Modeling functional connectivity allows researchers to compare brain activation to behavioral outcomes. Image: Chu, Parhi, & Lenglet, Nature, 2018.

For undergraduates, sleep can be as elusive as it is important. For undergraduate researcher Katie Freedy, Trinity ’20, understanding sleep is even more important because she works in Ahmad Hariri’s Lab of Neurogenetics.

After taking a psychopharmacology class while studying abroad in Copenhagen, Freedy became interested in the default mode network, a brain network implicated in autobiographical thought, self-representation and depression. Upon returning to her lab at Duke, Freedy wanted to explore the interaction between brain regions like the default mode network with sleep and depression.

Freedy’s project uses data from the Duke Neurogenetics Study, a study that collected data on brain scans, anxiety, depression, and sleep in 1,300 Duke undergraduates. While previous research has found connections between brain connectivity, sleep, and depression, Freedy was interested in a novel approach.

Connectome predictive modeling (CPM) is a statistical technique that uses fMRI data to create models for connections within the brain. In the case of Freedy’s project, the model takes in data on resting state and task-based scans to model intrinsic functional connectivity. Functional connectivity is mapped as a relationship between the activation of two different parts of the brain during a specific task. By looking at both resting state and task-based scans, Freedy’s models can create a broader picture of connectivity.

To build the best model, a procedure is repeated for each subject where a single subject’s data is left out of the model. Once the model is constructed, its validity is tested by taking the brain scan data of the left-out subject and assessing how well the model predicts that subject’s other data. Repeating this for every subject trains the model to make the most generally applicable but accurate predictions of behavioral data based on brain connectivity.

Freedy presented the preliminary results from her model this past summer at the BioCORE Symposium as a Summer Neuroscience Program fellow. The preliminary results showed that patterns of brain connectivity were able to predict overall sleep quality. With additional analyses, Freedy is eager to explore which specific patterns of connectivity can predict sleep quality, and how this is mediated by depression.

Freedy presented the preliminary results of her project at Duke’s BioCORE Symposium.

Understanding the links between brain connectivity, sleep, and depression is of specific importance to the often sleep-deprived undergraduates.

“Using data from Duke students makes it directly related to our lives and important to those around me,” Freedy says. “With the field of neuroscience, there is so much we still don’t know, so any effort in neuroscience to directly tease out what is happening is important.”

Post by undergraduate blogger Sarah Haurin
Post by undergraduate blogger Sarah Haurin