床问题。首先，我们试图精确地定义宽带临床记录中的高频频谱成分如何反映特定于癫痫发作脑区的病理活动。其次，我们将确定允许局部病理活动传播的神经元机制，以及这种传播如何影响宽带临床记录的特性。与此领域的现有文献相比，我们的方法明确地结合了已知在实验性癫痫发作期间发生的细胞动力学的网络效应，即特定细胞群的阵发性去极化和去极化阻滞。在目标1中，我们使用单个网络节点的可扩展和详细建模，将单细胞活动与介观网络规模联系起来。这一目标的重点是确定网络相互作用如何产生高频振荡。独立地，在目标2中，我们测试了这样的假设，即这些小的介观网络是负责的，通过高频分量的传播，在宏电极信号的高伽马振荡。我们通过用一个简单的线性模型生成宏电极信号来实现这一点，该模型采用单细胞和小网络信号作为其输入。在目标3中，我们将观察到的癫痫发作传播（由于抑制性否决失败）与阵发性去极化阻滞相关的网络动力学联系起来。对于所有的目标，我们将验证模拟结果与数据记录在人类大脑皮层切片实验癫痫发作（单细胞和局部网络活动），并在人体癫痫发作（多单元以及局部网络活动）在体内微电极记录。

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).