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