Sendi Receives J. Norman and Rosalyn Wells Fellowship Award

<p>Mohammad Sendi</p>

Mohammad Sendi

Mohammad Sendi received the J. Norman and Rosalyn Wells Fellowship Award, which is presented by the Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University. This fellowship is a one-time stipend bonus for doctoral students who are conducting outstanding research in the areas of neuroengineering or brain tumors. 

The title of Sendi’s award-winning project proposal is “An Active Learning Framework for Quantifying the Effect of Neuromodulation.” He and his colleagues propose a novel framework for characterizing the neurophysiological effects of deep brain stimulation (DBS) on neuronal dynamics and the optimal design of closed-loop DBS algorithms. DBS approaches have the potential to revolutionize the understanding of information processing in the brain by selectively regulating specific features of neural activities and testing the causal link between manipulating those features and the behavioral or clinical outcomes. 

The next generation of DBS therapy, called closed-loop DBS, can offer substantial benefit for adaptive and patient-specific treatment of neuropsychiatric disorders and restore function after disease or injuries. Quantifying the functional and the neurophysiological effects of DBS is an important step towards understanding the neural mechanisms of DBS and developing closed-loop DBS therapies. 

In this project, toward making closed-loop DBS, two fundamental questions will be addressed: 1) what are the underlying neural changes induced by DBS therapies; and 2) how those neural features change in response to different DBS parameters, such as amplitude, frequency, and pulse width of DBS. Sendi’s project aims to develop a framework that leverages interpretable machine learning techniques for characterizing the neurophysiological effects of DBS and active learning techniques for the optimal design of closed-loop DBS control systems. Sendi and his team would then implement and validate the proposed framework in a translational experimental setup.

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