Showing papers on "Non-rapid eye movement sleep published in 2018"

Polysomnographic phenotypes and their cardiovascular implications in obstructive sleep apnoea

Abstract: Background Obstructive sleep apnoea (OSA) is a heterogeneous disorder, and improved understanding of physiologic phenotypes and their clinical implications is needed. We aimed to determine whether routine polysomnographic data can be used to identify OSA phenotypes (clusters) and to assess the associations between the phenotypes and cardiovascular outcomes. Methods Cross-sectional and longitudinal analyses of a multisite, observational US Veteran (n=1247) cohort were performed. Principal components-based clustering was used to identify polysomnographic features in OSA’s four pathophysiological domains (sleep architecture disturbance, autonomic dysregulation, breathing disturbance and hypoxia). Using these features, OSA phenotypes were identified by cluster analysis (K-means). Cox survival analysis was used to evaluate longitudinal relationships between clusters and the combined outcome of incident transient ischaemic attack, stroke, acute coronary syndrome or death. Results Seven patient clusters were identified based on distinguishing polysomnographic features: ‘mild’, ‘periodic limb movements of sleep (PLMS)’, ‘NREM and arousal’, ‘REM and hypoxia’, ‘hypopnoea and hypoxia’, ‘arousal and poor sleep’ and ‘combined severe’. In adjusted analyses, the risk (compared with ‘mild’) of the combined outcome (HR (95% CI)) was significantly increased for ‘PLMS’, (2.02 (1.32 to 3.08)), ‘hypopnoea and hypoxia’ (1.74 (1.02 to 2.99)) and ‘combined severe’ (1.69 (1.09 to 2.62)). Conventional apnoea–hypopnoea index (AHI) severity categories of moderate (15≤AHI Conclusions Among patients referred for OSA evaluation, routine polysomnographic data can identify physiological phenotypes that capture risk of adverse cardiovascular outcomes otherwise missed by conventional OSA severity classification.

Thalamic dual control of sleep and wakefulness.

Abstract: Slow waves (0.5-4 Hz) predominate in the cortical electroencephalogram during non-rapid eye movement (NREM) sleep in mammals. They reflect the synchronization of large neuronal ensembles alternating between active (UP) and quiescent (Down) states and propagating along the neocortex. The thalamic contribution to cortical UP states and sleep modulation remains unclear. Here we show that spontaneous firing of centromedial thalamus (CMT) neurons in mice is phase-advanced to global cortical UP states and NREM-wake transitions. Tonic optogenetic activation of CMT neurons induces NREM-wake transitions, whereas burst activation mimics UP states in the cingulate cortex and enhances brain-wide synchrony of cortical slow waves during sleep, through a relay in the anterodorsal thalamus. Finally, we demonstrate that CMT and anterodorsal thalamus relay neurons promote sleep recovery. These findings suggest that the tonic and/or burst firing pattern of CMT neurons can modulate brain-wide cortical activity during sleep and provides dual control of sleep-wake states.

Local and Widespread Slow Waves in Stable NREM Sleep: Evidence for Distinct Regulation Mechanisms.

Abstract: Previous work showed that two types of slow waves are temporally dissociated during the transition to sleep: widespread, large and steep slow waves predominate early in the falling asleep period (type I), while smaller, more circumscribed slow waves become more prevalent later (type II). Here, we studied the possible occurrence of these two types of slow waves in stable non-REM (NREM) sleep and explored potential differences in their regulation. A heuristic approach based on slow wave synchronization efficiency was developed and applied to high-density electroencephalographic (EEG) recordings collected during consolidated NREM sleep to identify the potential type I and type II slow waves. Slow waves with characteristics compatible with those previously described for type I and type II were identified in stable NREM sleep. Importantly, these slow waves underwent opposite changes across the night, with only type II slow waves displaying a clear homeostatic regulation. In addition, we showed that the occurrence of type I slow waves was often followed by larger type II slow waves, whereas the occurrence of type II slow waves was usually followed by smaller type I waves. Finally, type II slow waves were associated with a relative increase in spindle activity, while type I slow waves triggered periods of high-frequency activity. Our results provide evidence for the existence of two distinct slow wave synchronization processes that underlie two different types of slow waves. These slow waves may have different functional roles and mark partially distinct "micro-states" of the sleeping brain.

