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Seizure mapping tools developed by NCSL researchers featured in JHU Hub

Two new models could solve a problem that’s long frustrated millions of people with epilepsy and the doctors who treat them: how to find precisely where seizures originate in the brain.

By helping surgeons decide if and where to operate, the tools developed by researchers in Sri Sarma’s Neuromedical Control Systems Lab could help patients avoid risky and often-ineffective surgeries as well as prolonged hospital stays.

See the full article here: New tools map seizures in the brain, improve epilepsy treatment

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Engineers drive new directions in translational epilepsy research

There is a seismic shift underway in epilepsy research and patient care, and it’s coming from engineering. Two studies published by researchers in Sri Sarma’s Neuromedical Control Systems Lab were mentioned in a recent editorial on engineers in epilepsy in the journal Brain. Read the full commentary in the link below:

Engineers drive new directions in translational epilepsy research.

Categories Media News

New Tools Map Seizures, Improve Epilepsy Treatment

Two new models could solve a problem that’s long frustrated millions of people with epilepsy and the doctors who treat them: how to find precisely where seizures originate to treat exactly that part of the brain.

By helping surgeons decide if and where to operate, the tools developed by Johns Hopkins University researchers and newly detailed in the journal Brain, could help patients avoid risky and often-ineffective surgeries as well as prolonged hospital stays.

“These are underserved patients,” said Sridevi V. Sarma, associate director of Johns Hopkins Institute of Computational Medicine and head of the Neuromedical Control Systems Lab. “We want surgeries to go well, but we also want to prevent surgeries that may never go well.”

Read more here

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Sri Sarma awarded 2021 Convocation Award for Excellence in Mentoring

Sri Sarma, associate director of the Institute for Computational Medicine, is the recipient of the Capers and Marion McDonald Award for Excellence in Mentoring and Advising. This award, created by Capers and Marion McDonald, is given to honor teachers, researchers, and administrators who have consistently supported the personal and professional development of their students. Sarma will be recognized for her achievement at the 2021 Whiting School of Engineering Convocation Ceremony on Thursday, May 27.

In addition to her role at the ICM, Sarma is an associate professor of biomedical engineering and vice dean for graduate education. She is the principal investigator of Neuromedical Control Systems Lab where she mentors undergraduates, graduate students, and postdoctoral fellows in their research and professional development. Her research interests include modeling, estimation and control of neural systems using electrical stimulation.

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Opening for research associate or undergraduate

Research description:

Stereotactic EEG (sEEG) is an electrophysiological recording modality that obtains high temporal resolution of brain tissue’s electrical activity. In addition, it can sample deep structures of the brain. SEEG-Kit (SEEK) is a set of software tools that aims to merge data modalities (T1 MRI, CT, sEEG) and sEEG time-series analysis to robustly visualize data in 3-D and 4-D. SEEK is currently used in its alpha stage at Johns Hopkins Hospital for epilepsy monitoring. We are currently looking for a research assistant to join our research group in helping further develop SEEK. If you are interested in helping build out a next-generation data-analysis and visualization tool, please get in touch with us.

What you will gain:

  • Experience contributing robust software to a research tool that is used by neuroscience and clinical teams
  • Opportunities to be included on publications that utilize software tools built by you

Research associate qualifications:

We are looking for eager and driven engineers to help build out SEEK and its different components, specifically in visualization. We are looking for undergraduates who are ready to learn and contribute to an open-source set of packages. The minimum set of requirements are:

  • Entry-level software developer skills
  • Eager to learn
  • Familiarity with data structures

Preferred qualification include:

  • Comfortable with the following technologies:
    o Git: Version control
    o NodeJS: Backend server
    o threejs: Data visualizations for the web
  • Comfortable contributing to open-source
  • Familiar with unit-testing and continuous integration testing
  • Familiarity with neuroimaging
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Rachel June Smith wins 2020 Young Investigator Award

Rachel June Smith, a postdoctoral fellow at the Institute for Computational Medicine, has been awarded a 2020 Young Investigator Award by the American Epilepsy Society for her abstract titled, “Dynamical Systems Theory Applied to Single-Pulse Electrical Stimulation Data to Infer Epileptogenic Networks.” Rachel was one of 20 young investigators selected from 1,100 submissions for outstanding achievement in basic, translational, or clinical epilepsy research.

Rachel’s research brings together computational modeling, systems theory, and epilepsy medicine. She constructs dynamic models of epileptic networks for the purposes of understanding where seizures start and spread in the brain. Her model predictions can assist neurologists in treating epilepsy patients who undergo surgery to remove epileptogenic brain tissue, thereby increasing the patient’s chances of seizure freedom after treatment.

Rachel conducts her research in the Neuromedical Control Systems Lab led by Sri Sarma, who is the Institute for Computational Medicine’s associate director and The Whiting School of Engineering’s vice dean of graduate education.

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Adam Li named ARCS scholar

Adam Li is a Ph.D. candidate in Biomedical Engineering at Johns Hopkins University with Dr. Sridevi Sarma. He obtained his undergraduate degree at University of California, San Diego (B.S’s in Bioengineering and Applied Mathematics). His research is in computational approaches to seizure onset zone localization in human epilepsy patients using intracranial EEG data applying control theory, computational modeling and machine learning.