Regional slow waves and spindles in human sleep

Mapping Slow Waves by EEG Topography and Source Localization: Effects of Sleep Deprivation

Slow waves are a salient feature of the electroencephalogram (EEG) during non-rapid eye movement (non-REM) sleep. The aim of this study was to assess the topography of EEG power and the activation of brain structures during slow wave sleep under normal conditions and after sleep deprivation. Sleep EEG recordings during baseline and recovery sleep after 40 h of sustained wakefulness were analyzed (eight healthy young men, 27 channel EEG). Power maps were computed for the first non-REM sleep episode (where sleep pressure is highest) in baseline and recovery sleep, at frequencies between 0.5 and 2 Hz. Power maps had a frontal predominance at all frequencies between 0.5 and 2 Hz. An additional occipital focus of activity was observed below 1 Hz. Power maps ≤ 1 Hz were not affected by sleep deprivation, whereas an increase in power was observed in the maps ≥ 1.25 Hz. Based on the response to sleep deprivation, low-delta (0.5-1 Hz) and mid-delta activity (1.25-2 Hz) were dissociated. Electrical sources within the cortex of low- and mid-delta activity were estimated using eLORETA. Source localization revealed a predominantly frontal distribution of activity for low-delta and mid-delta activity. Sleep deprivation resulted in an increase in source strength only for mid-delta activity, mainly in parietal and frontal regions. Low-delta activity dominated in occipital and temporal regions and mid-delta activity in limbic and frontal regions independent of the level of sleep pressure. Both, power maps and electrical sources exhibited trait-like aspects.

Using Oscillating Sounds to Manipulate Sleep Spindles

Introduction

EEG oscillations known as sleep spindles have been linked with various aspects of cognition, but the specific functions they signal remain controversial. Two types of EEG sleep spindles have been distinguished: slow spindles at 11-13.5 Hz and fast spindles at 13.5-16 Hz. Slow spindles exhibit a frontal scalp topography, whereas fast spindles exhibit a posterior scalp topography and have been preferentially linked with memory consolidation during sleep. To advance understanding beyond that provided from correlative studies of spindles, we aimed to develop a new method to systematically manipulate spindles.

Aims and Methods

We presented repeating bursts of oscillating white noise to people during a 90-min afternoon nap. During stage 2 and slow-wave sleep, oscillations were embedded within contiguous 10-s stimulation intervals, each comprising 2 s of white noise amplitude modulated at 12 Hz (targeting slow spindles), 15 Hz (targeting fast spindles), or 50 Hz followed by 8 s of constant white noise.

Results

During oscillating stimulation compared to constant stimulation, parietal EEG recordings showed more slow spindles in the 12-Hz condition, more fast spindles in the 15-Hz condition, and no change in the 50-Hz control condition. These effects were topographically selective, and were absent in frontopolar EEG recordings, where slow spindle density was highest. Spindles during stimulation were similar to spontaneous spindles in standard physiological features, including duration and scalp distribution.

Conclusions

These results define a new method to selectively and noninvasively manipulate spindles through acoustic resonance, while also providing new evidence for functional distinctions between the 2 types of EEG spindles.

Congenital deafness affects deep layers in primary and secondary auditory cortex

Congenital deafness leads to functional deficits in the auditory cortex for which early cochlear implantation can effectively compensate. Most of these deficits have been demonstrated functionally. Furthermore, the majority of previous studies on deafness have involved the primary auditory cortex; knowledge of higher-order areas is limited to effects of cross-modal reorganization. In this study, we compared the cortical cytoarchitecture of four cortical areas in adult hearing and congenitally deaf cats (CDCs): the primary auditory field A1, two secondary auditory fields, namely the dorsal zone and second auditory field (A2); and a reference visual association field (area 7) in the same section stained either using Nissl or SMI-32 antibodies. The general cytoarchitectonic pattern and the area-specific characteristics in the auditory cortex remained unchanged in animals with congenital deafness. Whereas area 7 did not differ between the groups investigated, all auditory fields were slightly thinner in CDCs, this being caused by reduced thickness of layers IV-VI. The study documents that, while the cytoarchitectonic patterns are in general independent of sensory experience, reduced layer thickness is observed in both primary and higher-order auditory fields in layer IV and infragranular layers. The study demonstrates differences in effects of congenital deafness between supragranular and other cortical layers, but similar dystrophic effects in all investigated auditory fields.

