Neuroimaging of dynamic navigational codes

项目总结： 空间导航是所有移动生物必须应对的挑战。虽然很多都是 已知动物空间导航的神经机制， 人类空间导航的神经认知基础还不是很清楚。这部分是因为 技术限制：目前可用于非侵入性脑测量的最佳工具 活动是功能磁共振成像(FMRI)，而传统的fMRI分析方法 不适合于识别在动态的、自然主义的情况下引起的表示 例如不受限制的航海插曲。目前的项目将试图克服这一点 使用功能磁共振分析中的一项最新技术创新--体素编码的局限性 建模--识别调节主动导航的神经表征。具体来说，我们 将模拟在虚拟现实城市中导航时的fMRI响应 已知具有细胞坐标的空间变量与啮齿动物和 非人灵长类动物，然后我们将通过测试该模型是否可以预测功能磁共振来评估该模型 未用于训练模型的待定数据集中的响应。此外，我们将探索是否 由此揭示的表示足以解决同时本地化问题和 映射(SLAM)；也就是说，它们是否允许一个人在 探索一个新颖的环境，同时学习它的布局。目标1是建立 体素编码模型并使用它来识别特定区域内的神经表示 调节动态导航的大脑区域。目标2是检查以下方面的泛化 通过测试在一个虚拟环境中训练的模型是否足以解决 另一个重击的问题。如果成功，我们预计这个项目将有一个主要的 通过实现对空间分布的定量描述，对该领域产生持续的影响 在动态实时过程中，信息在人脑的多个区域进行编码 导航。这将允许我们测试关于所开发的空间表示的特定假设 来自动物文学，并潜在地允许这些文学被更好地利用 理解多种认知功能，这些认知功能依赖于相同的基础神经结构， 包括空间认知、情景记忆和想象力。此外，这个项目将 为未来测试新的假设提供必要的技术基础 航海的生理基础。

Project Summary: Spatial navigation is a challenge that must be met by all mobile organisms. Although much is known about the neural mechanisms that underlie spatial navigation in animals, the neurocognitive basis of spatial navigation in humans is much less clear. This is partly because of technical limitations: the best tool currently available for noninvasive measurement of brain activity is functional magnetic resonance imaging (fMRI), but traditional fMRI analysis methods are not well-suited for identifying representations elicited in dynamic, naturalistic situations such as unconstrained navigational episodes. The current project will attempt to overcome this limitation by using a recent technical innovation in fMRI analysis—voxel-wise encoding modeling—to identify the neural representations that mediate active navigation. Specifically, we will model the fMRI response during navigation within a virtual-reality city in terms of the spatial variables that are known to have known cellular coordinates correlates in rodents and non-human primates, and we will then evaluate the model by testing whether it predicts fMRI response in a held-out dataset not used to train the model. Additionally, we will explore whether the representations thus revealed suffice to solve the problem of simultaneous localization and mapping (SLAM); that is, whether they allow one to keep track of one's position during exploration of a novel environment while simultaneously learning its layout. Aim 1 is to build the voxel-wise encoding model and to use it to identify the neural representations within specific brain regions that mediate dynamic navigation. Aim 2 is to examine generalization across environments by testing whether a model trained in one virtual environment suffices to solve the problem of SLAM in another. If successful, we anticipate that this project will have a major and sustained impact on the field by achieving a quantitative description of how spatial information is encoded in multiple regions of the human brain during dynamic real-time navigation. This will allow us to test specific hypotheses about spatial representations developed from the animal literature, and potentially allow this literature to be leveraged to better understand multiple cognitive functions that rely on the same underlying neural architecture, including spatial cognition, episodic memory, and imagination. Moreover, this project will provide the essential technical foundation for future tests of novel hypotheses about the physiological basis of navigation.