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

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.