Dual function of thalamic low-vigilance state oscillations: rhythm-regulation and plasticity.

Abstract: During inattentive wakefulness and non-rapid eye movement (NREM) sleep, the neocortex and thalamus cooperatively engage in rhythmic activities that are exquisitely reflected in the electroencephalogram as distinctive rhythms spanning a range of frequencies from <1 Hz slow waves to 13 Hz alpha waves. In the thalamus, these diverse activities emerge through the interaction of cell-intrinsic mechanisms and local and long-range synaptic inputs. One crucial feature, however, unifies thalamic oscillations of different frequencies: repetitive burst firing driven by voltage-dependent Ca2+ spikes. Recent evidence reveals that thalamic Ca2+ spikes are inextricably linked to global somatodendritic Ca2+ transients and are essential for several forms of thalamic plasticity. Thus, we propose herein that alongside their rhythm-regulation function, thalamic oscillations of low-vigilance states have a plasticity function that, through modifications of synaptic strength and cellular excitability in local neuronal assemblies, can shape ongoing oscillations during inattention and NREM sleep and may potentially reconfigure thalamic networks for faithful information processing during attentive wakefulness.

NREM sleep parasomnias as disorders of sleep-state dissociation.

Abstract: Non-rapid eye movement (NREM) sleep parasomnias (or NREM parasomnias) are fascinating disorders with mysterious neurobiological substrates. These conditions are common and often severe, with social, personal and forensic implications. The NREM parasomnias include sleepwalking, sleep terrors and confusional arousals - collectively termed disorders of arousal (DOAs) - as well as less well-known entities such as sleep-related sexual behaviours and eating disorders. Affected patients can exhibit waking behaviours arising abruptly out of NREM sleep. Although the individual remains largely unresponsive to the external environment, their EEG shows both typical sleep-like and wake-like features, and they occasionally report dreaming afterwards. Therefore, these disorders offer a unique natural model to explore the abnormal coexistence of local sleep and wake brain activity and the dissociation between behaviour and various aspects of consciousness. In this article, we critically review major findings and updates on DOAs, focusing on neurophysiological studies, and offer an overview of new clinical frontiers and promising future research areas. We advocate a joint effort to inform clinicians and the general public about the management and follow-up of these conditions. We also strongly encourage collaborative multicentre studies to add more objective polysomnographic criteria to the current official diagnostic definitions and to develop clinical practice guidelines, multidisciplinary research approaches and evidence-based medical care.

Regulation of REM and Non-REM Sleep by Periaqueductal GABAergic Neurons.

Abstract: Mammalian sleep consists of distinct rapid eye movement (REM) and non-REM (NREM) states. The midbrain region ventrolateral periaqueductal gray (vlPAG) is known to be important for gating REM sleep, but the underlying neuronal mechanism is not well understood. Here, we show that activating vlPAG GABAergic neurons in mice suppresses the initiation and maintenance of REM sleep while consolidating NREM sleep, partly through their projection to the dorsolateral pons. Cell-type-specific recording and calcium imaging reveal that most vlPAG GABAergic neurons are strongly suppressed at REM sleep onset and activated at its termination. In addition to the rapid changes at brain state transitions, their activity decreases gradually between REM sleep and is reset by each REM episode in a duration-dependent manner, mirroring the accumulation and dissipation of REM sleep pressure. Thus, vlPAG GABAergic neurons powerfully gate REM sleep, and their firing rate modulation may contribute to the ultradian rhythm of REM/NREM alternation.

