Psychology Department, University of Montreal, QC, Canada.
Psychology Department, University of Montreal, QC, Canada.
Neuroimage. 2018 Oct 1;179:30-39. doi: 10.1016/j.neuroimage.2018.05.069. Epub 2018 Jun 7.
Rhythmic neuronal synchronization across large-scale networks is thought to play a key role in the regulation of conscious states. Changes in neuronal oscillation amplitude across states of consciousness have been widely reported, but little is known about possible changes in the temporal dynamics of these oscillations. The temporal structure of brain oscillations may provide novel insights into the neural mechanisms underlying consciousness. To address this question, we examined long-range temporal correlations (LRTC) of EEG oscillation amplitudes recorded during both wakefulness and anesthetic-induced unconsciousness. Importantly, the time-varying EEG oscillation envelopes were assessed over the course of a sevoflurane sedation protocol during which the participants alternated between states of consciousness and unconsciousness. Both spectral power and LRTC in oscillation amplitude were computed across multiple frequency bands. State-dependent differences in these features were assessed using non-parametric tests and supervised machine learning. We found that periods of unconsciousness were associated with increases in LRTC in beta (15-30Hz) amplitude over frontocentral channels and with a suppression of alpha (8-13Hz) amplitude over occipitoparietal electrodes. Moreover, classifiers trained to predict states of consciousness on single epochs demonstrated that the combination of beta LRTC with alpha amplitude provided the highest classification accuracy (above 80%). These results suggest that loss of consciousness is accompanied by an augmentation of temporal persistence in neuronal oscillation amplitude, which may reflect an increase in regularity and a decrease in network repertoire compared to the brain's activity during resting-state consciousness.
跨大规模网络的节律性神经元同步被认为在意识状态的调节中起着关键作用。意识状态变化中神经元振荡幅度的变化已被广泛报道，但对于这些振荡的时间动态可能发生的变化知之甚少。脑振荡的时间结构可能为意识背后的神经机制提供新的见解。为了解决这个问题，我们研究了在清醒和麻醉诱导无意识状态下记录的 EEG 振荡幅度的长程时间相关性（LRTC）。重要的是，在参与者在意识和无意识状态之间交替的七氟醚镇静方案过程中，评估了随时间变化的 EEG 振荡包络。在多个频带中计算了光谱功率和振荡幅度的 LRTC。使用非参数检验和监督机器学习评估了这些特征在状态依赖性上的差异。我们发现，无意识期与额叶中央通道的β（15-30Hz）幅度的 LRTC 增加以及枕顶电极的α（8-13Hz）幅度的抑制有关。此外，在单个脑电周期上训练以预测意识状态的分类器表明，β LRTC 与α幅度的组合提供了最高的分类准确性（超过 80%）。这些结果表明，意识丧失伴随着神经元振荡幅度的时间持久性增加，这可能反映了与休息状态意识下大脑活动相比，规则性增加和网络曲目减少。
