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

Maryam M. Shanechi, University of Southern California
Tuesday May 11, 12-1pm
Zoom Link:聽

Abstract: In this talk, I will discuss our work on dynamical modeling, decoding, and control of large-scale brain network activity underlying naturalistic motor and mood states. I present a multiscale dynamical modeling framework that allows us to decode human mood variations 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 brain 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. Finally, I show how our framework can model brain network activity across multiple spatiotemporal scales simultaneously, thus uncovering multiscale neural dynamics that explain naturalistic reach-and-grasp behavior. These dynamical models, decoders, and controllers can provide new neuroscientific insight and enable brain-machine interfaces for personalized therapy in neurological and neuropsychiatric disorders.