Sridevi V. Sarma, PhD
Appointments: Institute for Computational Medicine
Office: Hackerman 315
Lab: Neuromedical Control Systems Lab
PhD. MIT, Electrical Engineering and Computer Science (EECS), Minor in Brain and Cognitive Sciences
SM, MIT, Electrical Engineering and Computer Science (EECS)
BS, Cornell University, Electrical Engineering
Sridevi Sarma, associate professor in the Department of Biomedical Engineering and vice dean for graduate education at the Whiting School of Engineering, develops computational, data-driven, and biological approaches to advance the knowledge and treatment of diseases of the nervous system including epilepsy, chronic pain, Parkinson’s disease, and insomnia. She also harnesses dynamical systems and control theory to understand how the brain governs complex behaviors, including motor control and decision making.
Sarma, who is the associate director of Hopkins’ Institute of Computational Medicine (ICM), founded and directs the Neuromedical Control Systems Lab (NCSL). The mission of NCSL is to develop knowledge and computational tools that can be translated into the clinic. As an example, her groundbreaking research and tools are helping to shape treatment of epilepsy. Drug treatment does not work for 30 percent of the world’s 60-million-plus epilepsy sufferers. Hope for these patients lies in removal of the brain’s epileptogenic zone (EZ), where seizures originate. Currently, EZ localization is done manually through the daunting visual inspection of hundreds of intracranial EEG recordings. Within six-months post-surgery, there is an average of 50 percent chance of seizure recurrence, most likely caused by misidentification and/or nonresection of the entire EZ.
To address this, Sarma created EZTrack, a novel computational tool that quickly and accurately identifies the EZ from hundreds of EEG recordings. EZTrack uses the concept of nodal fragility in dynamical networked systems, where the most fragile nodes are more likely to cause seizures. Now in the testing phase, the patented technology will assist clinicians in determining EZ locations, increase surgical success, enable more resections, and reduce prolonged presurgical hospitalizations. Large retrospective studies conducted at five epilepsy centers across the nation have demonstrated that EZTrack not only can predict surgical outcomes with 25 percent more accuracy than can clinicians, but also can predict all failures with 100 percent accuracy. Sarma’s company, Neurologic Solutions, Inc., is commercializing the technology.
Sarma’s ddditional research in pain control includes constructing computational models of the dorsal horn (DH) circuit in the spinal cord to predict how different electrical stimulation treatments alter neuronal activity in the DH. Sarma’s team also created the first-ever computational model of the motor network under Parkinson’s Disease (PD) conditions to study the effects of deep brain stimulation (DBS). She found that high frequency DBS actually restores pathological network dynamics in the brain, while many thought that it was blocking abnormal activity.
Sarma’s research has garnered numerous awards, including a GE Faculty for the Future Research Fellowship and a National Science Foundation Research Fellowship. In 2012, she received Presidential Early Career Award for Scientists and Engineers (PECASE), and in 2014, she received the Krishna Kumar New Investigator Award from the North American Neuromodulation Society (NANS). In 2015, she was awarded the Whiting School of Engineering Robert B. Pond Excellence in Teaching Award.
Sarma is a member of the Institute of Electrical and Electronics Engineers (IEEE), Association for Women in Science (AWIS), the Biomedical Engineering Society (BMES), the American Society of Mechanical Engineers (ASME), and the Society for Neuroscience. She serves on the International Workshop Statistical Analysis of Neuronal Data (SAND7) Committee, has served as associate editor for IEEE Transactions on Neural Systems and Rehabilitation and was editor of a 2017 special issue of the Journal of Computational Neuroscience.
In addition to her publications, book chapters, and patents, Sarma’s leadership in the biomedical engineering field include invited presentations at numerous conferences, among them: Neuromodulation: The Science (2019), the plenary lecture at the 2019 International Federation of Automatic Control Workshop on Distributed Estimation and Control in Networked Systems; the 2019 Workshop on Learning Theory at Mumbai’s Tata Institute of Fundamental Research; panelist at a 2018 American Epilepsy Society Workshop; and at several Computational and Systems Neuroscience (COSYNE) workshops. Sarma has also appeared as a domain expert on several episodes of the Emmy-award-winning TV series “Brain Games” on the National Geographic network.
She received her BS in Electrical Engineering (1994) from Cornell University and her SM (1997) and PhD (2006) in Electrical Engineering and Computer Science from the Massachussets Institute of Technology (MIT). She was a postdoctoral fellow in Statistical Neural Data Analysis in the Neuroscience Statistics Research Laboratory at Massachusetts General Hospital and Harvard Medical School and at MIT’s Computational Neuroscience Laboratory in the Brain and Cognitive Science Department.
Burns SP, Santaniello S, Yaffe RB, Jouny C, Crone N, Bergey G, Anderson WS, Sarma SV. (2014) Network Dynamics of the Brain and Influence of the Epileptic Seizure Onset Zone. Proc Natl Acad Sci U S A. Dec 9;111(49):E5321–30
Santaniello S, McCarthy MM, Montgomery Jr. EB, Kopell N, Gale JT, and Sarma SV. (2014) The Therapeutic mechanism of high frequency DBS in Parkinson’s disease: neural restoration through loop-based reinforcement. Proc Natl Acad Sci U S A. 2015 Feb 10;112(6):E586–95.
Agarwal R, Thakor N, Sarma SV*, Massaquoi S. (2015) PMv Neuronal Firing May Be Driven by a Movement Command Trajectory within Multidimensional Gaussian Fields. The Journal of Neuroscience, 24 June 2015, 35(25):9508–9525.
Ehrens D, Sritharan D, Sarma SV. (2015) Closed-Loop Control of a Fragile Network: Application to Seizure-like Dynamics of an Epilepsy Model. Front. Neurosci., 03 March 2015.