Bimodal coupling of ripples and slower oscillations during sleep in patients with focal epilepsy

OBJECTIVE

Differentiating pathologic and physiologic high-frequency oscillations (HFOs) is challenging. In patients with focal epilepsy, HFOs occur during the transitional periods between the up and down state of slow waves. The preferred phase angles of this form of phase-event amplitude coupling are bimodally distributed, and the ripples (80-150 Hz) that occur during the up-down transition more often occur in the seizure-onset zone (SOZ). We investigated if bimodal ripple coupling was also evident for faster sleep oscillations, and could identify the SOZ.

METHODS

Using an automated ripple detector, we identified ripple events in 40-60 min intracranial electroencephalography (iEEG) recordings from 23 patients with medically refractory mesial temporal lobe or neocortical epilepsy. The detector quantified epochs of sleep oscillations and computed instantaneous phase. We utilized a ripple phasor transform, ripple-triggered averaging, and circular statistics to investigate phase event-amplitude coupling.

RESULTS

We found that at some individual recording sites, ripple event amplitude was coupled with the sleep oscillatory phase and the preferred phase angles exhibited two distinct clusters (p < 0.05). The distribution of the pooled mean preferred phase angle, defined by combining the means from each cluster at each individual recording site, also exhibited two distinct clusters (p < 0.05). Based on the range of preferred phase angles defined by these two clusters, we partitioned each ripple event at each recording site into two groups: depth iEEG peak-trough and trough-peak. The mean ripple rates of the two groups in the SOZ and non-SOZ (NSOZ) were compared. We found that in the frontal (spindle, p = 0.009; theta, p = 0.006, slow, p = 0.004) and parietal lobe (theta, p = 0.007, delta, p = 0.002, slow, p = 0.001) the SOZ incidence rate for the ripples occurring during the trough-peak transition was significantly increased.

SIGNIFICANCE

Phase-event amplitude coupling between ripples and sleep oscillations may be useful to distinguish pathologic and physiologic events in patients with frontal and parietal SOZ.

Slow wave sleep disruption increases cerebrospinal fluid amyloid-β levels

See Mander et al. (doi:10.1093/awx174) for a scientific commentary on this article.Sleep deprivation increases amyloid-β, suggesting that chronically disrupted sleep may promote amyloid plaques and other downstream Alzheimer's disease pathologies including tauopathy or inflammation. To date, studies have not examined which aspect of sleep modulates amyloid-β or other Alzheimer's disease biomarkers. Seventeen healthy adults (age 35-65 years) without sleep disorders underwent 5-14 days of actigraphy, followed by slow wave activity disruption during polysomnogram, and cerebrospinal fluid collection the following morning for measurement of amyloid-β, tau, total protein, YKL-40, and hypocretin. Data were compared to an identical protocol, with a sham condition during polysomnogram. Specific disruption of slow wave activity correlated with an increase in amyloid-β40 (r = 0.610, P = 0.009). This effect was specific for slow wave activity, and not for sleep duration or efficiency. This effect was also specific to amyloid-β, and not total protein, tau, YKL-40, or hypocretin. Additionally, worse home sleep quality, as measured by sleep efficiency by actigraphy in the six nights preceding lumbar punctures, was associated with higher tau (r = 0.543, P = 0.045). Slow wave activity disruption increases amyloid-β levels acutely, and poorer sleep quality over several days increases tau. These effects are specific to neuronally-derived proteins, which suggests they are likely driven by changes in neuronal activity during disrupted sleep.

Cortex-wide BOLD fMRI activity reflects locally-recorded slow oscillation-associated calcium waves

Spontaneous slow oscillation-associated slow wave activity represents an internally generated state which is characterized by alternations of network quiescence and stereotypical episodes of neuronal activity - slow wave events. However, it remains unclear which macroscopic signal is related to these active periods of the slow wave rhythm. We used optic fiber-based calcium recordings of local neural populations in cortex and thalamus to detect neurophysiologically defined slow calcium waves in isoflurane anesthetized rats. The individual slow wave events were used for an event-related analysis of simultaneously acquired whole-brain BOLD fMRI. We identified BOLD responses directly related to onsets of slow calcium waves, revealing a cortex-wide BOLD correlate: the entire cortex was engaged in this specific type of slow wave activity. These findings demonstrate a direct relation of defined neurophysiological events to a specific BOLD activity pattern and were confirmed for ongoing slow wave activity by independent component and seed-based analyses.

