Presented By: Biomedical Engineering
Alan J. Hunt Memorial Lecture-U-M Biomedical Engineering
Sridevi Sarma, Ph.D.
2025 Alan J. Hunt Memorial Lecture
Measuring the Health of the Brain: From Global Networks to Local Biomarkers
Abstract:
How do we measure the health of the brain? Unlike blood pressure or cholesterol, there is no single number that captures brain function. We are developing the Brain Entropy Index (BEI), a new measure that integrates thermodynamics, statistical modeling, and brain imaging to provide a global indicator of brain health. Using data from modalities such as EEG, fMRI, and MEG, the BEI can reliably separate healthy from diseased brains, offering promise as a universal screening tool.
Yet, just as a blood test might flag “illness” without specifying the disease, a global index cannot by itself identify the underlying condition. Neurological disorders are defined by distinct local network dynamics, and it is these dynamics that can give rise to robust biomarkers. One example is EpiScalp, a computational tool we developed to model local brain networks from scalp EEG. EpiScalp can differentiate true epilepsy from seizure-mimicking disorders and from healthy brains, providing a disorder-specific biomarker.
Together, these approaches demonstrate a two-tiered framework: global measures can screen for health versus disease, while local network dynamics serve as biomarkers that refine diagnosis and define specific conditions. More broadly, this talk will illustrate how concepts from systems & control theory, physics, and data science can converge to open new frontiers in neuroscience and medicine.
Bio:
Sridevi Sarma received a B.S. in Electrical Engineering from Cornell University, and an M.S. and Ph.D. in Electrical Engineering and Computer Science from Massachusetts Institute of Technology (MIT). She was a Postdoctoral Fellow in the Brain and Cognitive Sciences Department at the MIT. Dr. Sarma is now a Professor in the Institute for Computational Medicine, Department of Biomedical Engineering, at Johns Hopkins University. Her research includes modeling, estimation and control of neural systems using electrical stimulation to better diagnose and treat neurological disorders. She is PI for NeuroTech Harbor, an NIH-funded BluePrint Hub for NeuroTechnologies and recently won an NIH Research Investigator Award (R35) that supports her translational research in epilepsy for 8 years. She is a recipient of the the Burroughs Wellcome Fund Careers at the Scientific Interface Award, the Krishna Kumar New Investigator Award from the North American Neuromodulation Society, a recipient of the Presidential Early Career Award for Scientists and Engineers and the Whiting School of Engineering Robert B. Pond Excellence in Teaching Award.
Measuring the Health of the Brain: From Global Networks to Local Biomarkers
Abstract:
How do we measure the health of the brain? Unlike blood pressure or cholesterol, there is no single number that captures brain function. We are developing the Brain Entropy Index (BEI), a new measure that integrates thermodynamics, statistical modeling, and brain imaging to provide a global indicator of brain health. Using data from modalities such as EEG, fMRI, and MEG, the BEI can reliably separate healthy from diseased brains, offering promise as a universal screening tool.
Yet, just as a blood test might flag “illness” without specifying the disease, a global index cannot by itself identify the underlying condition. Neurological disorders are defined by distinct local network dynamics, and it is these dynamics that can give rise to robust biomarkers. One example is EpiScalp, a computational tool we developed to model local brain networks from scalp EEG. EpiScalp can differentiate true epilepsy from seizure-mimicking disorders and from healthy brains, providing a disorder-specific biomarker.
Together, these approaches demonstrate a two-tiered framework: global measures can screen for health versus disease, while local network dynamics serve as biomarkers that refine diagnosis and define specific conditions. More broadly, this talk will illustrate how concepts from systems & control theory, physics, and data science can converge to open new frontiers in neuroscience and medicine.
Bio:
Sridevi Sarma received a B.S. in Electrical Engineering from Cornell University, and an M.S. and Ph.D. in Electrical Engineering and Computer Science from Massachusetts Institute of Technology (MIT). She was a Postdoctoral Fellow in the Brain and Cognitive Sciences Department at the MIT. Dr. Sarma is now a Professor in the Institute for Computational Medicine, Department of Biomedical Engineering, at Johns Hopkins University. Her research includes modeling, estimation and control of neural systems using electrical stimulation to better diagnose and treat neurological disorders. She is PI for NeuroTech Harbor, an NIH-funded BluePrint Hub for NeuroTechnologies and recently won an NIH Research Investigator Award (R35) that supports her translational research in epilepsy for 8 years. She is a recipient of the the Burroughs Wellcome Fund Careers at the Scientific Interface Award, the Krishna Kumar New Investigator Award from the North American Neuromodulation Society, a recipient of the Presidential Early Career Award for Scientists and Engineers and the Whiting School of Engineering Robert B. Pond Excellence in Teaching Award.
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