Neural signatures of virtual and real-world navigation and spatial learning

虚拟和现实世界导航和空间学习的神经特征

• 批准号: 10393754
• 负责人: Kathryn Nicole Graves
• 金额: $ 4.7万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10393754
• 关键词: Address; Alzheimer' s disease patient; Animal Model; Applied Research; Area; Basic Science; Behavior; Behavioral; Binding; Biological Assay; Brain; Cells; Chronic; Clinical; Cognition; Complex; Computer Models; Coupling; Data; Data Collection; Diagnosis; Discipline; Disease; Electroencephalography; Electrophysiology (science); Environment; Epilepsy; Ethics; Felis catus; Fire - disasters; Foundations; Functional Magnetic Resonance Imaging; Goals; Hippocampus (Brain); Human; Individual; Institution; Interdisciplinary Study; Investigation; Learning; Literature; Location; Machine Learning; Maps; Measures; Medial; Memory; Memory impairment; Mentors; Methodology; Methods; Modeling; Modernization; Movement; Nerve Degeneration; Neurobiology; Organism; Participant; Patients; Phase; Population; Positioning Attribute; Prefrontal Cortex; Primates; Principal Investigator; Process; Proxy; Public Health; Rattus; Research; Research Methodology; Research Project Grants; Research Training; Resolution; Resources; Rodent Model; Role; Series; Signal Transduction; Source; Structure; System; Techniques; Testing; Time; Training; Training Programs; Update; Work; base; behavior measurement; classical conditioning; clinical application; cognitive neuroscience; design; experience; experimental study; high resolution imaging; hippocampal atrophy; human model; innovation; learned behavior; multimodality; neural implant; neuroimaging; neurophysiology; neurotransmission; novel; patient population; post-doctoral training; programs; relating to nervous system; spatial memory; statistical learning; statistics; theories; virtual; virtual reality; way finding

PROJECT SUMMARY Navigation is one of our most foundational behavioral capacities, and substantial work using rodent models has established the hippocampus as a critical contributor to this complex behavior. However, efforts to model these effects in humans have been limited, both in scope and generalizability. Little work has explored the interaction in the hippocampus between navigation and foundational non-navigation learning processes, like statistical learning, and it therefore remains unclear how navigation processes are integrated with and may be supported by these learning mechanisms. Further, studies in humans have primarily consisted of virtual navigation, limiting applicability and relevance of the established neural signals to real-world navigation behavior. Understanding how navigation and statistical learning compete or cooperate in the human hippocampus will significantly inform models of memory systems and consolidation, and addressing these questions in a real-world paradigm will close a wide gap in our understanding of human navigation. This requires a multimodal approach, coupling the high temporal and spatial resolution of intracranial EEG (iEEG) in epilepsy patients with quantitative behavioral measures of human navigation and learning. In my doctoral dissertation work thus far, I have used behavioral, computational, and iEEG methods to test hypotheses about the interaction between statistical learning and spatial navigation in the human hippocampus and related structures. My work has produced impactful findings via novel computational modeling techniques and innovative multivariate iEEG analyses. In my proposed research and training program, I will use a cutting-edge research method to collect direct neural recordings from chronic brain implants in human participants as they physically ambulate in the real world. Through my multidisciplinary research mentors, this experience will provide me training with novel research methodology and ecologically valid experiment design, as well as clinical perspectives, considerations, and practices for working with patients with epilepsy. In my proposed postdoctoral training phase, I will pursue a more clinical focus, returning to the question of how modern cognitive neuroscience can guide assessments and therapies for hippocampal disease. I will gain theoretical and practical experience with high-resolution imaging and data collection with hippocampal atrophy populations, including epilepsy and Alzheimer’s patients. This research program has the potential to inform and substantially extend models of human navigation and memory by exploring the intersection of multiple processing demands on the hippocampus as a shared neural resource, and how these processes operate in the real world. This work represents the convergence of disparate literatures on hippocampal function, and informs diagnosis and treatment of hippocampal patients. This program will equip me not only with expertise in behavioral, computational, and neuroscientific methodologies, but the basic and clinical theoretical acumen necessary to conduct independent investigation as a principal investigator across disciplines.

项目摘要 导航是我们最基本的行为能力之一， 已经确定海马体是这种复杂行为的关键因素。然而，努力塑造 这些对人类的影响在范围和普遍性上都是有限的。很少有工作探索了 海马体中导航和基础非导航学习过程之间的相互作用，如 统计学习，因此仍然不清楚导航过程如何与 这些学习机制的支持。此外，对人类的研究主要包括虚拟 导航，限制已建立的神经信号对现实世界导航的适用性和相关性 行为了解导航和统计学习如何在人类中竞争或合作 海马体将为记忆系统和巩固的模型提供重要信息，并解决这些问题。 在现实世界的范例中提出的问题将缩小我们对人类导航理解的巨大差距。这 需要多模态方法，将颅内EEG（iEEG）的高时间和空间分辨率耦合到 癫痫患者的人类导航和学习的定量行为测量。在我的博士论文中 到目前为止，我已经使用了行为，计算和iEEG方法来测试关于 统计学习和空间导航之间的相互作用在人类海马和相关 结构.我的工作通过新颖的计算建模技术产生了有影响力的发现， 创新的多变量iEEG分析。在我提出的研究和培训计划中，我将使用一种尖端的 一种研究方法，从人类参与者的慢性大脑植入物中收集直接神经记录， 在真实的世界中行走。通过我的多学科研究导师，这种经验将 为我提供新的研究方法和生态有效的实验设计培训，以及 临床观点，注意事项和与癫痫患者一起工作的实践。在我的提议中， 在博士后培养阶段，我将更多地关注临床，回到如何现代化的问题上来 认知神经科学可以指导海马疾病的评估和治疗。我将获得理论上的 以及海马萎缩的高分辨率成像和数据收集的实践经验 包括癫痫和阿尔茨海默病患者。这项研究计划有可能提供信息， 通过探索多个领域的交叉点， 处理要求海马体作为共享的神经资源，以及这些过程如何在 真实的世界这项工作代表了海马功能的不同文献的融合， 为海马患者的诊断和治疗提供信息。该计划将装备我不仅与专业知识， 行为，计算和神经科学方法，但基础和临床理论的敏锐性， 作为跨学科的主要研究者进行独立调查是必要的。