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

Caglar Cakan, Cristiana Dimulescu, Liliia Khakimova, Daniela Obst, Agnes Flöel, Klaus Obermayer

During slow-wave sleep, the brain is in a self-organized regime in which slow oscillations (SOs) between up- and down-states propagate across the cortex. We address the mechanism of how SOs emerge and can recruit large parts of the brain using a whole-brain model based on empirical connectivity data. Individual brain areas generate SOs that are induced by a local adaptation mechanism. Optimal fits to human resting-state fMRI data and EEG during deep sleep are found at critical values of the adaptation strength 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 due to the heterogeneous network structure of the human brain. 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.

Front. Comput. Neurosci. 15:800101 (2022)


evolutionary algorithmmean-field modelslow oscillationsslow-wave sleepwhole-brain model
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