Multiscale Oscillatory Dynamics in Cortical Function

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Project SummaryThe proposed project aims at developing a model of cortical oscillation in the brain as they arebelieved to encode in a precise way all the different brain-state dynamics. Nowadays, we are ableto obtain measurements of neural activities at multiple scales starting at the molecular level withdetailed chemical interactions, to cellular levels with localized recordings of synaptic currents andfiring dynamic, to networks with recordings from neuronal assemblies, to systems with recordingsof brain activation from multiple cortical areas.The model will have the following attributes: 1) it is consistent with physiological hypothesesand experimental recordings under specific brain states, 2) it is hierarchical, describing the brainactivity at multiple resolutions as well as describing brain state transitions as information-staterefinements and 3) it is amenable to efficient simulations.This model will further our understanding of several phenomena evident in the cortical dy-namics of the brain: 1) it will explain the recruitment process the brain undergoes in terms ofsynchronization, 2) it will explain the robustness of the synchronization process, 3) it will explainthe smoothness in the dynamics of brain-state transitions, 4) it will explain the relationship be-tween the number of synchronized cells and the frequency of synchronization, 5) it will explainthe dynamics and integration of the top-down dynamic evolution based on internal stimuli andthe bottom-up processing of sensory information, and 5) it will explain the timing properties andefficiency in execution.The derived detailed cortical model will then be integrated with our existing system-levelmodel of the motor control system. In particular, we aim to study the role of oscillations ininformation transfer across constituent cortical subsystems, and to subsequently identify uniquesignatures of motion planning in scalp EEG recordings.Models of cortical oscillations will have utility in various ways. On one hand, if used aspredictors, they can point to important experimental activities designed to invalidate the models,which will result in a substantial progress of our understanding of the brain processes. On theother hand, these models are critical as a diagnostic tool, for example, such models can be usedto detect seizures and brain tumors, to calibrate drug action during anesthesia, to study cognitivetask signatures, and to study mental retardation. Finally, they can provide the appropriatedynamic description for a proper design of neural prosthesis.Intellectual Merit: The proposed research provides a bridge between neurophysiology, neu-roanatomy, and engineering through the development of a consistent computational model ofcortical activities. The research is conducted by a multi-disciplinary group with expertise inboth modeling, analysis and design of systems in general, as well as modeling and analysis ofthe nervous system in particular, with a track record of collaboration through NSF support. Theproposed research will be augmented with an experimental component through our ongoing motorsystem control collaboration with workers in Massachusetts General Hospital.Broad Impact: Beyond the utility mentioned above, this research is systematically closing thegap between the fragmented research on understanding the nervous system conducted by biolo-gists, statisticians, computer scientists and system theorists. With this unification, computationalbiology developments will aid in understanding and potentially curing much of the neural diseasesknown.Progress in this research removes the boundaries between the strongly segregated fields such assciences and engineering. Such multi-disciplinary research requires complete interactions betweendifferent groups with different domains of expertise. This research will result in PhD theses cov-ering both such fields as demonstrated by our KDI project. In addition, courses across disciplinescan be structured to provide better multi-disciplinary training at the undergraduate level.