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Parnian Hemmati

As a Ph.D. candidate in Mechanical Engineering, my research focuses on the vital role of cerebrospinal fluid (CSF) and glymphatic flow in maintaining brain health and function. CSF acts as a protective cushion for the brain, absorbing shocks and reducing the risk of injury, while also playing a crucial role in waste removal by transporting metabolic byproducts and toxins away from the brain. The glymphatic system enhances this waste removal by facilitating the clearance of harmful proteins, such as beta-amyloid, which are linked to neurodegenerative diseases like Alzheimer’s. My research focuses on exploring the mechanisms of fluid, particle, and ion transport within the glymphatic system, as well as investigating the effect of external factors, such as head impact, on the CSF flow. By integrating cutting-edge engineering approaches with bioscience and clinical research, I aim to advance our understanding of brain health and develop innovative solutions. Additionally, I incorporate data science techniques to combine numerical modeling and experimental data, enhancing the accuracy and applicability of the findings for translational research in neurological disorders.

Mentor: Mayumi Prins, Ph.D.

Lei Ma

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.

Fleming Peck

My research combines insights from neuroscience, psychology, and computer science to understand human learning and memory. I am interested in how the brain supports working memory and context-dependent statistical learning where temporal regularities are consistent within an environment but interfere between environments. I use machine learning algorithms to relate brain activity to behavioral measures, and I model behavioral results with neural networks.

Mentor: Jesse Rissman, Ph.D.

Alice Hsu

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.

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