Harnessing olfactory bulb oscillations to perform fully brain-based sleep-scoring and real-time monitoring of anaesthesia depth.
Harnessing olfactory bulb oscillations to perform fully brain-based sleep-scoring and real-time monitoring of anaesthesia depth.
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DOI：
10.1371/journal.pbio.2005458
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发表时间：
2018-11
期刊：
影响因子：
9.8
通讯作者：
Benchenane K
中科院分区：
文献类型：
--
作者：
Bagur S;Lacroix MM;de Lavilléon G;Lefort JM;Geoffroy H;Benchenane K
实时跟踪与睡眠或麻醉相关的警觉状态已经是世纪以来的目标。然而，睡眠评分目前不能单独使用大脑信号进行，尽管伴随着睡眠状态变化的深层神经调节转换。因此，从本质上讲，睡眠和清醒之间的操作区别是静止和运动，尽管这种一对一映射失败的情况很多。在这里，我们证明，在自由移动的小鼠中使用局部场电位（LFP）记录，嗅球（OB）中的伽马（50-70 Hz）功率允许对睡眠和觉醒进行明确分类，从而提供了一个基于大脑的标准来区分这两种警惕状态，而不依赖于运动活动。再加上海马体的θ活动，它允许制定一个睡眠评分算法，只依赖于大脑活动。该方法与基于肌肉活动（肌电图[EMG]）和视频跟踪的经典方法具有90%以上的同源性。此外，与EMG相反，OB伽马功率允许在模糊的情况下正确区分睡眠和不动，例如与恐惧相关的冻结。我们使用海马θ振荡和OB γ振荡的瞬时功率来构建一个2D相空间，该相空间在整个时间内，在单个小鼠和小鼠品系中以及在经典药物治疗下都是高度稳健的。在这个空间内的轨迹的动态分析产生了一个新的表征睡眠/觉醒过渡：而醒来是一个快速和直接的过渡，可以模拟的弹道轨迹，入睡是最好的描述为一个随机和渐进的状态变化。最后，我们证明了OB振荡也允许我们跟踪其他警惕状态。非快速眼动（NREM）和快速眼动（REM）睡眠可以基于β（10-15 Hz）功率以高精度区分。更重要的是，我们表明，麻醉深度可以跟踪真实的时间使用OB伽马功率。实际上，伽马功率预测和预期在恒定麻醉下的稳定状态下和在恢复期期间动态地对刺激的运动响应。总而言之，这种方法为大脑状态的多时间尺度表征开辟了道路，并为警惕水平提供了前所未有的窗口。与清醒、睡眠和麻醉相关的警戒状态的实时跟踪已经是超过世纪的目标。然而，清醒和不同睡眠状态的识别目前不能用大脑信号常规地执行，而是依赖于运动活动。在这里，我们证明了50-70 Hz的电振荡在小鼠的嗅球（OB）是一个可靠的指标，全球的大脑状态。用植入的电极记录这种活动可以清楚地分类睡眠和清醒，而不需要运动活动监测。我们构建了一个完全自动的睡眠评分算法，该算法仅依赖于大脑活动，并且在动物之间和药物给药后的整个时间内都是稳健的。我们的方法还跟踪在真实的时间的麻醉深度在稳定状态下恒定的麻醉和动态的恢复期间从麻醉。此外，该指数预测麻醉下对伤害性刺激的反应性。总而言之，这种方法开辟了基于OB记录的警戒状态表征的途径。
Real-time tracking of vigilance states related to both sleep or anaesthesia has been a goal for over a century. However, sleep scoring cannot currently be performed with brain signals alone, despite the deep neuromodulatory transformations that accompany sleep state changes. Therefore, at heart, the operational distinction between sleep and wake is that of immobility and movement, despite numerous situations in which this one-to-one mapping fails. Here we demonstrate, using local field potential (LFP) recordings in freely moving mice, that gamma (50–70 Hz) power in the olfactory bulb (OB) allows for clear classification of sleep and wake, thus providing a brain-based criterion to distinguish these two vigilance states without relying on motor activity. Coupled with hippocampal theta activity, it allows the elaboration of a sleep scoring algorithm that relies on brain activity alone. This method reaches over 90% homology with classical methods based on muscular activity (electromyography [EMG]) and video tracking. Moreover, contrary to EMG, OB gamma power allows correct discrimination between sleep and immobility in ambiguous situations such as fear-related freezing. We use the instantaneous power of hippocampal theta oscillation and OB gamma oscillation to construct a 2D phase space that is highly robust throughout time, across individual mice and mouse strains, and under classical drug treatment. Dynamic analysis of trajectories within this space yields a novel characterisation of sleep/wake transitions: whereas waking up is a fast and direct transition that can be modelled by a ballistic trajectory, falling asleep is best described as a stochastic and gradual state change. Finally, we demonstrate that OB oscillations also allow us to track other vigilance states. Non-REM (NREM) and rapid eye movement (REM) sleep can be distinguished with high accuracy based on beta (10–15 Hz) power. More importantly, we show that depth of anaesthesia can be tracked in real time using OB gamma power. Indeed, the gamma power predicts and anticipates the motor response to stimulation both in the steady state under constant anaesthetic and dynamically during the recovery period. Altogether, this methodology opens the avenue for multi-timescale characterisation of brain states and provides an unprecedented window onto levels of vigilance. Real-time tracking of vigilance states related to wake, sleep, and anaesthesia has been a goal for over a century. However identification of wakefulness and different sleep states cannot currently be performed routinely with brain signals and instead relies on motor activity. Here we demonstrate that 50–70 Hz electrical oscillations in the olfactory bulb (OB) of mice are a reliable indicator for global brain states. Recording this activity with an implanted electrode allows for clear classification of sleep and wake, without the need for motor activity monitoring. We construct a fully automatic sleep scoring algorithm that relies on brain activity alone and is robust throughout time, between animals, and after drug administration. Our method also tracks in real time the depth of anaesthesia both in the steady state under constant anaesthetic and dynamically during the recovery period from anaesthesia. Furthermore, this index predicts responsiveness to noxious stimulation under anaesthesia. Altogether, this methodology opens the avenue for characterisation of vigilance states based on OB recordings.
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DOI：
10.1016/0013-4694(53)90010-8
发表时间：
1953-01-01
期刊：
ELECTROENCEPHALOGRAPHY AND CLINICAL NEUROPHYSIOLOGY
影响因子：
--
作者：
ARDUINI, A;MORUZZI, G
通讯作者：
MORUZZI, G
影响因子：
2.9
作者：
Gascuel J;Lemoine A;Rigault C;Datiche F;Benani A;Penicaud L;Lopez-Mascaraque L
通讯作者：
Lopez-Mascaraque L
影响因子：
2.9
作者：
Brankack, Jurij;Kukushka, Valeriy I.;Draguhn, Andreas
通讯作者：
Draguhn, Andreas
影响因子：
22.4
作者：
CAIN, DP
通讯作者：
CAIN, DP
影响因子：
2.5
作者：
Bathellier, B;Lagier, S;Lledo, PM
通讯作者：
Lledo, PM