Functional Organization of Navigational Coding in the Human Brain

人脑中导航编码的功能组织

• 批准号: 1829398
• 负责人: Chantal Stern
• 金额: $ 66.98万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1829398&HistoricalAwards=false
• 关键词: Functional; Organization; Navigational; Coding; Human

With funding from NSF, the researchers will study how the human brain represents dimensions that are needed for finding our way around in the world. The interdisciplinary approach combines cognitive and visual neuroscience methods, aimed towards understanding how the human brain processes spatial navigation information. The studies incorporate behavioral, cognitive, and neuroimaging techniques to examine how the human brain codes for distance, heading direction, speed, and time, which may contribute to higher-level navigation mechanisms, such as planning a route to a known destination or finding one's way home. The results have the potential to impact other fields, including robotics and spatial sciences. In robotics, autonomous systems have difficulty determining whether they have successfully returned back to their origin after an outbound journey, which robotics researchers call the loop closure problem. In contrast, humans and animals can readily solve this problem. Understanding how visual information is used to localize and orient will provide knowledge that could potentially facilitate innovation in mobile robots and self-driving cars or training for more efficient navigation in humans. Greater knowledge of the basic properties of navigation in humans could also lead to improved electronic navigation systems, emergency response training, and more effective transportation signage.The scientific goals harness the strengths of cognitive neuroscience, visual neuroscience, and spatial sciences to examine navigation in humans. While much is known about the navigation system in rodents, the rat and primate have fundamentally different visual systems. Contributions from the visual system provide critical information necessary for self-motion guided navigation, and the theoretical basis for this proposal stems from computational models that posit that perceptual information, including optic flow, speed, and direction signals, are necessary for successful navigation. The researchers propose a framework in which spatial representations transform from a retinotopic to a spatiotopic organization. This framework posits testable hypotheses about the nature of self-motion guided navigational representations in the brain. A series of experiments will examine how the human brain codes lower-level representations, such as distance, heading direction, speed, and time, which may serve as basis functions for generating higher-level level navigational representations. The studies will examine how these selective properties are spatially organized in the brain, as well as the higher-level computations that bring this information together to compute path integration. To do so, the proposed studies employ innovative functional MRI paradigms adapted from visual neuroscience, including population receptive-field mapping, phase-encoded analyses, and model-based time-series analyses. The proposed work is critical for extending computational models of navigation to the systems level in humans.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在美国国家科学基金会的资助下，研究人员将研究人类大脑如何代表我们在世界上寻找道路所需的维度。跨学科的方法结合了认知和视觉神经科学方法，旨在了解人类大脑如何处理空间导航信息。这些研究结合了行为、认知和神经成像技术来研究人类大脑如何编码距离、前进方向、速度和时间，这可能有助于更高层次的导航机制，如规划一条通往已知目的地的路线或找到回家的路。研究结果有可能影响其他领域，包括机器人和空间科学。在机器人技术中，自主系统很难确定它们在一次出站旅行后是否成功返回了原点，机器人研究人员称之为闭环问题。相比之下，人类和动物可以轻易地解决这个问题。了解视觉信息如何用于定位和定向，将为移动机器人和自动驾驶汽车的创新提供潜在的知识，或者为人类提供更有效的导航培训。更多地了解人类导航的基本特性，也可以改善电子导航系统、应急响应培训和更有效的交通标志。科学目标利用认知神经科学、视觉神经科学和空间科学的优势来研究人类的导航。虽然我们对啮齿类动物的导航系统了解很多，但大鼠和灵长类动物的视觉系统有着根本不同。视觉系统的贡献为自运动制导导航提供了必要的关键信息，这一建议的理论基础源于计算模型，该模型假设感知信息，包括光流、速度和方向信号，是成功导航所必需的。研究人员提出了一个框架，在该框架中，空间表征从视网膜位组织转变为空间位组织。这个框架对大脑中自我运动引导的导航表征的本质提出了可检验的假设。一系列的实验将研究人类大脑如何编码较低层次的表征，如距离、方向、速度和时间，这些可能作为生成更高层次导航表征的基础函数。这些研究将研究这些选择性属性是如何在大脑中进行空间组织的，以及将这些信息整合在一起以计算路径整合的高级计算。为此，本研究采用了视觉神经科学的创新功能MRI范式，包括群体接受场映射、相位编码分析和基于模型的时间序列分析。所提出的工作对于将导航的计算模型扩展到人类的系统水平至关重要。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。