CRCNS Research Proposal: Contributions of the Thalamus and Basal Ganglia to Neocortical Beta Oscillation: A Novel Computational Hypothesis

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For over 80 years, researchers have observed rhythmic electrical activity in the neocortex, the outer shell of neurons covering the mammalian brain. Despite the prevalence of these oscillations, their meaning for computation--if and how they help us move, perceive and/or think--is a topic of intense debate. Beta oscillations, rhythmic activity at 15‐29 Hz, are prominent in neocortex. Overexpression of beta is a hallmark of Parkinson's disease, and treatments that relieve its motor symptoms also diminish beta. Similarly, beta oscillations predict failure by human subjects to perceive sensory stimuli. These findings directly implicate beta as important for information processing and healthy brain function. Understanding the detailed origins of beta is crucial to knowing its role and to potentially guiding targeted therapies.The present study will test a recently developed hypothesis that explains the natural expression patterns of beta in the human brain. This hypothesis emerged from a detailed biophysically accurate and multi‐area computational neural model. To test this hypothesis, the proposed research will use detailed simulations of multiple brain areas to guide experimental recordings and brain stimulation. These data will in turn be used to advance and constrain the model. As part of this research, the model will be elaborated to include interconnected elements from the basal ganglia, thalamus and neocortex.These same areas will be targets for neural recording, to understand how their activity correlates with neocortical beta in anesthetized and behaving preparations processing sensory information. Causal testing of model predictions will be achieved by leveraging recent innovations in optogenetics, the application of light pulses to turn neurons on and off with millisecond precision and cell-type specificity. Optogenetics will test in a real brain the sufficiency and necessity of the activity patterns that are predicted by the model to lead to expression of this brain rhythm.