Spatiotemporal patterns of adaptation-induced slow oscillations in a whole-brain model of slow-wave sleep

TL;DR

This study uses a whole-brain model based on empirical data to explore how slow oscillations during sleep emerge and propagate across the cortex, revealing the role of network heterogeneities and adaptation mechanisms.

Contribution

It introduces a novel whole-brain model with local adaptation that reproduces sleep slow oscillation patterns and elucidates their propagation dynamics across the brain.

Findings

Global oscillations travel as waves of silence from anterior to posterior regions.

Heterogeneities in connection strengths influence the initiation of traveling waves.

Model fits to empirical data are optimized near a bifurcation point balancing local and global SOs.

Abstract

During slow-wave sleep, the brain is in a self-organized regime in which slow oscillations (SOs) between up- and down-states travel across the cortex. While an isolated piece of cortex can produce SOs, the brain-wide propagation of these oscillations are thought to be mediated by the long-range axonal connections. We address the mechanism of how SOs emerge and recruit large parts of the brain using a whole-brain model constructed from empirical connectivity data in which SOs are induced independently in each brain area by a local adaptation mechanism. Using an evolutionary optimization approach, good fits to human resting-state fMRI data and sleep EEG data are found at values of the adaptation strength close to a bifurcation where the model produces a balance between local and global SOs with realistic spatiotemporal statistics. Local oscillations are more frequent, last shorter, and have a lower amplitude. Global oscillations spread as waves of silence across the brain, traveling from anterior to posterior regions. These traveling waves are caused by heterogeneities in the brain network in which the connection strengths between brain areas determine which areas transition to a down-state first, and thus initiate traveling waves across the cortex. Our results demonstrate the utility of whole-brain models for explaining the origin of large-scale cortical oscillations and how they are shaped by the connectome.