Neuro-X Seminar: Dynamical modeling, decoding, and control of multiscale brain network activity: from motor to mood

Abstract:                                                            
A major challenge in engineering and neuroscience is to model, decode, and control the activity of large populations of neurons that underlie our brain’s functions and dysfunctions. I will present our work toward addressing this challenge. First, I 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. Second, I 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. Third, I present the PSID dynamical modeling framework for neural-behavioral data that can dissociate behaviorally relevant neural dynamics, learn them more accurately, and uncover their dimensionality. I also show how we can further disentangle the intrinsic behaviorally relevant dynamics from input dynamics, thus revealing that the former can be remarkably similar across animals and tasks unlike overall intrinsic dynamics. Finally, I turn my attention to nonlinear dynamical modeling. I present a new neural network modeling framework that localizes the nonlinearity in the neural-behavioral transformation to enable interpretation, prioritizes the learning of behaviorally relevant dynamics, and extends across data modalities. These dynamical models, decoders, and controllers can enable a new generation of brain-machine interfaces for personalized therapy in neurological and neuropsychiatric disorders.
 
Bio:
Maryam M. Shanechi is Professor 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). She is also the founder and director of a newly established USC Center for Neurotechnology. 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.