CAREER: Control-Aware System Identification of Heterogenous Multiscale Brain Network Dynamics

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This Faculty Early Career Development (CAREER) project aims to develop mathematical models of the human brain, with an eye on laying the foundation for their implementation in clinical treatments. Treatments of neurological and psychiatric disorders impose over $1 trillion annually on the US population alone. A large body of literature on mathematical modeling in neuroscience had little impact on clinical treatments because most models either rely on simplifying assumptions or use machine learning methods that obscure the link to the underlying biology. This research will develop a new category of mathematical models for the brain that are at once biologically meaningful and interpretable, without relying on simplifying assumptions. These models will be rooted in engineering approaches that combine data-driven and nonlinear dynamical systems methods. The research is tightly integrated with a diverse and solid body of educational activities targeted towards high school, undergraduate, and graduate students at UCR, the local Inland Empire community, and across the globe.This project will develop data-driven models of the human brain that rigorously incorporate three critical but often ignored aspects of biological neural networks: (i) spanning across multiple scales, (ii) heterogeneity, and (iii) response to neuromodulation. The first thrust will be achieved through data-driven modeling of feedforward and feedback interactions between neural dynamics at different spatial scales (neurons, neural populations, and brain regions), that move beyond simple macroscopic readouts of microscopic dynamics and enhance our understanding of how macroscopic dynamics emerge from, and feed back into the smaller scales. The second thrust will develop structurally heterogeneous brain models that incorporate data of brain heterogeneities in nonlinearity, dimensionality, and neural code across cortical and subcortical regions. The third thrust will generate a data-driven, sample-efficient framework for modeling the effects of deep brain stimulation at the level of the whole-brain network, thus moving beyond local predictions based on first principle modeling of electromagnetic diffusion. The expected outcomes are potentially transformative models and modeling techniques that provide the neuroscience community with solid and clinically translatable tools for the design of neuromodulation, while also significantly increasing our understanding of the multiscale, heterogeneous, and input-driven dynamics of the human brain.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.