PROJECT SUMMARY/ABSTRACT
Learning and remembering the locations of resources while avoiding dangerous locations is a major challenge
for complex organisms. Although the neural representations of known environments have been well studied,
comparatively little is known about how that spatial knowledge is acquired in the first place. Here, we address
the important problem of how people learn and remember new environments. In particular, we aim to
investigate a fundamental type of spatial knowledge, the path connections between locations (‘graph
knowledge’). A topological graph consists of place nodes linked by path edges which could generate routes,
but without exact metric distances and angles, like a subway map. When it comes to learning spatial
knowledge, it seems intuitive that active navigation should facilitate, however, we do not yet understand the
mechanisms behind this advantage. Our overarching hypothesis is that interactions of a prefrontal-
hippocampal-striatal (PHS) circuit support graph learning, particularly during active decision making about
exploration. Combined with decision making and reinforcement learning mechanisms, the PHS pathway is
hypothesized to facilitate memory during learning. Based on this model, interactions and functional
communication within the PHS circuit are critical to new learning. The goals of this fundamental basic research
proposal are to 1) determine the trajectory of navigational learning, including both behavioral and brain network
dynamics, 2) identify the underlying brain mechanisms behind active decision making during graph learning,
and 3) answer fundamental questions about the relationship between decision making and memory. In
Specific Aim 1, we will determine exploration behaviors that facilitate graph learning. We will compare a
variety of graph structures, environmental openness, and scale to determine the robustness of graph learning.
In Specific Aim 2, we will use novel fMRI methods to examine changes in the formation of cohesive groups of
brain areas (‘communities’), harnessing the dynamics of learning. We will use this technique to identify brain
networks supporting active compared to passive learning. In Specific Aim 3, we will compare the brain
networks found in graph learning with those in non-spatial and non-Euclidean graphs. These studies will test
for brain networks common across different types of graphs, as well as those unique to spatial graphs. The
outcomes will provide insights into fundamental processes of navigation, learning, and memory, and will help
answer questions about learning beyond the realm of navigation. The PHS circuit is relevant to mental
disorders involving reinforcement and reward learning, including OCD, depression, and Parkinson’s Disease.
These studies will establish a vital link between spatial navigation and the PHS circuit, and will form the basis
for computational approaches to navigation, learning, memory, and breakdowns of the PHS circuit. The far-
reaching impact of this research includes assessing the function and dysfunction of this circuit in clinical
populations to better understand disease mechanisms.
