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The Dynamic Brain: Modeling Neural Dynamics and Interactions From Human Electrophysiological Recordings


"The mind is the music that neural networks play." This quote from computational neurobiologist T.J. Sejnowski underscores a growing scientific consensus that studying the structure and function of vast networks of connections between brain regions is essential to understanding cognitive and affective state maintenance, sensorimotor information processing and control, etiologies and remedies for numerous neuropathologies, as well as a host of other facets of our conscious (and non-conscious) experience. Towards this goal, an ongoing challenge lies in identifying - in vivo in humans - spatiotemporal cortical network dynamics, at the level of individuals and groups, across experimental task conditions, and at the level of single trials.

In the opening chapter of this dissertation, I introduce the Source Information Flow Toolbox (SIFT), a novel open-source software package for identification of neuronal dynamics and causal interactions in electrophysiological source and sensor data. The software integrates with the widely used EEGLAB analysis suite, addressing a need for robust tools for identifying single- and multi-trial multivariate brain network dynamics across time, frequency, anatomical source location, and subjects. I then introduce and assess two new methods (Measure Projection Analysis and Multi-view Hierarchical Bayesian Learning) for statistical analysis of source-level dynamics (including connectivity) across groups of subjects in the presence of missing data. The remaining chapters focus on applications of dynamical modeling approaches in SIFT to open problems within the fields of cognitive neuroscience, clinical neuroscience and neuroengineering. I first present three studies examining single-trial time-varying spatiotemporal network dynamics underlying generation and maintenance of epileptic seizures. Next I present a case study examining the effect of visual feedback on an occipito-parietal-motor network in freezing-of-gait in patients with Parkinson's disease. The final chapters focus on new directions in neuroengineering and brain-computer interfaces (BCI) leveraging neural dynamical system identification. We first review the history and state of the BCI field and summarize important new directions in BCI design. I then present a novel system for real-time mobile brain imaging, artifact rejection, neuronal system identification, and cognitive state prediction, and demonstrate its application in predicting response error commission from cortical network dynamics using a new high-density mobile dry EEG system.

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