During the sleep-wake cycle, the brain undergoes profound dynamical changes, which are manifested subjectively as transitions between conscious experience and unconsciousness. Yet, neurophysiological signatures that can objectively distinguish different consciousness states based are scarce. Here, we show that differences in the level of brain-wide signals can reliably distinguish different stages of sleep and anesthesia from the awake state in human and monkey resting state data. Moreover, a whole-brain computational model can faithfully reproduce changes in global synchronization and other metrics such as the structure-function relationship, integration and segregation across vigilance states. We demonstrate that the awake brain is close to a Hopf bifurcation, which naturally coincides with the emergence of globally correlated fMRI signals. Furthermore, simulating lesions of individual brain areas highlight the importance of connectivity hubs in the posterior brain and subcortical structures in maintaining the model in the awake state, as predicted by graph-theoretical analyses of structural data.

中文翻译：

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猴子和人类 fMRI 信号的全脑同步模式中的意识特征

在睡眠-觉醒周期中，大脑经历了深刻的动态变化，主观上表现为有意识体验和无意识之间的过渡。然而，能够客观区分不同意识状态的神经生理学特征很少见。在这里，我们表明全脑信号水平的差异可以可靠地区分睡眠和麻醉的不同阶段与人类和猴子静息状态数据中的清醒状态。此外，全脑计算模型可以忠实地再现全局同步和其他指标的变化，例如跨警戒状态的结构-功能关系、整合和分离。我们证明清醒的大脑接近 Hopf 分叉，这自然与全局相关 fMRI 信号的出现相吻合。