Research Blog

How do we represent space in the brain? Neuroscientists have been working to understand this question since the mid-20^th century, when researchers like EC Tolman started experimenting with rats in mazes. When placed in a maze with a food reward that the rats had been trained to retrieve, the rats consistently chose the shortest path to the reward, even if they hadn’t practiced that path before.

Over 50 years later, researchers like Sam Gershman, PhD, of Harvard’s Gershman Lab are still working to understand how our brains encode information about space.

Gershman’s research questions center around the concept of a cognitive map, which allows the brain to represent landmarks in space and the distance between them. He spoke at a Center for Cognitive Neuroscience colloquium at Duke on Feb. 7.

Maps are formed via reinforcement learning, which involves predicting and maximizing future reward. When an individual is faced with problems that have multiple steps, they can do this by relying on previously learned predictions about the future, a method called successor representation (SR), which would suggest that the maps we hold in our brain are predictive rather than retroactive.

One specific region implicated in representations of physical space is the hippocampus, with hippocampal place cell activity corresponding to positions in physical space. In one study, Gershman found, as rats move through space, that place field activity corresponding to physical location in space skews opposite of the direction of travel; in other words, activity reflects both where the rodent currently is and where it just was. This pattern suggests encoding of information that will be useful for future travel through the same terrain: in Gershman’s words, “As you repeatedly traverse the linear track, the locations behind you now become predictive of where you are going to be in the future.”

This idea that cognitive activity during learning reflects construction of a predictive map is further supported by studies where the rodents encounter novel barriers. After being trained to retrieve a reward from a particular location, introducing a barrier along this known path leads to increased place cell activity as they get closer to the barrier; the animal is updating its predictive map to account for the novel obstacle.

This model also explains a concept called context preexposure facilitation effect, seen when animals are introduced to a new environment and subsequently exposed to a mild electrical shock. Animals who spend more time in the new environment before receiving the shock show a stronger fear response upon subsequent exposures to the box than those that receive a shock immediately in the new environment. Gershman attributes this observation to the time it takes the animal to construct its predictive map of the new environment; if the animal is shocked before it can construct its predictive map, it may be less able to generalize the fear response to the new environment.

With this understanding of cognitive maps, Gershman presents a compelling and far-reaching model to explain how we encode information about our environments to aid us in future tasks and decision making.

As we age, our bodies change, and these changes extend into our brains and cognition. Although research has identified many changes to the brain with age, like decreases in gray matter volume or delayed recall from memory, researchers like Shivangi Jain, PhD, are interested in a deeper look at how the brain changes with age.

As a post-doctoral associate in the David Madden Lab at Duke, Jain is interested in how structural and functional connectivity in the brain change with age. Jain relies on the increasingly popular method of graph theory, which is a way of modeling the brain as a set of nodes or brain regions that are interconnected. Studying the brain in this way allows researchers to make connections between the physical layout of the brain and how these regions interact when they are active. Structural connectivity represents actual anatomical connections between regions in the brain, while functional connectivity refers to correlated activity between brain regions.

Jain’s studies use a series of tasks that test speed, executive function, and memory, each of which decline with age. Using fMRI data, Jain observed a decline in functional connectivity, where functional modules become less segregated with age.  In terms of structural connectivity, aging was associated with a decline in the strength of white matter connections and global efficiency, which represents the length between modules with shorter paths being more efficient. Thus, the aging brain shows changes at the anatomical, activational, and behavioral levels.

Jain then examined how these network-level changes played a role in the observed behavioral changes. Using statistical modeling, she found that the decline in performance in tasks for executive control could be explained by the observed changes in functional connectivity. Furthermore, Jain found that the changes in structural connectivity caused the change in functional connectivity. Taken together, these results indicate that the physical connections between areas in the brain deteriorate with age, which in turn causes a decrease in functional connectedness and a decline in cognitive ability.

Research like Jain’s can help explain the complicated relationships between brain structure and function, and how these relationships affect behavioral output.