Anusha Keshireddy, SEAS ’23. Chesterfield, NJ
This summer I had the opportunity to continue my research at the Richardson Lab in the Department of Neurosurgery at the Perelman School of Medicine. I first joined the Richardson Lab in January of this year, but this summer, I was able to work full-time.
The Richardson Lab works in the field of brain machine interfaces (BMI), which offer an approach to restore lost motor and sensory function in individuals who have lost their natural pathways due to disease or injury, such as spinal cord injury or stroke. The project that I am working on has a long-term goal of restoring sensation.
Electrically stimulating the brain through intracortical microstimulation (ICMS) may be used to encode and restore naturalistic sensation. Currently, sensory percepts elicited by ICMS are mapped intraoperatively by asking patients what they feel in response to different stimulation patterns. Relying on the subject’s perception to determine discriminability between stimulation parameters is time consuming and resource-limited, rarely yielding naturalistic results. Furthermore, it doesn’t allow for the opportunity to explore the full stimulus parameter space. If stimulation evokes discriminable percepts, patients can learn to associate types of stimulation with different sensations. Thus, finding stimulation patterns that elicit clearly and demonstrably discriminable sensory percepts is important, but currently a cumbersome process.
We are working to implement a closed-loop system that automates the selection of stimulus parameters. Studies show that discriminable natural percepts elicit significantly differentiable responses in the primary motor cortex and sensory cortex. Instead of depending solely on patients’ verbal feedback, the neural response to stimulation in a downstream region may be used to see which stimulation patterns lead to discriminable responses.
In our lab, we are working with an animal model. In rats, we electrically stimulate the sensory cortex, and record the stimulus evoked neural activity from the downstream motor cortex. Using this recorded data, we worked on implementing a closed-loop system that selects stimulus parameters in real time using an algorithm that clusters data to maximize the discriminability of the responses.
My work focused on the processing and interpretation of the recorded data. I developed a pipeline to analyze the neural data, determine which stimuli resulted in a response, and quantify that response, which ultimately serves as the input to the clustering algorithm. In addition, I worked on visualizing the neural data and generating useful figures in real-time.
Over the past three months, I’ve gained various skills and expanded my knowledge base in neural engineering, a field I am passionate about. I’ve improved my coding skills in both Python and MATLAB. I’ve gained skills in signal processing, data visualization, data science, optimization, machine learning, and handling very large data sets. My mentors have been very encouraging and wonderful teachers, and I’ve enjoyed getting to know them and my other lab members better. Overall, I have a much better understanding of what the research process entails and the challenges that can occur, and how a team can work together to troubleshoot these issues. After this enriching summer, I’m excited to continue working in the Richardson Lab!
This is part of a series of posts by recipients of the 2022 Career Services Summer Funding Grant. We’ve asked funding recipients to reflect on their summer experiences and talk about the industries in which they spent their summer. You can read the entire series here.