Electrophysiological mechanisms of human memory consolidation

Abstract: Consolidation stabilizes memory traces after initial encoding. Rodent studies suggest that memory consolidation depends on replay of stimulus-specific activity patterns during fast hippocampal “ripple” oscillations. Here, we measured replay in intracranial electroencephalography recordings in human epilepsy patients, and related replay to ripples. Stimulus-specific activity was identified using representational similarity analysis and then tracked during waking rest and sleep after encoding. Stimulus-specific gamma (30–90 Hz) activity during early (100–500 ms) and late (500–1200 ms) encoding is spontaneously reactivated during waking state and sleep, independent of later memory. Ripples during nREM sleep, but not during waking state, trigger replay of activity from the late time window specifically for remembered items. Ripple-triggered replay of activity from the early time window during nREM sleep is enhanced for forgotten items. These results provide the first electrophysiological evidence for replay related to memory consolidation in humans, and point to a prominent role of nREM ripple-triggered replay in consolidation processes.

Sleep Disturbances as a Risk Factor for Stroke.

Abstract: Sleep, a vital process of human being, is carefully orchestrated by the brain and consists of cyclic transitions between rapid eye movement (REM) and non-REM (NREM) sleep Autonomic tranquility during NREM sleep is characterized by vagal dominance and stable breathing, providing an opportunity for the cardiovascular-neural axis to restore homeostasis, in response to use, distress or fatigue inflicted during wakefulness Abrupt irregular swings in sympathovagal balance during REM sleep act as phasic loads on the resting cardiovascular system Any causes of sleep curtailment or fragmentation such as sleep restriction, sleep apnea, insomnia, periodic limb movements during sleep, and shift work, not only impair cardiovascular restoration but also impose a stress on the cardiovascular system Sleep disturbances have been reported to play a role in the development of stroke and other cardiovascular disorders This review aims to provide updated information on the role of abnormal sleep in the development of stroke, to discuss the implications of recent research findings, and to help both stroke clinicians and researchers understand the importance of identification and management of sleep pathology for stroke prevention and care

Dreaming in NREM sleep: a high-density EEG study of slow waves and spindles

Abstract: Dreaming can occur in both rapid eye movement (REM) and non-REM (NREM) sleep. We recently showed that in both REM and NREM sleep, dreaming is associated with local decreases in slow wave activity (SWA) in posterior brain regions. To expand these findings, here we asked how specific features of slow waves and spindles, the hallmarks of NREM sleep, relate to dream experiences. Fourteen healthy human subjects (10 females) underwent nocturnal high-density EEG recordings combined with a serial awakening paradigm. Reports of dreaming, compared with reports of no experience, were preceded by fewer, smaller, and shallower slow waves, and faster spindles, especially in central and posterior cortical areas. We also identified a minority of very steep and large slow waves in frontal regions, which occurred on a background of reduced SWA and were associated with high-frequency power increases (local "microarousals") heralding the successful recall of dream content. These results suggest that the capacity of the brain to generate experiences during sleep is reduced in the presence of neuronal off-states in posterior and central brain regions, and that dream recall may be facilitated by the intermittent activation of arousal systems during NREM sleep.SIGNIFICANCE STATEMENT By combining high-density EEG recordings with a serial awakening paradigm in healthy subjects, we show that dreaming in non-rapid eye movement sleep occurs when slow waves in central and posterior regions are sparse, small, and shallow. We also identified a small subset of very large and steep frontal slow waves that are associated with high-frequency activity increases (local "microarousals") heralding successful recall of dream content. These results provide noninvasive measures that could represent a useful tool to infer the state of consciousness during sleep.

Automatic sleep staging based on SVD, VMD, HHT and morphological features of single-lead ECG signal

