UC Berkeley

Navigation in the natural world is a complex task that engages many cognitive systems, including vision, attention, motor control, cognitive maps, and planning. These systems recruit many brain regions that form multiple functional networks spanning the brain. Navigation has been the subject of many non-human neurophysiology studies, primarily in rodents, and less frequently in non-human primates.In the recent decades, neuroimaging using functional magnetic resonance imaging has enabled non-invasive brain activity recordings. Neuroimaging studies are able to examine cognitive processes in healthy human subjects. Together, neurophysiology and neuroimaging studies have revealed multiple regions in the human brain that are active during navigation. However, we still know very little about how these regions accomplish the complex task of navigation, and what information is represented by each region. This dissertation describes a next-generation neuroimaging experiment that maps navigational representations across the cortex to gain insight into the complex processes underlying human navigation. Chapter 1 reviews the current neurophysiology and neuroimaging literature on the navigation system of the brain. These current literatures suggest that the brain breaks navigation down into a set of subtasks, each with its associated brain regions. This chapter also describes several limitations in the current understanding of navigation in the human brain. Chapter 2 describes a next-generation, naturalistic neuroimaging paradigm to study human navigation. This paradigm leverages Unreal Engine 4, a modern game engine, to create a realistic and dynamic virtual world. A custom MR-compatible steering wheel and pedal set enables subjects to drive naturally in this virtual world. Chapter 3 describes an active navigation neuroimaging experiment that uses the paradigm developed in Chapter 2. Subjects actively perform a taxi driver task in a virtual world while BOLD activity is recorded from the brain. Voxelwise modelling with banded ridge regression is used to map the cortical representation of over 20,000 features across 33 feature spaces. Chapter 4 examines task-related visual semantic tuning shifts. Visual semantic tunings in the active navigation task are compared with those from a passive movie watching task. Results show that there are significant visual semantic tuning shifts between active navigation and passive movie watching. Chapter 5 describes a route progression model derived from the rodent literature. Results suggest that a topologic representation of route progression in RSC is conserved across species. Furthermore, RSC, unlike other visual navigation areas, strongly prefers the start of routes. Finally, Chapter 6 describes a novel, state-space based method for analyzing high-dimensional brain data. This method treats the activity of the brain as a dynamical system, and finds a low-dimensional subspace in the brain’s activity space that is related to the representation of task variables. As a proof-of-concept, this method is applied to a visual semantic attention task and a video game task, and recovers low-dimensional spaces related to the representation of attentional targets and game states.