Navigational learning and memory: Cognitive graphs, active decision making, and brain network dynamics

导航学习和记忆：认知图、主动决策和大脑网络动力学

• 批准号: 10367112
• 负责人: Elizabeth Chrastil
• 金额: $ 53.12万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10367112
• 关键词: Address; Animal Model; Area; Attention deficit hyperactivity disorder; Basic Science; Behavior; Behavioral; Brain; Brain imaging; Clinical; Cognitive; Communication; Communities; Complex; Corpus striatum structure; Dangerousness; Decision Making; Disease; Drug Addiction; Environment; Episodic memory; Functional Magnetic Resonance Imaging; Functional disorder; Goals; Graph; Hippocampus (Brain); Home; Human; Intuition; Investigation; Knowledge; Learning; Link; Location; Major Depressive Disorder; Maps; Medial; Memory; Mental Depression; Mental disorders; Methods; Modeling; Obsessive-Compulsive Disorder; Organism; Outcome; Parkinson Disease; Pathway interactions; Pattern; Peripheral; Persons; Population; Process; Psychological reinforcement; Research; Research Proposals; Resources; Rewards; Risk; Route; Structure; Subway; Surveys; Techniques; Temporal Lobe; Testing; Theoretical model; Time; base; cohesion; comparative; computational basis; educational atmosphere; entorhinal cortex; experience; graph learning; insight; interest; knowledge graph; network models; novel; relating to nervous system; spatial memory; support network; virtual reality; way finding; young adult

PROJECT SUMMARY/ABSTRACT Learning and remembering the locations of resources while avoiding dangerous locations is a major challenge for complex organisms. Although the neural representations of known environments have been well studied, comparatively little is known about how that spatial knowledge is acquired in the first place. Here, we address the important problem of how people learn and remember new environments. In particular, we aim to investigate a fundamental type of spatial knowledge, the path connections between locations (‘graph knowledge’). A topological graph consists of place nodes linked by path edges which could generate routes, but without exact metric distances and angles, like a subway map. When it comes to learning spatial knowledge, it seems intuitive that active navigation should facilitate, however, we do not yet understand the mechanisms behind this advantage. Our overarching hypothesis is that interactions of a prefrontal- hippocampal-striatal (PHS) circuit support graph learning, particularly during active decision making about exploration. Combined with decision making and reinforcement learning mechanisms, the PHS pathway is hypothesized to facilitate memory during learning. Based on this model, interactions and functional communication within the PHS circuit are critical to new learning. The goals of this fundamental basic research proposal are to 1) determine the trajectory of navigational learning, including both behavioral and brain network dynamics, 2) identify the underlying brain mechanisms behind active decision making during graph learning, and 3) answer fundamental questions about the relationship between decision making and memory. In Specific Aim 1, we will determine exploration behaviors that facilitate graph learning. We will compare a variety of graph structures, environmental openness, and scale to determine the robustness of graph learning. In Specific Aim 2, we will use novel fMRI methods to examine changes in the formation of cohesive groups of brain areas (‘communities’), harnessing the dynamics of learning. We will use this technique to identify brain networks supporting active compared to passive learning. In Specific Aim 3, we will compare the brain networks found in graph learning with those in non-spatial and non-Euclidean graphs. These studies will test for brain networks common across different types of graphs, as well as those unique to spatial graphs. The outcomes will provide insights into fundamental processes of navigation, learning, and memory, and will help answer questions about learning beyond the realm of navigation. The PHS circuit is relevant to mental disorders involving reinforcement and reward learning, including OCD, depression, and Parkinson’s Disease. These studies will establish a vital link between spatial navigation and the PHS circuit, and will form the basis for computational approaches to navigation, learning, memory, and breakdowns of the PHS circuit. The far- reaching impact of this research includes assessing the function and dysfunction of this circuit in clinical populations to better understand disease mechanisms.

项目概要/摘要 学习并记住资源的位置，同时避开危险的位置是一个重大挑战 尽管已知环境的神经表征已经被深入研究， 首先，我们对如何获取空间知识知之甚少。 我们特别致力于解决人们如何学习和记忆新环境的重要问题。 研究空间知识的基本类型，即位置之间的路径连接（“图 拓扑图由通过路径边连接的位置节点组成，可以生成路线， 但在学习空间时没有精确的度量距离和角度，例如地铁地图。 知识，主动导航似乎很直观，应该会促进，但是，我们还不了解 我们的总体假设是前额叶的相互作用。 海马-纹状体 (PHS) 回路支持图形学习，特别是在主动决策过程中 结合决策和强化学习机制，PHS 路径是 基于这个模型，在学习过程中不遗余力地促进记忆。 PHS 电路内的通信对于新的学习至关重要。 建议 1）确定导航学习的轨迹，包括行为和大脑网络 动力学，2）识别图学习过程中主动决策背后的潜在大脑机制， 3）回答有关决策和记忆之间关系的基本问题。 具体目标 1，我们将确定促进图学习的探索行为，我们将比较一个。 图结构的多样性、环境的开放性和规模来决定图学习的鲁棒性。 在具体目标 2 中，我们将使用新颖的功能磁共振成像方法来检查粘性群体形成的变化 大脑区域（“社区”），利用学习的动态我们将使用这种技术来识别大脑。 支持主动学习和被动学习的网络在特定目标 3 中，我们将比较大脑。 这些研究将测试图学习中发现的网络与非空间和非欧几里得图中的网络。 适用于不同类型图之间常见的大脑网络，以及空间图特有的大脑网络。 结果将提供对导航、学习和记忆的基本过程的见解，并将有助于 回答有关导航领域之外的学习的问题 PHS 电路与心理相关。 涉及强化和奖励学习的疾病，包括强迫症、抑郁症和帕金森病。 这些研究将在空间导航和 PHS 电路之间建立重要的联系，并将构成基础 用于导航、学习、记忆和 PHS 电路故障的计算方法。 这项研究的影响包括评估该回路在达到临床效果方面的功能和功能障碍 人群更好地了解疾病机制。