Abstract: Electroencephalogram (EEG) signals, which are among the primary polysomnography (PSG) signals used for sleep staging, are difficult to obtain and interpret. However, it is much easier to obtain and interpret electrocardiogram (ECG) signals. The use of ECG signals for automatic sleep staging systems could bring practicality to these systems. In this study, ECG signals were used to identify the wake (W), non-rapid eye movement (NREM) and rapid eye movement (REM) stages of the sleep data from two different databases with 17,758 epochs of 28 subjects (21 healthy subjects and 7 obstructive sleep apnea (OSA) patients) in total. Four different methods were used to extract features from these signals: Singular Value Decomposition (SVD), Variational Mode Decomposition (VMD), Hilbert Huang Transform (HHT), and Morphological method. As a result of applying the methods separately, four different data sets were obtained. The four different datasets were given to the Wrapper Subset Evaluation system with the best-first search algorithm. After the feature selection procedure, the datasets were separately classified by using the Random Forest classifier. The results were interpreted by using the essential statistical criteria. Among the methods, morphological method was the most successful and it was followed by SVD in terms of success rate for both two databases. For the first database, the mean classification accuracy rate, Kappa coefficient and mean F-measure value of the Morphological method were found as 87.11%, 0.7369, 0.869 for the healthy and 78.08%, 0.5715, 0.782 for the patient, respectively. For the second database, the same statistical measures were determined as 77.02%, 0.4308, 0.755 for the healthy and 76.79%, 0.5227, 0.759 for the patient, respectively. The performance results of the study, which is consistent with real life applications, were compared with the previous studies in this field listed in the literature.

Regulation of cortical activity and arousal by the matrix cells of the ventromedial thalamic nucleus

Abstract: The "non-specific" ventromedial thalamic nucleus (VM) has long been considered a candidate for mediating cortical arousal due to its diffuse, superficial projections, but direct evidence was lacking. Here, we show in mice that the activity of VM calbindin1-positive matrix cells is high in wake and REM sleep and low in NREM sleep, and increases before cortical activity at the sleep-to-wake transition. Optogenetic stimulation of VM cells rapidly awoke all mice from NREM sleep and consistently caused EEG activation during slow wave anesthesia, while arousal did not occur from REM sleep. Conversely, chemogenetic inhibition of VM decreased wake duration. Optogenetic activation of the "specific" ventral posteromedial nucleus (VPM) did not cause arousal from either NREM or REM sleep. Thus, matrix cells in VM produce arousal and broad cortical activation during NREM sleep and slow wave anesthesia in a way that accounts for the effects classically attributed to "non-specific" thalamic nuclei.

Differential roles of sleep spindles and sleep slow oscillations in memory consolidation

Abstract: Sleep plays an important role in the consolidation of recent memories. However, the cellular and synaptic mechanisms of consolidation during sleep remain poorly understood. In this study, using a realistic computational model of the thalamocortical network, we tested the role of Non-Rapid Eye Movement (NREM) sleep in memory consolidation. We found that sleep spindles (the hallmark of N2 stage sleep) and slow oscillations (the hallmark of N3 stage sleep) both promote replay of the spike sequences learned in the awake state and replay was localized at the trained network locations. Memory performance improved after a period of NREM sleep but not after the same time period in awake. When multiple memories were trained, the local nature of the spike sequence replay during spindles allowed replay of the distinct memory traces independently, while slow oscillations promoted competition that could prevent replay of the weak memories in a presence of the stronger memory traces. This could lead to extinction of the weak memories unless when sleep spindles (N2 sleep) preceded slow oscillations (N3 sleep), as observed during the natural sleep cycle. Our study presents a mechanistic explanation for the role of sleep rhythms in memory consolidation and proposes a testable hypothesis how the natural structure of sleep stages provides an optimal environment to consolidate memories.

Deep learning in the cross-time frequency domain for sleep staging from a single-lead electrocardiogram

