Dynamical modeling, decoding, and control of multiscale brain networks: from motor to mood (Maryam Shanechi -- University of Southern California)

  • Starts: 4:00 pm on Thursday, October 28, 2021
In this talk, I first discuss our recent work on modeling, decoding, and controlling multiregional brain network dynamics underlying 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 and decode brain network dynamics across multiple spatiotemporal scales simultaneously, thus adding information and uncovering the relationship across scales. These dynamical models, decoders, and controllers can provide new neuroscientific insight and enable closed-loop neurotechnologies for personalized therapy in neurological and neuropsychiatric disorders.
Zoom; Refreshments will be served in MCS B24, 111 Cummington Mall

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