The difference between sleep and wakefulness is critical for human health. Sleep takes up one third of our lives and remains one of the most mysterious conditions; it plays an important role in memory consolidation and health restoration. Distinct neural behaviors take place under awake and asleep conditions, according to neuroimaging studies. While disordered transitions between wakefulness and sleep accompany brain disease, further investigation of their specific characteristics is required. In this study, the difference is objectively quantified by means of network controllability. We propose a new pipeline using a public intracranial stereo-electroencephalography (stereo-EEG) dataset to unravel differences in the two conditions in terms of system neuroscience. Because intracranial stereo-EEG records neural oscillations covering large-scale cerebral areas, it offers the highest temporal resolution for recording neural behaviors. After EEG preprocessing, the EEG signals are band-passed into sub-slow (0.1-1 Hz), delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-45 Hz) band oscillations. Then, dynamic functional connectivity is extracted from time-windowed EEG neural oscillations through phase-locking value (PLV) and non-overlapping sliding time windows. Next, average and modal network controllability are implemented on these time-varying brain networks. Based on this preliminary study, it appears that significant differences exist in the dorsolateral frontal-parietal network (FPN), salience network (SN), and default-mode network (DMN). The combination of network controllability and dynamic functional networks offers new insight for characterizing distinctions between awake and asleep stages in the brain. In other words, network controllability captures the underlying brain dynamics under both awake and asleep conditions.
睡眠和清醒之间的差异对人类的健康至关重要，清醒和睡眠之间的转换紊乱伴随脑部疾病，因此需要深入研究其具体特征。本研究引入网络可控性揭示大脑脑电活动中频率成分的功能特异性。具体来说，我们采用一个公开的颅内立体脑电图数据集。首先，记录受试者清醒和睡眠条件下的脑电信号，经过降噪、伪迹去除等预处理方法，通过带通滤波提取亚慢波（0.1~1 Hz）、δ（1~4 Hz）、θ（4~8 Hz）、α（8~13 Hz）、β（13~30 Hz）和γ（30~45 Hz）波段振荡。其次，利用锁相值（PLV）和不重叠滑动时间窗从时间窗脑电神经振荡中提取动态功能连通性。最后，在这些时变大脑网络上计算平均和模态网络的可控性。初步结果显示，清醒和睡眠状态下，不同频段脑电活动在额顶网络（FPN）、显著网络（SN）和默认模式网络（DMN）存在显著差异，即不同频率成分的脑电信号以不同网络控制策略参与大脑清醒和睡眠。网络可控性揭示了清醒和睡眠条件下的潜在大脑动力学，网络可控性和动态功能网络的结合为表征大脑清醒和睡眠阶段的区别提供了新的度量方法。.
睡眠和清醒之间的差异对人类的健康至关重要，清醒和睡眠之间的转换紊乱伴随脑部疾病，因此需要深入研究其具体特征。本研究引入网络可控性揭示大脑脑电活动中频率成分的功能特异性。具体来说，我们采用一个公开的颅内立体脑电图数据集。首先，记录受试者清醒和睡眠条件下的脑电信号，经过降噪、伪迹去除等预处理方法，通过带通滤波提取亚慢波（0.1~1 Hz）、δ（1~4 Hz）、θ（4~8 Hz）、α（8~13 Hz）、β（13~30 Hz）和γ（30~45 Hz）波段振荡。其次，利用锁相值（PLV）和不重叠滑动时间窗从时间窗脑电神经振荡中提取动态功能连通性。最后，在这些时变大脑网络上计算平均和模态网络的可控性。初步结果显示，清醒和睡眠状态下，不同频段脑电活动在额顶网络（FPN）、显著网络（SN）和默认模式网络（DMN）存在显著差异，即不同频率成分的脑电信号以不同网络控制策略参与大脑清醒和睡眠。网络可控性揭示了清醒和睡眠条件下的潜在大脑动力学，网络可控性和动态功能网络的结合为表征大脑清醒和睡眠阶段的区别提供了新的度量方法。