Abstract: OBJECTIVE This study classifies sleep stages from a single lead electrocardiogram (ECG) using beat detection, cardiorespiratory coupling in the time-frequency domain and a deep convolutional neural network (CNN). APPROACH An ECG-derived respiration (EDR) signal and synchronous beat-to-beat heart rate variability (HRV) time series were derived from the ECG using previously described robust algorithms. A measure of cardiorespiratory coupling (CRC) was extracted by calculating the coherence and cross-spectrogram of the EDR and HRV signal in 5 min windows. A CNN was then trained to classify the sleep stages (wake, rapid-eye-movement (REM) sleep, non-REM (NREM) light sleep and NREM deep sleep) from the corresponding CRC spectrograms. A support vector machine was then used to combine the output of CNN with the other features derived from the ECG, including phase-rectified signal averaging (PRSA), sample entropy, as well as standard spectral and temporal HRV measures. The MIT-BIH Polysomnographic Database (SLPDB), the PhysioNet/Computing in Cardiology Challenge 2018 database (CinC2018) and the Sleep Heart Health Study (SHHS) database, all expert-annotated for sleep stages, were used to train and validate the algorithm. MAIN RESULTS Ten-fold cross validation results showed that the proposed algorithm achieved an accuracy (Acc) of 75.4% and a Cohen's kappa coefficient of [Formula: see text] = 0.54 on the out of sample validation data in the classification of Wake, REM, NREM light and deep sleep in SLPDB. This rose to Acc = 81.6% and [Formula: see text] = 0.63 for the classification of Wake, REM sleep and NREM sleep and Acc = 85.1% and [Formula: see text] = 0.68 for the classification of NREM sleep versus REM/wakefulness in SLPDB. SIGNIFICANCE The proposed ECG-based sleep stage classification approach that represents the highest reported results on non-electroencephalographic data and uses datasets over ten times larger than those in previous studies. By using a state-of-the-art QRS detector and deep learning model, the system does not require human annotation and can therefore be scaled for mass analysis.

Theta Bursts Precede, and Spindles Follow, Cortical and Thalamic Downstates in Human NREM Sleep.

Abstract: Since their discovery, slow oscillations have been observed to group spindles during non-REM sleep. Previous studies assert that the slow-oscillation downstate (DS) is preceded by slow spindles (10–12 Hz) and followed by fast spindles (12–16 Hz). Here, using both direct transcortical recordings in patients with intractable epilepsy (n = 10, 8 female), as well as scalp EEG recordings from a healthy cohort (n = 3, 1 female), we find in multiple cortical areas that both slow and fast spindles follow the DS. Although discrete oscillations do precede DSs, they are theta bursts (TBs) centered at 5–8 Hz. TBs were more pronounced for DSs in NREM stage 2 (N2) sleep compared with N3. TB with similar properties occur in the thalamus, but unlike spindles they have no clear temporal relationship with cortical TB. These differences in corticothalamic dynamics, as well as differences between spindles and theta in coupling high-frequency content, are consistent with NREM theta having separate generative mechanisms from spindles. The final inhibitory cycle of the TB coincides with the DS peak, suggesting that in N2, TB may help trigger the DS. Since the transition to N1 is marked by the appearance of theta, and the transition to N2 by the appearance of DS and thus spindles, a role of TB in triggering DS could help explain the sequence of electrophysiological events characterizing sleep. Finally, the coordinated appearance of spindles and DSs are implicated in memory consolidation processes, and the current findings redefine their temporal coupling with theta during NREM sleep. SIGNIFICANCE STATEMENT Sleep is characterized by large slow waves which modulate brain activity. Prominent among these are downstates (DSs), periods of a few tenths of a second when most cells stop firing, and spindles, oscillations at ∼12 times a second lasting for ∼a second. In this study, we provide the first detailed description of another kind of sleep wave: theta bursts (TBs), a brief oscillation at ∼six cycles per second. We show, recording during natural sleep directly from the human cortex and thalamus, as well as on the scalp, that TBs precede, and spindles follow DSs. TBs may help trigger DSs in some circumstances, and could organize cortical and thalamic activity so that memories can be consolidated during sleep.