When a person is in a deep non-dreaming sleep, neurons in their brain alternate slowly between periods of silence and periods of activity. This gives rise to low-frequency brain rhythms called slow waves, which are thought to help stabilize memories. Slow wave activity can be detected on multiple scales, from the pattern of electrical impulses sent by an individual neuron to the collective activity of the brain’s entire outer layer, the cortex. But does slow wave activity in an individual group of neurons in the cortex affect the activity of the rest of the brain?

To find out, Schwalm, Schmid, Wachsmuth et al. took advantage of the fact that slow waves also occur under general anesthesia, and placed anesthetized rats inside miniature whole-brain scanners. A small region of cortex in each rat had been injected with a dye that fluoresces whenever the neurons in that region are active. An optical fiber was lowered into the rat’s brain to transmit the fluorescence signals to a computer. Monitoring these signals while the animals lay inside the scanner revealed that slow-wave activity in any one group of cortical neurons was accompanied by slow-wave activity across the cortex as a whole. This relationship was seen only for slow waves, and not for other brain rhythms.

Slow waves seem to occur in all species of animal with a backbone, and in both healthy and diseased brains. While it is not possible to inject fluorescent dyes into the human brain, it is possible to monitor neuronal activity using electrodes. Comparing local electrode recordings with measures of whole-brain activity from scanners could thus allow similar experiments to be performed in people. There is growing evidence – from animal models and from studies of patients – that slow waves may be altered in Alzheimer’s disease. Further work is required to determine whether detecting these changes could help diagnose disease at earlier stages, and whether reversing them may have therapeutic potential.

Individual Differences in Frequency and Topography of Slow and Fast Sleep Spindles

Sleep spindles are transient oscillatory waveforms that occur during non-rapid eye movement (NREM) sleep across widespread cortical areas. In humans, spindles can be classified as either slow or fast, but large individual differences in spindle frequency as well as methodological difficulties have hindered progress towards understanding their function. Using two nights of high-density electroencephalography recordings from 28 healthy individuals, we first characterize the individual variability of NREM spectra and demonstrate the difficulty of determining subject-specific spindle frequencies. We then introduce a novel spatial filtering approach that can reliably separate subject-specific spindle activity into slow and fast components that are stable across nights and across N2 and N3 sleep. We then proceed to provide detailed analyses of the topographical expression of individualized slow and fast spindle activity. Group-level analyses conform to known spatial properties of spindles, but also uncover novel differences between sleep stages and spindle classes. Moreover, subject-specific examinations reveal that individual topographies show considerable variability that is stable across nights. Finally, we demonstrate that topographical maps depend nontrivially on the spindle metric employed. In sum, our findings indicate that group-level approaches mask substantial individual variability of spindle dynamics, in both the spectral and spatial domains. We suggest that leveraging, rather than ignoring, such differences may prove useful to further our understanding of the physiology and functional role of sleep spindles.

Dynamic reconfiguration of cortical functional connectivity across brain states

Throughout each day, the brain displays transient changes in state, as evidenced by shifts in behavior and vigilance. While the electrophysiological correlates of brain states have been studied for some time, it remains unclear how large-scale cortico-cortical functional connectivity systematically reconfigures across states. Here, we investigate state-dependent shifts in cortical functional connectivity by recording local field potentials (LFPs) during spontaneous behavioral transitions in the ferret using chronically implanted micro-electrocorticographic (µECoG) arrays positioned over occipital, parietal, and temporal cortical regions. To objectively classify brain state, we describe a data-driven approach that projects time-varying LFP spectral properties into brain state space. Distinct brain states displayed markedly different patterns of cross-frequency phase-amplitude coupling and inter-electrode phase synchronization across several LFP frequency bands. The largest across-state differences in functional connectivity were observed between periods of presumed slow-wave and rapid-eye-movement-sleep/active-state, which were characterized by the contrasting phenomena of cortical network fragmentation and global synchronization, respectively. Collectively, our data provide strong evidence that large-scale functional interactions in the brain dynamically reconfigure across behavioral states.

