The brain is the most complex system on earth, and we are still far from understanding the intricacies which allow us to move, think, and feel. My work in my Ph.D. research at Carnegie Mellon University is centered on understanding how cognitive variables like motivation and attention are encoded in different areas of the brain.

Project 1: Identifying across-brain interactions using machine learning

Check out our recent preprint (link) and our code pack published on GitHub (link).

Brain functions involve processing in local networks as well as modulation from brainwide signals, such as arousal. Dissecting the contributions of populations of neurons to these functions requires knowledge of interactions between brain areas. We investigated these interactions using dual hemisphere recordings of prefrontal cortex in monkeys performing a spatial memory task. To tease apart global processing from local interactions, we applied a novel statistical approach called pCCA-FA (a combination of probabilistic canonical correlation analysis and factor analysis) to analyze trial-to-trial variability in neuronal responses. We found substantial shared variability among neurons within each population, much of which was actually shared across populations and linked to an arousal process. Our work presents a path by which we can leverage multi-area recordings to reveal aspects of brain functions that are hidden in single-area recordings.

Project 2: Uncovering correlates of motivation across the brain

My second project in my thesis work is centered around understanding how our motivation to complete a task is encoded across various brain regions. Reward and motivation impact everything we do, but how does our brain integrate these signals? In my work I’ve recorded from various brain regions across the cortex and am searching for the cognitive encoding of motivation.