The rostromedial tegmental nucleus is essential for non-rapid eye movement sleep

Abstract: The rostromedial tegmental nucleus (RMTg), also called the GABAergic tail of the ventral tegmental area, projects to the midbrain dopaminergic system, dorsal raphe nucleus, locus coeruleus, and other regions. Whether the RMTg is involved in sleep-wake regulation is unknown. In the present study, pharmacogenetic activation of rat RMTg neurons promoted non-rapid eye movement (NREM) sleep with increased slow-wave activity (SWA). Conversely, rats after neurotoxic lesions of 8 or 16 days showed decreased NREM sleep with reduced SWA at lights on. The reduced SWA persisted at least 25 days after lesions. Similarly, pharmacological and pharmacogenetic inactivation of rat RMTg neurons decreased NREM sleep. Electrophysiological experiments combined with optogenetics showed a direct inhibitory connection between the terminals of RMTg neurons and midbrain dopaminergic neurons. The bidirectional effects of the RMTg on the sleep-wake cycle were mimicked by the modulation of ventral tegmental area (VTA)/substantia nigra compacta (SNc) dopaminergic neuronal activity using a pharmacogenetic approach. Furthermore, during the 2-hour recovery period following 6-hour sleep deprivation, the amount of NREM sleep in both the lesion and control rats was significantly increased compared with baseline levels; however, only the control rats showed a significant increase in SWA compared with baseline levels. Collectively, our findings reveal an essential role of the RMTg in the promotion of NREM sleep and homeostatic regulation.

Hippocampal Network Oscillations Rescue Memory Consolidation Deficits Caused by Sleep Loss

Abstract: Oscillations in the hippocampal network during sleep are proposed to play a role in memory storage by patterning neuronal ensemble activity. Here we show that following single-trial fear learning, sleep deprivation (which impairs memory consolidation) disrupts coherent firing rhythms in hippocampal area CA1. State-targeted optogenetic inhibition of CA1 parvalbumin-expressing (PV+) interneurons during postlearning NREM sleep, but not REM sleep or wake, disrupts contextual fear memory (CFM) consolidation in a manner similar to sleep deprivation. NREM-targeted inhibition disrupts CA1 network oscillations which predict successful memory storage. Rhythmic optogenetic activation of PV+ interneurons following learning generates CA1 oscillations with coherent principal neuron firing. This patterning of CA1 activity rescues CFM consolidation in sleep-deprived mice. Critically, behavioral and optogenetic manipulations that disrupt CFM also disrupt learning-induced stabilization of CA1 ensembles' communication patterns in the hours following learning. Conversely, manipulations that promote CFM also promote long-term stability of CA1 communication patterns. We conclude that sleep promotes memory consolidation by generating coherent rhythms of CA1 network activity, which provide consistent communication patterns within neuronal ensembles. Most importantly, we show that this rhythmic patterning of activity is sufficient to promote long-term memory storage in the absence of sleep.

Sleep and EEG Power Spectral Analysis in Three Transgenic Mouse Models of Alzheimer’s Disease: APP/PS1, 3xTgAD, and Tg2576

Abstract: BACKGROUND Sleep disturbances have long been associated with Alzheimer's disease (AD), and there is a growing interest in how these disturbances might impact AD pathophysiology. Despite this growing interest, surprisingly little is known about how sleep architecture and the broader neuronal network are affected in widely used transgenic mouse models of AD. OBJECTIVE We analyzed sleep and electroencephalography (EEG) power in three transgenic mouse models of AD, using identical and commercially available hardware and analytical software. The goal was to assess the suitability of these mouse lines to model sleep and the broader neuronal network dysfunction measured by EEG in AD. METHODS Tg2576, APP/PS1, and 3xTgAD transgenic AD mice were studied using in vivo EEG recordings for sleep/wake time and power spectral analysis. RESULTS Both the APP/PS1 model at 8- 10 months and the Tg2576 model at 12 months of age exhibited stage-dependent decreases in theta and delta power, and shifts in the power spectra toward higher frequencies. Stage-dependent power spectral analyses showed no changes in the 3xTgAD model at 18 months of age. The percentage of time spent awake, in non-rapid eye movement sleep (NREM), or in rapid-eye-movement sleep (REM) was not different between genotypes in any of the transgenic lines. CONCLUSION Our findings are consistent with data from several other transgenic AD models as well as certain studies in patients with mild cognitive impairment. Further studies will be needed to better understand the correlation between EEG spectra and AD pathophysiology, both in AD models and the human condition.