Circadian Disruption Associated with Alzheimer's Disease

Alzheimer's disease (AD) is increasing in prevalence and has a significant impact on caregivers and the healthcare system. One of the many physiologic process affected by AD is the circadian system, with disruption reflected in abnormalities of the sleep-wake cycle. This interaction is bidirectional, with circadian and sleep disruption influencing disease progression. Understanding the bidirectional relationship between AD and circadian disruption may allow for earlier recognition of the potential to develop dementia as well as improved targeted approaches for therapy. Therapies including melatonin and bright light therapy may be advantageous in improving sleep and circadian rhythms and preventing the progression of disease. However, unfortunately, these modalities are not curative, and additional research is needed to improve treatment options for these individuals.

Role of Somatostatin-Positive Cortical Interneurons in the Generation of Sleep Slow Waves

During non-rapid eye-movement (NREM) sleep, cortical and thalamic neurons oscillate every second or so between ON periods, characterized by membrane depolarization and wake-like tonic firing, and OFF periods, characterized by membrane hyperpolarization and neuronal silence. Cortical slow waves, the hallmark of NREM sleep, reflect near-synchronous OFF periods in cortical neurons. However, the mechanisms triggering such OFF periods are unclear, as there is little evidence for somatic inhibition. We studied cortical inhibitory interneurons that express somatostatin (SOM), because ∼70% of them are Martinotti cells that target diffusely layer I and can block excitatory transmission presynaptically, at glutamatergic terminals, and postsynaptically, at apical dendrites, without inhibiting the soma. In freely moving male mice, we show that SOM+ cells can fire immediately before slow waves and their optogenetic stimulation during ON periods of NREM sleep triggers long OFF periods. Next, we show that chemogenetic activation of SOM+ cells increases slow-wave activity (SWA), slope of individual slow waves, and NREM sleep duration; whereas their chemogenetic inhibition decreases SWA and slow-wave incidence without changing time spent in NREM sleep. By contrast, activation of parvalbumin+ (PV+) cells, the most numerous population of cortical inhibitory neurons, greatly decreases SWA and cortical firing, triggers short OFF periods in NREM sleep, and increases NREM sleep duration. Thus SOM+ cells, but not PV+ cells, are involved in the generation of sleep slow waves. Whether Martinotti cells are solely responsible for this effect, or are complemented by other classes of inhibitory neurons, remains to be investigated.SIGNIFICANCE STATEMENT Cortical slow waves are a defining feature of non-rapid eye-movement (NREM) sleep and are thought to be important for many of its restorative benefits. Yet, the mechanism by which cortical neurons abruptly and synchronously cease firing, the neuronal basis of the slow wave, remains unknown. Using chemogenetic and optogenetic approaches, we provide the first evidence that links a specific class of inhibitory interneurons-somatostatin-positive cells-to the generation of slow waves during NREM sleep in freely moving mice.

Optogenetic Investigation of Arousal Circuits

Modulation between sleep and wake states is controlled by a number of heterogeneous neuron populations. Due to the topological proximity and genetic co-localization of the neurons underlying sleep-wake state modulation optogenetic methods offer a significant improvement in the ability to benefit from both the precision of genetic targeting and millisecond temporal control. Beginning with an overview of the neuron populations mediating arousal, this review outlines the progress that has been made in the investigation of arousal circuits since the incorporation of optogenetic techniques and the first in vivo application of optogenetic stimulation in hypocretin neurons in the lateral hypothalamus. This overview is followed by a discussion of the future progress that can be made by incorporating more recent technological developments into the research of neural circuits.

Epileptic interictal discharges are more frequent during NREM slow wave downstates

Epileptiform activity in various but not all epilepsy and recording types and cerebral areas is more frequent in NREM sleep, and especially during sleep periods with high-amplitude EEG slow waves. Slow waves synchronize high-frequency oscillations: physiological activity from the theta through the gamma band usually appears during scalp-positive upstates while epileptiform activity occurs at transitory phases and the scalp-negative downstate. It has been proposed that interictal discharges (IIDs) are facilitated by the high degree of neuronal firing synchrony during slow wave transitory and downstates. This would suggest that their occurrence increases as a function of slow wave synchronization, indicated by greater amplitude, steeper slopes and higher EEG signal synchronization. We investigated the occurrence of IIDs during NREM sleep slow waves in epileptic patients undergoing presurgical electrophysiological monitoring. Intracranially registered IIDs preferentially occurred during the scalp-negative downstates of frontal scalp slow waves in all subjects. IID occurrence was more frequent during larger slow waves in the pooled sample and a subset of subjects. However, slow wave slope steepness and EEG signal synchronization between two frontal scalp channels was not significantly associated with IID occurrence. Our results indicate that IIDs indeed do not occur at the same slow wave phase as physiological rhythms, but contrary to previous hypotheses their occurrence is not strongly affected by EEG synchronization.

