Detailed Models of Network Bursts and Oscillations

描述(申请人提供)：癫痫影响从0.5-1%的人口，估计有1/3到近1/2的患者无法达到满意的癫痫控制。大多数患者有局灶性发作，可能有继发性全身性发作。在癫痫灶内，神经元的行为在时间和空间上演变：这反映在一群神经元中发生低幅度、高频(伽马[30-70赫兹]或更高[&gt；70赫兹])振荡，就在更典型的癫痫样波开始之前，然后传播。在体外和体内的癫痫模型中，时空进化已经被复制，还有另一个不一致性：不同的神经元在皮层上的放电模式。一个体外模型的&gt；100赫兹振荡导致脑电癫痫也存在。更好地了解病灶内癫痫发作的启动和扩散的详细病理生理学是很重要的：a)它可以改善对即将发生的癫痫的早期预警，并提出终止癫痫的替代措施；b)它可以建议药物治疗，例如与可能构成轴突缝隙连接的蛋白质结合，或上述连接的调节器；c)它可能通过指出最基于生理的术前脑电监测类型，例如，集中在通常没有检查的频段，来完善对手术候选者的术前评估。这项建议描述了我们如何首先采用现有的单柱网络模型来研究非常快的振荡和癫痫发作开始，然后建立一个多柱皮质网络模型。癫痫发作的时间演变可以在一列中研究，空间演变可以在多列中研究。我们使用详细的模型神经元元素，以及突触和缝隙连接。这种模型对于整合不同类型的实验测量(场势、单细胞电学和光学记录)以及解释a)多种细胞类型的固有属性、b)列内突触和电连接以及c)列间突触连接对种群活动的贡献是必要的。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，他们过去一直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.