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 Singh^1,2,3, 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