Estimating microscopic biophysical information from macroscopic neuroimaging data via the inversion of neural-glial mass models

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Progress in the neuroimaging field in recent years has made it possible to create maps of hemodynamic-based and electrophysiology-based functional connectivity, as well as diffusion MRI-based structural connectivity maps. However, a major challenge we face is not only the reproduction of realistic brain connectivity and dynamics, but also the identification of important features and mechanism of these dynamics and of the network structure that are critical to brain function. The broad goal of this research program is the development of large-scale theoretical brain models to bridge the gap between structure and function in the brain, and identify key network properties underlying the empirically observed dynamics. More specifically, this proposal is focused on the development of a macroscopic generative model of brain activity built from multiple interconnected neural-glial mass models (NGMMs), which will bridge the microscopic and macroscopic spatial scales in the brain. Thus, by estimating this model from macroscopic data we can infer microscopic data such as excitatory and inhibitory synaptic activity, which is indispensable for understanding the mechanism underlying brain information processing.