Slow Cortical Waves via Cyclicity

Abraham, I.; Shahsavarani, B.; Zimmerman, B.; Husain, F.; baryshnikov, y.

doi:10.1101/2021.05.16.444387

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bioRxiv · 10.1101/2021.05.16.444387

Slow Cortical Waves via Cyclicity

Abraham, I.·Shahsavarani, B.·Zimmerman, B.·Husain, F.·baryshnikov, y.

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Abstract

Fine-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.

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Abraham, I., Shahsavarani, B., Zimmerman, B., Husain, F., baryshnikov, y.. 2021-05-17. Slow Cortical Waves via Cyclicity. https://doi.org/10.1101/2021.05.16.444387

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