Skip to main content

Dynamical modeling, decoding, and control of multiscale brain networks: from motor to mood

Speaker: Maryam Shanechi

I will present our work on dynamical modeling, decoding, and control of multiscale brain network activity toward restoring lost motor and emotional function in brain disorders. 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) toward enabling closed-loop control of neural activity. Further, I extend our modeling framework to enable dissociating and uncovering behaviorally relevant neural dynamics that can otherwise be missed, such as those during naturalistic movements. I then show how our framework can model the dynamics of multiple modalities and spatiotemporal scales of brain activity simultaneously, thus enhancing decoding and uncovering the relationship across scales. Finally, I develop recurrent neural network (RNN) models that can dissect the source of nonlinearity in behaviorally relevant neural dynamics. These dynamical models, decoders, and controllers can enable a new generation of brain-machine interfaces for personalized therapy in neurological and neuropsychiatric disorders.

𝗛𝗼𝘄 𝗰𝗮𝗻 𝘆𝗼𝘂 𝗷𝗼𝗶𝗻?
Register here.
(Registration closes 2 hours before the beginning of the seminar).

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 Department of Biomedical Engineering at the University of Southern California. 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, and a DoD Multidisciplinary University Research Initiative (MURI) Award.



Share this: