Mentor: Daniel Lu, M.D., Ph.D.
My long-term research interests involve understanding behavioral and cognitive deficits in rodent models of Parkinson’s disease. My goal is to unravel the neuronal circuits that correlate these behaviors and use that knowledge to develop treatments for this debilitating disease. The behavioral deficits associated with Parkinson’s disease is widely studied so I would like to shed some light on the lesser known, but still just as harmful, cognitive deficits that seems to be an understudied part of the disease. I hope that my work will be relevant to the growing body of knowledge in cognitive deficits in Parkinson’s disease and will lay novel and impactful groundwork for current and future investigators.
Mentor: William Zeiger, M.D., Ph.D.
I study how the human brain controls gait and balance while navigating the world around us. Using immersive virtual reality to simulate real-world scenarios, I examine how intracranial and scalp electrophysiology signals can predict the motor behaviors that keep the body upright and moving. I aim to leverage these neural markers to guide rehabilitation strategies for improving gait function in neurological populations at risk for falls.
Mentor: Katy Cross, M.D., Ph.D.
I am interested in using neuromodulating technologies, such as transcranial magnetic stimulation (TMS), to treat traumatic brain injuries. We are testing whether TMS can reset frontoamygdala circuitry to extinguish fear avoidance behavior, autonomic reactivity, and sleep disturbances that prolong symptoms after concussion. Using machine learning, in the largest study of its kind with the most continuous data, I will develop an algorithm that uses at-home measures of autonomic function (heart rate variability (HRV), heart rate, respiratory rate, oxygenation, and sleep/rest temperature) from the Oura Ring to predict in-lab autonomics (HRV and pupillary dynamics before, during, and after TMS and an exposure task as well as central autonomic activity in fMRI). I will develop a software that transforms consumer-based wearable data into biomarkers to predict concussion recovery and guide treatment for patients with prolonged symptoms.
Mentor: Kevin Bickart, M.D., Ph.D.