Dynamic coupling between the central and autonomic nervous systems during sleep: A review
Neuroscience and Biobehavioral Reviews
Sleep is characterized by coordinated cortical and cardiac oscillations reflecting communication between the central (CNS) and autonomic (ANS) nervous systems. Here, we review fluctuations in ANS activity in association with CNS-defined sleep stages and cycles, and with phasic cortical events during sleep (e.g., arousals, K-complexes). Recent novel analytic methods reveal a dynamic organization of integrated physiological networks during sleep and indicate how multiple factors (e.g., sleep
... ture, age, sleep disorders) affect "CNS-ANS coupling". However, these data are mostly correlational and there is a lack of clarity of the underlying physiology, making it challenging to interpret causality and direction of coupling. Experimental manipulations (e.g., evoking K-complexes or arousals) provide information on the precise temporal sequence of cortical-cardiac activity, and are useful for investigating physiological pathways underlying the CNS-ANS coupling. With the emergence of new analytical approaches and a renewed interest in ANS and CNS communication during sleep, future work may reveal novel insights into sleep and cardiovascular interactions during health and disease, in which coupling could be adversely impacted. interconnections. For example, continuous linear and non-linear relationships between time-series of cortical and cardiac function, considering strength, timecourse, and directionality. Assessment of ANS during sleep Polysomnography (PSG) is the gold-standard method to objectively assess sleep using multiple sources of information. The characterization of sleep-wake states (i.e., wakefulness, the stages N1, N2, and N3 of non-rapid-eye-movement [NREM] sleep, and rapid-eyemovement [REM] sleep) is fully described in the scoring manual of the American Academy of Sleep Medicine (AASM) (see Berry et al., 2017) . This characterization follows standardized rules based on visual detection and discrimination of electroencephalography (EEG), electromyography, and electrooculography features within fixed discrete time windows (30 s epochs) across the night. EEG is typically sampled at ≥ 200 Hz to fully capture high frequency activity. Sleep can be broadly divided into two main states characterized by specific features and EEG patterns. In humans, REM sleep is characterized by a "wake-like" low-amplitude high frequency EEG activity, irregular and sharp eye movements, and low muscle tone. NREM sleep, which constitutes between 75% and 80% of the total sleep period, is characterized by progressive EEG synchronization. It begins with low amplitude and mixed frequency EEG activity with a predominant theta (4-7 Hz) rhythm in NREM sleep stage N1. This is followed by NREM sleep stage N2, characterized by the appearance of spontaneous Kcomplexes (the largest event in the human EEG, consisting of a positive-negative-positive waveform ≥ 0.5 s) and sleep spindles (bursts of distinct 11-16 Hz waves, ≥ 0.5 s). There is then a predominance of high-voltage slow wave oscillations (1-4 Hz) in NREM sleep stage N3. Sleep shows a strong ultradian rhythm such that REM and NREM alternate with a cycle length of ~90 min across the night. There is predominance of N3 sleep during cycles in the first part of the night with a progressive increase in time spent in REM sleep across the night (Kryger et al., 2015) . Importantly, both sleep states (NREM and REM), as well as phasic sleep events (e.g., arousals, K-complexes) are accompanied by distinct patterns of tonic and phasic ANS activation (see Section 1.2). Conventional sleep staging offers an overall picture of a night's sleep, and can be clinically useful in assessing sleep quality and composition. However, it is limited by being visually determined, arbitrary, and providing discrete characterization of sleep. The current scoring guidelines (Berry et al., 2017) are essentially similar to those codified 50 years ago by a committee led by Rechtschaffen and Kales (Kales and Rechtschaffen, 1968) for use with analog data recorded on pen chart recorders. These criteria were, in turn, largely based on visual scoring systems developed 80 years ago (Loomis et al., 1937) . Importantly, this approach constrains the analysis of all aspects of human physiology (e.g., cardiovascular measures, hormonal variations) within arbitrarily-defined, discrete, and CNS-defined sleep periods. Automated quantitative EEG analysis has the potential to overcome limitations inherent in visual scoring and provides continuous and detailed information on the state of cortical activation and EEG complexity. Typically, an EEG spectrum is obtained by applying a fast de Zambotti et al. Heart rate (or heart period, which is its reciprocal) has the advantage of enabling assessment of cardiac ANS tone (i.e., average ANS activity) and of transient (beat-to-beat) cardiac modulation, and thus it has been repeatedly applied in studies of CNS-ANS coupling. The available evidence indicates that heart period is linearly related to the frequency of cardiac de Zambotti et al. However, papers reporting the LF/HF ratio in the sleep field have often overlooked other computational issues (Burr, 2007) . The LF and HF components of HRV power are often expressed in normalized units (nu), as the % of the total power of HRV. In normalizing LF and HF power, however, it is common practice to remove the VLF component from total de Zambotti et al.