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Poster C79

Precision data-driven modeling of cortical dynamics reveals idiosyncratic mechanisms underlying canonical oscillations

Poster Session C - Sunday, April 14, 2024, 5:00 – 7:00 pm EDT, Sheraton Hall ABC

Matthew Singh1,2,3, Todd Braver2, Michael Cole3, ShiNung Ching1; 1Washington University in St. Louis, Dept. of Electrical and Systems Engineering, 2Washington University in St. Louis, Dept. of Psychological and Brain Sciences, 3Rutgers University, Newark, Center for Molecular and Behavioral Neuroscience

Task-free brain activity affords unique insight into the functional structure of brain network dynamics and is a strong marker of individual differences, but has proven difficult to model at fast time-scales. Previous approaches have either used normative models, in which only a small number of parameters need to be estimated, or relied upon statistical characterizations rather than true generative (predictive) modeling. By contrast, in this work, we present a novel optimization framework that makes it possible to directly invert brain-wide models from single-subject recordings. This technique provides a powerful neurocomputational tool for interrogating mechanisms underlying individual brain dynamics (``precision brain models") and make quantitative predictions. We extensively validate the models' performance in forecasting future brain activity and predicting individual variability in key M/EEG markers. Lastly, we demonstrate the power of our technique in resolving individual differences in the generation of alpha and beta-frequency oscillations. We reveal a powerful characterization of subjects based upon models' attractor topology and a dynamical-systems mechanism whereby these topologies generate individual variation in the expression of alpha vs. beta waves.

Topic Area: METHODS: Neuroimaging


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April 13–16  |  2024