Neural Decoding and Control of Multiscale Brain Networks: From Motor to Mood

Abstract

In this talk, I first discuss our recent work on modeling, decoding, and controlling multisite human brain activity underlying mood states. I present a multiscale dynamical modeling framework that allows us, for the first time, to decode mood variations and identify brain sites that are most predictive of mood. I then develop a system identification approach that can predict large-scale brain network dynamics (output) in response to electrical stimulation (input) to enable closed-loop control of brain activity. Finally, I demonstrate that our modeling framework can uncover multiscale neural dynamics from hybrid spike-field activity in monkeys performing unconstrained movements and can further combine information from multiple scales of activity and model their different time-scales and statistical profiles. These models, decoders, and controllers could facilitate future closed-loop therapies for neurological and neuropsychiatric disorders and help probe neural circuits.