Thalamocortical synchronization during induction and emergence from propofol-induced unconsciousness

General anesthesia (GA) is a reversible drug-induced state of altered arousal required for more than 60,000 surgical procedures each day in the United States alone. Sedation and unconsciousness under GA are associated with stereotyped electrophysiological oscillations that are thought to reflect profound disruptions of activity in neuronal circuits that mediate awareness and cognition. Computational models make specific predictions about the role of the cortex and thalamus in these oscillations. In this paper, we provide in vivo evidence in rats that alpha oscillations (10-15 Hz) induced by the commonly used anesthetic drug propofol are synchronized between the thalamus and the medial prefrontal cortex. We also show that at deep levels of unconsciousness where movement ceases, coherent thalamocortical delta oscillations (1-5 Hz) develop, distinct from concurrent slow oscillations (0.1-1 Hz). The structure of these oscillations in both cortex and thalamus closely parallel those observed in the human electroencephalogram during propofol-induced unconsciousness. During emergence from GA, this synchronized activity dissipates in a sequence different from that observed during loss of consciousness. A possible explanation is that recovery from anesthesia-induced unconsciousness follows a "boot-up" sequence actively driven by ascending arousal centers. The involvement of medial prefrontal cortex suggests that when these oscillations (alpha, delta, slow) are observed in humans, self-awareness and internal consciousness would be impaired if not abolished. These studies advance our understanding of anesthesia-induced unconsciousness and altered arousal and further establish principled neurophysiological markers of these states.

Formation and suppression of acoustic memories during human sleep

Sleep and memory are deeply related, but the nature of the neuroplastic processes induced by sleep remains unclear. Here, we report that memory traces can be both formed or suppressed during sleep, depending on sleep phase. We played samples of acoustic noise to sleeping human listeners. Repeated exposure to a novel noise during Rapid Eye Movements (REM) or light non-REM (NREM) sleep leads to improvements in behavioral performance upon awakening. Strikingly, the same exposure during deep NREM sleep leads to impaired performance upon awakening. Electroencephalographic markers of learning extracted during sleep confirm a dissociation between sleep facilitating memory formation (light NREM and REM sleep) and sleep suppressing learning (deep NREM sleep). We can trace these neural changes back to transient sleep events, such as spindles for memory facilitation and slow waves for suppression. Thus, highly selective memory processes are active during human sleep, with intertwined episodes of facilitative and suppressive plasticity.Though memory and sleep are related, it is still unclear whether new memories can be formed during sleep. Here, authors show that people could learn new sounds during REM or light non-REM sleep, but that learning was suppressed when sounds were played during deep NREM sleep.

Sleep to Remember

Scientific investigation into the possible role of sleep in memory consolidation began with the early studies of Jenkins and Dallenbach (1924). Despite nearly a century of investigation with a waxing and waning of interest, the role of sleep in memory processing remains controversial and elusive. This review provides the historical background for current views and considers the relative contribution of two sleep states, rapid eye movement sleep and slow-wave sleep, to offline memory processing. The sequential hypothesis, until now largely ignored, is discussed, and recent literature supporting this view is reviewed.

Molecular Mechanisms of Sleep Homeostasis in Flies and Mammals

Sleep is homeostatically regulated with sleep pressure accumulating with the increasing duration of prior wakefulness. Yet, a clear understanding of the molecular components of the homeostat, as well as the molecular and cellular processes they sense and control to regulate sleep intensity and duration, remain a mystery. Here, we will discuss the cellular and molecular basis of sleep homeostasis, first focusing on the best homeostatic sleep marker in vertebrates, slow wave activity; second, moving to the molecular genetic analysis of sleep homeostasis in the fruit fly Drosophila; and, finally, discussing more systemic aspects of sleep homeostasis.

