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.

项目摘要/摘要 学习和记住资源的位置，同时避免危险的位置是一个主要的挑战 对于复杂的生物体来说。尽管人们已经很好地研究了已知环境的神经表示法， 对于这种空间知识最初是如何获得的，人们知之甚少。在这里，我们解决了 人们如何学习和记忆新环境的重要问题。特别是，我们的目标是 研究空间知识的一种基本类型，即位置之间的路径连接(‘graph 知识‘)。拓扑图由由路径边链接的位置节点组成，路径边可以生成路线， 但没有精确的公制距离和角度，就像地铁地图一样。当谈到学习空间 知识，这似乎是直觉的，主动导航应该有助于，然而，我们还不明白 这一优势背后的机制。我们的主要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，特别是在主动决策时 探险。结合决策和强化学习机制，小灵通路径是 假想是为了在学习过程中促进记忆。基于此模型，交互和功能 小灵通电路内的通信对新的学习至关重要。这项基础基础研究的目标是 建议1)确定导航学习的轨迹，包括行为网络和大脑网络 动力学，2)识别图形学习过程中主动决策背后的潜在大脑机制， 3)回答决策与记忆之间关系的基本问题。在……里面 具体目标1，我们将确定有助于图形学习的探索行为。我们将比较一个 图形结构的多样性、环境的开放性和规模决定了图形学习的健壮性。 在特定的目标2中，我们将使用新的功能磁共振方法来检查凝聚性群体形成的变化。 大脑区域(“社区”)，利用学习的动力。我们将使用这项技术来识别大脑 与被动学习相比，支持主动学习的网络。在特定的目标3中，我们将比较大脑 图学习中的网络与非空间和非欧几里德图中的网络相同。这些研究将检验 用于在不同类型的图表中通用的大脑网络，以及空间图表特有的大脑网络。这个 结果将提供对导航、学习和记忆的基本过程的洞察，并将有助于 回答有关超越航海领域的学习的问题。小灵通回路与心智有关 涉及强化学习和奖赏学习的障碍，包括强迫症、抑郁症和帕金森氏症。 这些研究将建立空间导航和小灵通电路之间的重要联系，并将形成基础 用于导航、学习、记忆和小灵通电路故障的计算方法。遥远的- 本研究达到的影响包括评估临床上该回路的功能和功能障碍。 使人们更好地了解疾病机制。