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

基本信息

批准号：
10705013
负责人：
Kathryn Nicole Graves
金额：
$ 4.78万
依托单位：
YALE UNIVERSITY
依托单位国家：
美国
项目类别：
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/10705013
关键词：
Address Alzheimer&apos 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

项目摘要

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.

项目概要导航是我们最基本的行为能力之一，并且使用啮齿动物模型进行了大量工作已经确定海马体是这种复杂行为的关键贡献者，但是，努力建模。这些对人类的影响无论是范围还是普遍性都是有限的。海马体中导航和基础非导航学习过程之间的相互作用，例如统计学习，因此仍不清楚导航过程如何与此外，在这些学习机制的支持下，人类研究主要包括虚拟。导航，限制了已建立的神经信号与现实世界导航的适用性和相关性了解导航和统计学习在人类中如何竞争或合作。海马体将为记忆系统和巩固模型提供重要信息，并解决这些问题现实世界范式中的问题将缩小我们对人类导航理解的巨大差距。需要采用多模态方法，将颅内脑电图 (iEEG) 的高时间和空间分辨率耦合到在我的博士论文中，对癫痫患者进行了人类导航和学习的定量行为测量。到目前为止，我已经使用行为、计算和 iEEG 方法来检验关于以下方面的假设：人类海马体中统计学习和空间导航之间的相互作用及相关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。在我提出的研究和培训计划中，我将使用尖端的多变量 iEEG 分析。从人类慢性大脑植入物中收集直接神经记录的研究方法通过我的多学科研究导师，这种体验将在现实世界中移动。为我提供新颖的研究方法和生态有效的实验设计的培训，以及在我的提议中，我提出了治疗癫痫患者的临床观点、考虑因素和实践。博士后培养阶段，我会追求更加临床的关注，回到如何现代的问题认知神经科学可以对海马疾病进行评估和治疗，我将获得理论知识。以及海马萎缩高分辨率成像和数据收集的实践经验该研究项目有可能为包括癫痫症和阿尔茨海默氏症患者在内的人群提供信息和帮助。通过探索多个领域的交叉点，极大地扩展了人类导航和记忆的模型海马体作为共享神经资源的处理需求，以及这些过程如何运作这项工作代表了关于海马功能的不同文献的融合，以及该计划不仅为我提供了海马患者的诊断和治疗方面的专业知识。行为、计算和神经科学方法，但基础和临床理论敏锐度作为主要研究者进行跨学科的独立调查是必要的。