By Maryam Shanechi (University of Southern California)
Talk Abstract: A major challenge in engineering and neuroscience is to model, decode, and control the complex activity of large populations of neurons that underlie our brain’s functions and dysfunctions. I will present our work toward addressing this challenge, which can help restore lost motor and emotional function in millions of patients with disabling neurological and neuropsychiatric disorders such as major depression. I first discuss a multiscale dynamical modeling framework that can decode mood variations from multisite human brain activity and identify brain regions that are most predictive of mood. I then develop a system identification approach that can predict multiregional brain network dynamics (output) in response to time-varying electrical stimulation (input) to enable closed-loop control of neural activity. Further, I extend our modeling framework to dissociate behaviorally relevant neural dynamics that can otherwise be missed, such as those during naturalistic movements. I also present how these models can incorporate multiple spatiotemporal scales of brain activity simultaneously. Finally, I will develop neural network architectures and learning algorithms that can capture and localize nonlinearities in behaviorally relevant neural dynamics. These dynamical models, decoders, and controllers can enable a new generation of brain-machine interfaces for personalized therapy in brain disorders.
Speaker Bio: Maryam M. Shanechi is Associate Professor and Viterbi Early Career Chair in Electrical and Computer Engineering (ECE) and a member of the Neuroscience Graduate Program and Departments of Computer Science and Biomedical Engineering at the University of Southern California (USC). Prior to joining USC, she was Assistant Professor at Cornell University’s ECE department in 2014. She received her B.A.Sc. degree in Engineering Science from the University of Toronto, her S.M. and Ph.D. degrees in Electrical Engineering and Computer Science from MIT, and her postdoctoral training in Neural Engineering at Harvard Medical School and UC Berkeley. Her research focuses on developing closed-loop neurotechnology and studying the brain through decoding and control of neural dynamics. She is the recipient of several awards including the NIH Director’s New Innovator Award, NSF CAREER Award, ONR Young Investigator Award, ASEE’s Curtis W. McGraw Research Award, MIT Technology Review’s top 35 Innovators Under 35, Popular Science Brilliant 10, Science News SN10, One Mind Rising Star Award, and a DoD Multidisciplinary University Research Initiative (MURI) Award.