Slow Cortical Waves through Cyclicity Analysis

Abstract

AbstractFine-grained understanding of dynamics in cortical networks is crucial in unpacking brain function. Here, we introduce a novel analytical method to characterize the dynamic interaction between distant brain regions, and apply it to data from the Human Connectome Project.Resting-state fMRI results in time series recordings of the activity of different brain regions, which are aperiodic and lacking a base frequency. Cyclicity Analysis, a novel technique robust with respect to time-reparametrizations, is effective in recovering temporal ordering of such time series along a circular trajectory without assuming any time-scale. Our analysis detected slow cortical waves of activity propagating across the brain with consistent lead-lag relationships between specific brain regions. We also observed short bursts of task-modulated strong temporal ordering that dominate overall lead-lag relationships between pairs of regions in the brain. Our results suggest the possible role played by slow waves of information transmission between brain regions that underlie emergent cognitive function.