Grid Cell Dynamics During Navigation In Virtual Reality

描述（由申请人提供）：内侧内嗅皮层（MEC）有助于导航和情景记忆，这是许多退行性疾病和精神疾病中退化的基本认知功能。网格单元的发现提供了MEC功能的关键，网格单元在一组六边形网格镶嵌空间的顶点上开火。网格单元系统被假设在导航过程中执行路径整合，并成为空间环境的地图。由于其发射场的惊人的规律性，网格单元产生了广泛的理论兴趣，并提出了许多模型来解释网格是如何形成的，它们是如何组织在微电路中，以及它们如何使用独特的（自我运动）信息来进行路径整合。因此，网格细胞系统提供了在机械水平上研究有认知意义的神经计算的机会。在这里，我们利用最新的技术进步，包括我们实验室以前开发的啮齿动物虚拟现实方法，来研究GRI细胞的细胞内，微电路和整合特性，有三个目标：1。目前的网格细胞模型可以重现六边形晶格放电模式，但它们预测不同的细胞内膜电位时间过程，反映不同的潜在细胞或网络机制。为了测试这些预测，在目标1中，我们将利用我们的虚拟现实系统实现的头部固定导航，在行为期间从网格细胞进行细胞内记录。将对膜电压时间序列进行统计分析，以检查是否存在斜坡和θ振荡等特征，以及它们是否与放电场的位置相关。例如，我们将研究θ振荡幅度是否在发射场中较大，以及θ频率是否随鼠标速度增加，如网格细胞的θ干扰模型所预测的那样。2.）网格单元并不相同，但具有不同的尺度和相移，可能反映不同的功能模块。与这一想法相一致的是，越来越多的证据表明MEC中存在解剖学定义的细胞簇。为了描述功能模块和解剖簇之间的联系，在目标2中，我们将在虚拟导航过程中使用细胞分辨率双光子钙成像，以微电路规模提供MEC中已识别网格细胞的空间组织的第一次测量。特别是，我们将绘制网格细胞属性（空间尺度和相位）和细胞色素氧化酶丰富的补丁之间的关系，并确定是否有急剧中断空间尺度沿着背腹轴。3.）第三章网格单元被认为执行路径整合，这是一个主导当前关于MEC功能作用的想法。在目标3中，我们将使用虚拟现实来控制所有提供位置信息的感官线索，以严格测试路径整合假设。总之，这些目标应该推进我们的理解的单细胞，微电路和网格单元的计算特性。

DESCRIPTION (provided by applicant): The medial entorhinal cortex (MEC) contributes to navigation and episodic memory, essential cognitive functions degraded in many degenerative and psychiatric disorders. A key to MEC function was provided by the discovery of grid cells, which fire on the vertices of a set of hexagonal lattices tessellating space. The grid cell system has been hypothesized to perform path integration during navigation and to be a map of the spatial environment. Because of the striking regularity of their firing fields, grid cells have generated widespread theoretical interest, and numerous models have been proposed to explain how grids are formed, how they are organized in microcircuits, and how they might use idiothetic (self motion) information to path integrate. The grid cell system therefore offers the opportunity to study a cognitively meaningful neural computation at a mechanistic level. Here we leverage recent technical advances, including virtual reality methods for rodents previously developed in our lab, to examine the intracellular, microcircuit, and integrative properties of gri cells in three aims: 1.) Current grid cell models can reproduce hexagonal lattice firing patterns but they predict different intracellular membrane potential time courses that reflect different underlying cellular or network mechanisms. To test these predictions, in Aim 1, we will take advantage of head-fixed navigation enabled by our virtual reality system to make intracellular recordings from grid cells during behavior. Statistical analysis will be performed on the membrane voltage time series to examine if characteristic features such as ramps and theta oscillations are present and if they correlate with the location of the firing fields. For example,we will examine if theta oscillation amplitude is larger in firing fields, and if theta frequency increases with mouse velocity, as predicted by theta interference models of grid cells. 2.) Grid cells are not identical, but have different scales and phase shifts that may reflect distinct functional modules. Consistent with this idea, converging evidence points to the existence of anatomically defined clusters of cells in MEC. To delineate the link between functional modules and anatomical clusters, in Aim 2 we will use cellular-resolution two-photon calcium imaging during virtual navigation to provide the first measurements of spatial organization, at the microcircuit scale, of identified grid cells in MEC. In particular, we will map the relationship between grid cell properties (spatial scale and phase) and cytochrome oxidase rich patches, and determine whether there are sharp breaks in spatial scale along the dorsoventral axis. 3.) Grid cells are thought to perform path integration, an idea that dominates the current thinking about the functional role of the MEC. In Aim 3 we will use virtual reality to control all sensory cues providing information about position in order to rigorously test the path integration hypothesis. Together, these aims should advance our understanding of the single-cell, microcircuit, and computational properties of grid cells.