Sridevi Sarma, Johns Hopkins University, “Network Dynamics of the Brain and Influence of the Epileptic Seizure Onset Zone”
Johns Hopkins University Eastern Campus
1101 E 33rd St, Baltimore, MD 21218
USA
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Bio
“Network Dynamics of the Brain and Influence of the Epileptic Seizure Onset Zone”
Sridevi Sarma received her B.S. degree in electrical engineering from Cornell University, Ithaca NY, in 1994; and an M.S. and Ph.D. degrees in Electrical Engineering and Computer Science from Massachusetts Institute of Technology in, Cambridge MA, in 1997 and 2006, respectively. From 2000-2003 she took a leave of absence to start a data analytics company. From 2006–2009, she was a Postdoctoral Fellow in the Brain and Cognitive Sciences Department at the Massachusetts Institute of Technology, Cambridge. She is now an assistant professor in the Institute for Computational Medicine, Department of Biomedical Engineering, at Johns Hopkins University, Baltimore MD. Her research interests include modeling, estimation and control of neural systems using electrical stimulation. She is a recipient of the GE faculty for the future scholarship, a National Science Foundation graduate research fellow, a L’Oreal For Women in Science fellow, the Burroughs Wellcome Fund Careers at the Scientific Interface Award, the Krishna Kumar New Investigator Award from the North American Neuromodulation Society, and a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE) and the Whiting School of Engineering Robert B. Pond Excellence in Teaching Award.
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Abstract
“Network Dynamics of the Brain and Influence of the Epileptic Seizure Onset Zone”
In this talk, I will describe how we applied network-based data analytics to multiple intracranial EEG recordings sampled from epileptic cortical networks in patients with intractable focal epilepsy (IFE). Our analyses uncovered several dynamical states in the brain before, during, and after seizure events, and discovered an EEG marker of the seizure onset zone (SOZ). This marker was then used to develop a computational tool to assist clinicians in localizing the SOZ, which is required for surgical treatment. Localization of the SOZ is currently prone to errors, and surgical outcomes are disappointing and highly variable. Our tool has been tested in a retrospective study including 42 patients with IFE (including 22 SEEG patients from the Cleveland Clinic) and achieved 95% accuracy in predicting surgical outcome.
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