Inken Toedt, Hermann Gesine, Tagliazucchi Enzo, Todtenhaupt Inga Karin, Laufs Helmut, von Wegner Frederic
Department of Neurology, Christian-Albrechts University, Arnold-Heller-Strasse 3, 24105, Kiel, Germany.
Institute of Sexual Medicine & Forensic Psychiatry and Psychotherapy, Christian- Albrechts University, Schwanenweg 24, 24105, Kiel, Germany.
Brain Topogr. 2025 Sep 5;38(6):63. doi: 10.1007/s10548-025-01144-9.
Different levels of reduced consciousness characterise human sleep stages at the behavioural level. On electroencephalography (EEG), the identification of sleep stages predominantly relies on localised oscillatory power within distinct frequency bands. Several theoretical frameworks converge on the central significance of long-range information sharing in maintaining consciousness, which experimentally manifests as high functional connectivity (FC) between distant brain regions. Here, we test the hypothesis that EEG-FC reflects sleep stages and hence changes in consciousness. We retrospectively investigated sleep EEG recordings in 14 participants undergoing all stages of non-rapid eye movement (NREM) sleep. We quantified FC with six phase coupling metrics and used the FC coefficients between electrode pairs as features for a gradient boosting classifier trained to distinguish between sleep stages. To characterise FC during each stage of NREM sleep, we compared these metrics regarding their classification accuracy and analysed the ranked feature importance across all electrode pairs. We observed frequency-specific differences in FC between sleep stages for all metrics except the imaginary part of coherence. Alpha coupling decreased from wake to sleep stages N1 and N2, whereas delta coupling increased in deep sleep (N3). FC-based sleep classifiers yielded 51% (phase locking index) to 73% (phase locking value) classification accuracy. Distributed FC patterns in the alpha band ranked highest in terms of feature importance. In a limited sample of 14 subjects, we demonstrated that FC computed from phase information changes significantly across sleep stages. The finding that EEG phase patterns are indicative of sleep stages supports the hypothesis that long-range and spatially distributed phase coupling within frequency bands, especially within the alpha band, is an electrophysiological correlate of consciousness across sleep stages.
在行为层面，不同程度的意识减退是人类睡眠阶段的特征。在脑电图（EEG）上，睡眠阶段的识别主要依赖于不同频段内的局部振荡功率。几个理论框架都聚焦于远程信息共享在维持意识方面的核心意义，这在实验中表现为遥远脑区之间的高功能连接性（FC）。在此，我们检验EEG-FC反映睡眠阶段并因此反映意识变化这一假设。我们回顾性研究了14名经历非快速眼动（NREM）睡眠各阶段的参与者的睡眠EEG记录。我们用六种相位耦合指标量化FC，并将电极对之间的FC系数用作梯度提升分类器的特征，该分类器经过训练以区分睡眠阶段。为了表征NREM睡眠各阶段的FC，我们比较了这些指标的分类准确性，并分析了所有电极对的特征重要性排名。除了相干性的虚部外，我们观察到所有指标在睡眠阶段之间的FC存在频率特异性差异。从清醒到N1和N2睡眠阶段，α耦合降低，而在深度睡眠（N3）中δ耦合增加。基于FC的睡眠分类器的分类准确率为51%（锁相指数）至73%（锁相值）。α波段中分布式FC模式在特征重要性方面排名最高。在14名受试者的有限样本中，我们证明从相位信息计算出的FC在睡眠阶段之间有显著变化。EEG相位模式指示睡眠阶段这一发现支持了以下假设：频段内，特别是α波段内的远程和空间分布式相位耦合是睡眠各阶段意识的电生理相关指标。
