Representation of navigational and driving-related information across human brain

人脑中导航和驾驶相关信息的表示

• 批准号: 10392486
• 负责人: JACK L GALLANT
• 金额: $ 44.35万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10392486
• 关键词: Aging; Animals; Area; Automobile Driving; Behavior; Brain; Brain Diseases; Computer Models; Data; Data Analyses; Data Set; Destinations; Devices; Diagnosis; Environment; Functional Magnetic Resonance Imaging; Goals; Human; Image; Impairment; Individual; Laboratories; Link; Literature; Location; Magnetic Resonance Imaging; Measurable; Measures; Mediating; Methodology; Methods; Modeling; Monitor; Motor; Neurodegenerative Disorders; Pathway Analysis; Process; Publications; Publishing; Quality of life; Research; Resolution; Rodent; Route; Sensory; Series; Signal Transduction; System; Testing; Veins; Work; cognitive system; experimental study; high dimensionality; improved; innovation; insight; nervous system disorder; neuroimaging; neuromechanism; neurophysiology; predictive modeling; skills; virtual environment; virtual world; way finding

ABSTRACT Natural navigation is an important skill that engages many sensory, motor and cognitive systems. Because aging and degenerative brain disease both diminish the capacity to navigate in the real world, a better understanding of the brain mechanisms mediating navigation will improve diagnosis and monitoring of neurological and neurodegenerative diseases. Neurophysiological studies in animals have led to fundamental insights about the neural mechanisms mediating navigation. However, due to methodological limitations neuroimaging studies of navigation in humans have generally been less compelling than the animal work. We propose to overcome these limitations by using the NexGen 7T MRI scanner recently installed at UC Berkeley to measure brain activity during a naturalistic driving task. Driving is an excellent target for fMRI studies because is a common human navigation task that unfolds across a large and varied landscape, and on a timescale commensurate with fMRI; it engages many navigational brain systems; and it is impacted by aging and neurological diseases. Data will be analyzed by means of an innovative and powerful voxelwise modeling framework developed in PI Gallant's lab over the past 10 years, and validated in many publications. Computational models reflecting 33 different types of navigational features will be fit to the fMRI data separately for each voxel and in each individual subject. Model prediction accuracy and generalization will be cross-validated using separate data sets and subjects reserved for this purpose. The results will be used to test dozens of specific hypotheses about navigation drawn from the theoretical and experimental literature on both rodents and humans. These results will also be used to obtain a detailed functional parcellation of navigational representations in each individual and across the group, and to identify functional networks that represent specific navigation-related features. By combining naturalistic experiments, large-scale computational modeling, multiple hypothesis testing, data-driven functional parcellation and functional network analysis, this research will provide fundamental new information about the human brain mechanisms mediating navigation and their relationship to prior findings from the animal literature.

摘要 自然导航是一项重要的技能，涉及许多感官，运动和认知系统。因为 衰老和退化性脑部疾病都降低了在真实的世界中航行的能力， 了解大脑机制介导导航将改善诊断和监测 神经和神经变性疾病。动物神经生理学研究已经导致了 关于神经机制介导导航的见解。然而，由于方法上的限制， 人类导航的神经影像学研究通常不如动物研究那么引人注目。我们 我建议通过使用最近安装在加州大学伯克利分校的NexGen 7 T MRI扫描仪来克服这些限制 来测量自然驾驶任务中的大脑活动。驾驶是功能磁共振成像研究的一个很好的目标 因为这是一个常见的人类导航任务，在一个巨大而多样的景观中展开， 与功能性磁共振成像相当的时间尺度;它涉及许多导航大脑系统;并且它受到年龄的影响 和神经系统疾病。数据将通过创新和强大的体素建模进行分析 该框架在PI Gallant的实验室在过去10年中开发，并在许多出版物中得到验证。 反映33种不同类型的导航功能的计算模型将适合功能磁共振成像数据 分别针对每个体素和每个个体受试者。模型预测精度和泛化能力将 使用单独的数据集和为此目的保留的主题进行交叉验证。结果将用于测试 几十个关于导航的具体假设，来自理论和实验文献， 啮齿动物和人类。这些结果也将用于获得详细的功能分区的导航 每个人和整个群体中的代表，并确定代表的功能网络 具体的导航功能。通过结合自然主义实验，大规模计算 建模、多重假设检验、数据驱动的功能划分和功能网络分析， 这项研究将提供有关人脑调节导航机制的基本新信息 以及它们与动物文献中先前发现的关系。