Prof. Matthew Singh , UIUC, will lecture on "Data-Driven Modeling for Individualized Neuroscience."
Abstract: A key goal of human brain mapping is to decipher how individual differences in brain signaling and dynamics (shaped by the underlying structure) relate to individual differences in cognition/behavior. Developing mechanistic models of individual human brains is one part of this endeavor. While long-term statistics, such as functional-connectivity, have been modeled extensively, individual-differences in transient, dynamical interactions are less understood. Current whole-brain modeling approaches have proven valuable for hypothesis-generation but are not typically specified at the single-subject level or directly constructed from timeseries data. I will present an algorithmic optimization framework that makes it possible to directly invert and parameterize brain-wide dynamical-systems models involving hundreds of interacting brain areas and thousands of unknown biological parameters, from single-subject time-series recordings forming a digital twin of individual human brains. This technique provides a powerful neurocomputational tool for interrogating mechanisms underlying individual brain dynamics, assimilating timeseries data with models, and making quantitative predictions.