During slow-wave sleep, the brain is in a URLanized regime in which slow oscillations (SOs) between up- and down-states propagate across the cortex. Here we address how SOs emerge locally and can recruit large parts of the brain . A whole-brain model that produces bistable states and adaptation-induced SOs is fit to fMRI and EEG datausing a multi-objective evolutionary algorithm . Optimal fits  are found at critical values of the adaptation strength where the model produces a wide range of local and global SOs with realistic  statistics. Local oscillations are more frequent, last shorter, and have a lower amplitude. Global oscillations spread as waves of silence across the brain . The well-known directionality of propagation from anterior to posterior regions is explained by heterogeneities of the connectome . Our results demonstrate the applicability of whole-brain models beyond the resting-state and their potential for elucidating the spatiotemporal patterns of brain oscillations in general .
During slow-wave sleep, the brain is in a URLanized 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 .
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Here we address
We address the mechanism of
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locally
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. A
using a
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that produces bistable states and adaptation-induced SOs is fit to fMRI and EEG datausing a multi-objective evolutionary algorithm
based on empirical connectivity data. Individual brain areas generate SOs that are induced by a local adaptation mechanism
A
to human resting-state fMRI data and EEG during deep sleep
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wide range of
balance between
A
spatiotemporal
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. The well-known directionality of propagation
, traveling
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is explained by heterogeneities of the connectome
due to the heterogeneous network structure of the human brain
R
applicability
utility
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beyond the resting-state and their potential for elucidating the spatiotemporal patterns of brain oscillations in general
for explaining the origin of large-scale cortical oscillations and how they are shaped by the connectome