Representation of navigational and driving-related information across human brain

摘要 自然导航是一项重要的技能，涉及许多感觉、运动和认知系统。因为 衰老和退行性脑部疾病都会降低在现实世界中导航的能力，这是一种更好的 了解大脑调节导航的机制将有助于提高诊断和监测 神经和神经退行性疾病。在动物身上进行的神经生理学研究导致了基本的 对调节导航的神经机制的见解。然而，由于方法上的限制， 人类导航的神经成像研究通常没有动物研究那么令人信服。我们 建议使用加州大学伯克利分校最近安装的NexGen 7T MRI扫描仪来克服这些限制 来测量自然主义驾驶任务中的大脑活动。驾驶是功能核磁共振研究的极佳靶点。 因为这是一项常见的人类导航任务，它跨越了一大片不同的地形，并在 时间尺度与功能磁共振成像相称；它涉及许多导航大脑系统；它受到年龄的影响 以及神经系统疾病。数据将通过创新和强大的体素建模进行分析 在过去的10年里，PiGalant的实验室开发了一个框架，并在许多出版物中进行了验证。 反映33种不同类型导航特征的计算模型将适用于功能磁共振成像数据 分别针对每个体素和在每个单独的对象中。模型的预测精度和泛化能力将 使用单独的数据集和为此保留的受试者进行交叉验证。结果将被用来测试 从这两个方面的理论和实验文献中得出了几十个关于导航的具体假设 啮齿动物和人类。这些结果也将被用来获得详细的导航功能分区 在每个人和整个组中表示，并识别代表 与导航相关的特定功能。通过结合自然主义实验，大规模的计算 建模、多假设检验、数据驱动的功能划分和功能网络分析，这 研究将提供有关人脑调节导航机制的基本新信息 以及它们与先前动物文献中的发现之间的关系。

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.