Abstract
Arousals can be roughly characterized by punctual intrusions of wakefulness into sleep. In a standard perspective, using human electroencephalography (EEG) data, arousals are associated to slow-wave rhythms and K-complex brain activity. The physiological mechanisms that give rise to arousals during sleep are not yet fully understood. Moreover, subtle body movement patterns, which may characterize arousals both in human and in animals, are usually not detectable by eye perception and are not in general present in sleep studies. In this paper, we focus attention on accelerometer records (AR) to characterize and predict arousal during slow wave sleep (SWS) stage of mice. Furthermore, we recorded the local field potentials (LFP) from the CA1 region in the hippocampus and paired with accelerometer data. The hippocampus signal was also used here to identify the SWS stage. We analyzed the AR dynamics of consecutive arousals using recurrence technique and the determinism (DET) quantifier. Recurrence is a fundamental property of dynamical systems, which can be exploited to characterize time series properties. The DET index evaluates how similar are the evolution of close trajectories: in this sense, it computes how accurate are predictions based on past trajectories. For all analyzed mice in this work, we observed, for the first time, the occurrence of a universal dynamic pattern a few seconds that precedes the arousals during SWS sleep stage based only on the AR signal. The predictability success of an arousal using DET from AR is nearly 90%, while similar analysis using LFP of hippocampus brain region reveal 88% of success. Noteworthy, our findings suggest an unique dynamical behavior pattern preceding an arousal of AR data during sleep. Thus, the employment of this technique applied to AR data may provide useful information about the dynamics of neuronal activities that control sleep-waking switch during SWS sleep period. We argue that the predictability of arousals observed through DET(AR) can be functionally explained by a respiratory-driven modification of neural states. Finally, we believe that the method associating AR data with other physiologic events such as neural rhythms can become an accurate, convenient and non-invasive way of studying the physiology and physiopathology of movement and respiratory processes during sleep.
Highlights
The sleep-wake cycle in mammals is controlled by the interactions of different neuronal systems located in distinct brain regions, including hypothalamus and brainstem [1]
We show a typical analysis of the accelerometer records (AR) temporal behavior pattern in Fig 2(b), and the DET quantifier in the Fig 2(a)
We focus our attention here in the AR signal and the respective determinism quantifier, we plotted the local field potential from CA1 area to indicate the transitional states in sleep-wake cycle
Summary
The sleep-wake cycle in mammals is controlled by the interactions of different neuronal systems located in distinct brain regions, including hypothalamus and brainstem [1]. The corresponding physiological substrate of the homeostatic component is the delta wave sleep in rodents and slow wave sleep (SWS) in humans In humans, this component has its major expression during the first part of sleep phase and is dramatically reduced throughout the rest of sleep duration [5]. We know that the duration of episodes and the SWS dynamics are dependent on body size and the metabolism of the species Small animals, such as rodents, disclose polyphasic sleep profile with multiple and alternate occurrences of sleep and waking stages. During SWS stage, several episodes of arousals occur and the subject usually returns, after a few seconds, to the previous SWS electrophysiological pattern [6,7,8]
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