Detailed Models of Network Bursts and Oscillations

描述（由申请方提供）：癫痫影响0.5-1%的人群，估计1/3至近1/2的患者无法获得令人满意的癫痫控制。大多数患者有局灶性发作，可能继发全身性发作。在癫痫发作病灶内，神经元的行为在时间和空间上演变：这反映在神经元亚群中低振幅、高频率（伽马[30-70 Hz]或更高[>70 Hz]）振荡的发生，就在更典型的癫痫样波开始之前，然后扩散。时空演变已在体外和体内癫痫发作模型中重现，因为有另一个不均匀性：不同的神经元放电模式在皮层层。还存在导致电描记癫痫发作的>100 Hz振荡的体外模型。更好地理解病灶内癫痫发作开始，随后扩散的详细病理生理学是重要的：a）它可以允许改善即将发生的癫痫发作的早期警告，并建议中止癫痫发作的替代措施; B）它可以建议药物治疗，例如构成推定的轴突间隙连接的蛋白质的结合剂，或所述连接的调节剂; c）通过指示最基于生理的术前EEG监测类型，例如，集中于通常不检查的频带，它可能细化手术候选人的术前评估。该提案描述了我们如何首先调整现有的单列网络模型用于研究非常快速的振荡和癫痫发作，然后构建多列皮层网络模型。可以在一列中研究癫痫发作的时间演变，在多列中研究空间演变。我们使用详细的模型神经元元素，沿着与突触和间隙连接。这些模型是必要的工具，用于整合不同类型的实验测量（场电位，单细胞电和光学记录），并占人口活动的贡献a）多种细胞类型的内在特性，B）内列突触和电连接，和c）列间突触连接。一个成功的模型将产生对实验合作者有用的具体和可测试的预测，他们过去曾在这个项目上密切合作，并将继续这样做。

DESCRIPTION (provided by applicant): Epilepsy affects from 0.5-1% of the human population, with an estimated 1/3 to nearly 1/2 of patients unable to attain satisfactory seizure control. A majority of patients have seizures of focal onset, possibly with secondary generalization. Within a seizure focus, the behavior of neurons evolves in time and space: this is reflected in the occurrence of low-amplitude, high-frequency (gamma [30-70 Hz], or higher [>70 Hz]) oscillations in a subpopulation of neurons, just prior to initiation of more typical epileptiform waves that then spread. Spatiotemporal evolution has been reproduced in in vitro and in vivo seizure models, as has another nonuniformity: different neuronal firing patterns across cortical layers. An in vitro model of >100 Hz oscillations leading into an electrographic seizure also exists. A better understanding of the detailed pathophysiology of within-focus seizure initiation, followed by spread, is important: a) it might allow improved early warning of an impending seizure, and suggest alternative measures to abort the seizure; b) it could suggest drug treatments, for example binders to proteins constituting putative axonal gap junctions, or modulators of said junctions; c) it could possibly refine the preoperative evaluation of surgical candidates, by indicating the most physiologically based types of preoperative EEG monitoring, for example, concentrating on frequency bands not typically examined. This proposal describes how we can first adapt an existing single-column network model for the study of very fast oscillations and seizure initiation, and then build a multi-column cortical network model. Temporal evolution of a seizure can be studied in one column, and spatial evolution in multiple columns. We use detailed model neuronal elements, along with synaptic and gap junctional connectivity. Such models are necessary as tools for integrating diverse types of experimental measurements (field potentials, single-cell electrical and optical recordings), and for accounting for the contributions to population activity made by a) intrinsic properties of multiple cell types, b) within-column synaptic and electrical connectivity, and c) between-column synaptic connectivity. A successful model will generate specific and testable predictions useful to experimental collaborators, who have worked closely on this project in the past and continue to do so.