The Wu Tsai Neurosciences Institute and Stanford Data Science are pleased to announce a special seminar series focused on the intersection of data and brain science.
Monitoring and modeling the autonomic nervous system: the bridge between the brain and the body
In both acute and chronic nervous system responses, the central nervous system coordinates across many systems of the body. This role is carried out by the autonomic nervous system (ANS), the body’s hidden control system that is responsible for our unconscious functioning. Understanding ANS control and interaction with the body provides a unique window into how changes in the CNS manifest throughout the body. However, as a complex and distributed network of nerves deep within the body, the ANS is a poorly understood part of basic physiology, including cognitive physiology. In this talk, I will provide examples in both acute and chronic nervous system contexts of how new data science methods guided by physiological reasoning and principles can be developed to make careful measurements of the ANS from other parts of the body. Throughout the talk, I will describe how context-guided data science methods leveraging physiological constraints can improve learning and inference. I will also showcase how these approaches can link such inferences back to mechanisms at the level of the brainstem. First, I will demonstrate a multimodal approach to track unconscious pain in humans during surgery. In the process of describing the methodology, I will give an example of the first physiology-based statistical model for sweat gland activity that enables dynamic estimation of ‘fight-or-flight’ activity that underlies the body’s pain response. I will also illustrate a chronic disease monitoring example involving continuous, at-home monitoring in people with aberrant neuromuscular function of the digestive system. Together, I will provide a vision of how physiology-guided dynamic modeling and characterization of the ANS will enable both fundamental neuroscientific advances in our understanding of how the brain controls and receives feedback from other bodily systems as well as the engineering of new translational solutions for acute and chronic disease.
Dr. Subramanian received B.S. Degrees in Biomedical Engineering and Applied Mathematics & Statistics in 2015 from Johns Hopkins University. She received an M.Phil. in 2016 from the University of Cambridge in Clinical Neurosciences. She received her Ph.D. in 2021 from the Harvard-MIT Division of Health Sciences and Technology under the mentorship of Professor Emery Brown. Her thesis work involved building interpretable physiology-based statistical models for autonomic nervous system responses to track unconscious pain processing during surgery. Now, as a postdoctoral fellow at Stanford with Professors Sean Mackey, Department of Pain Medicine, and Todd Coleman, Department of Bioengineering, she is developing systems and inference methods for continuous, at-home physiological monitoring to uncover autonomic underpinnings of complex chronic diseases such as migraine and functional digestive disorders. She is a Schmidt Science Fellow and Stanford Data Science Postdoctoral Fellow.