Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
Neuroimage. 2021 Feb 1;226:117470. doi: 10.1016/j.neuroimage.2020.117470. Epub 2020 Nov 1.
During the sleep-wake cycle, the brain undergoes profound dynamical changes, which manifest 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 fMRI resting state data. Moreover, a whole-brain computational model can faithfully reproduce changes in global synchronization and other metrics such as functional connectivity, 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 highlights the importance of connectivity hubs in the posterior brain and subcortical nuclei for maintaining the model in the awake state, as predicted by graph-theoretical analyses of structural data.
在睡眠-觉醒周期中,大脑会经历深刻的动力学变化,这些变化在主观上表现为意识体验和无意识之间的转变。然而,能够客观地区分不同意识状态的神经生理特征却很少。在这里,我们展示了在人类和猴子的 fMRI 静息态数据中,基于全脑信号水平的差异可以可靠地区分不同的睡眠和麻醉阶段与清醒状态。此外,一个全脑计算模型可以忠实地再现警觉状态下全局同步和其他指标(如功能连接、结构-功能关系、整合和分离)的变化。我们证明,清醒的大脑接近于 Hopf 分岔,这与全局相关的 fMRI 信号的出现自然吻合。此外,模拟单个脑区的损伤突出了后脑和皮质下核团连接枢纽对于保持模型处于清醒状态的重要性,这与结构数据的图论分析预测相符。