Stereo-electroencephalography identifies N2 sleep and spindles in human hippocampus

OBJECTIVES

To describe the hippocampal stereo-electroencephalogram during sleep according to sleep stages (including N2 sleep) and cycles, together with the hippocampal spindles.

METHODS

All patients with drug-resistant focal epilepsy undergoing intra-hippocampal implantation between August 2012 and June 2013 at Nancy University Hospital were screened. Six patients with explored hippocampus devoid of pathological features were analyzed. During one night, we identified continuous periods of successive N2, N3 and REM sleep for two full cycles. We performed a spectral analysis of the hippocampal signal for each labeled sleep period.

RESULTS

N2, N3 and REM sleeps were individualized according to their spectral powers, for each frequency band and sleep cycle. Hippocampal spindles showed dynamic intrinsic properties, the 11.5-16Hz frequency band being mainly dominant, whereas the 9-11.5Hz frequency band heightening during the beginning and the end of the transient. For N3 and REM sleep stages, the power of the hippocampal signal was significantly decreased between the first and the second sleep cycle.

CONCLUSION

Distinct N2 sleep, fast spindles and homeostatic profile are all common properties shared by hippocampus and cortex during sleep.

SIGNIFICANCE

The close functional link between hippocampus and cortex may have various sleep-related substrates.

The neurocognitive consequences of sleep restriction: A meta-analytic review

The current meta-analytic review evaluated the effects of experimentally manipulated sleep restriction on neurocognitive functioning. Random-effects models were employed to estimate the overall effect size and the differential effect size across cognitive domains. Age, time of day, age-adjusted sleep deficit, cumulative days of restricted sleep, sleep latency, subjective sleepiness, and biological sex were examined as potential moderators of the effect. Based on a sample of 61 studies, from 71 different populations, findings revealed a significant negative effect of sleep restriction on cognitive processing across cognitive domains (g=-0.383, p<0.001). This effect held for executive functioning (g=-0.324, p<0.001), sustained attention (g=-0.409, p<0.001), and long-term memory (g=-0.192, p=0.002). There was insufficient evidence to detect an effect within the domains of attention, multitask, impulsive decision-making or intelligence. Age group, time of day, cumulative days of restricted sleep, sleep latency, subjective sleepiness, and biological sex were all significant moderators of the overall effect. In conclusion, the current meta-analysis is the first comprehensive review to provide evidence that short-term sleep restriction significantly impairs waking neurocognitive functioning.

Tapping the Brakes: Cellular and Synaptic Mechanisms that Regulate Thalamic Oscillations

Thalamic oscillators contribute to both normal rhythms associated with sleep and anesthesia and abnormal, hypersynchronous oscillations that manifest behaviorally as absence seizures. In this review, we highlight new findings that refine thalamic contributions to cortical rhythms and suggest that thalamic oscillators may be subject to both local and global control. We describe endogenous thalamic mechanisms that limit network synchrony and discuss how these protective brakes might be restored to prevent absence seizures. Finally, we describe how intrinsic and circuit-level specializations among thalamocortical loops may determine their involvement in widespread oscillations and render subsets of thalamic nuclei especially vulnerable to pathological synchrony.

Coordinated Interaction between Hippocampal Sharp-Wave Ripples and Anterior Cingulate Unit Activity

Hippocampal-cortical interaction during sleep promotes transformation of memory for long-term storage in the cortex. In particular, hippocampal sharp-wave ripple-associated neural activation is important for this transformation during slow-wave sleep. The anterior cingulate cortex (ACC) has been shown to be crucial for expression and likely storage of long-term memory. However, little is known about how ACC activity is influenced by hippocampal ripple activity during sleep. We report here about coordinated interactions between hippocampal ripple activity and ACC neural firings. By recording from the ACC and hippocampal CA1 simultaneously in mice, we found that almost all ACC neurons showed increased activity before hippocampal ripple activity; moreover, a subpopulation (17%) displayed a further activation immediately after ripple activity. This postripple activation of ACC neurons correlated positively with ripple amplitude, and the same neurons were excited upon electrical stimulation of the CA1. Interestingly, the preripple activation of ACC neurons was present during the sleep state, but not during the awake state. These results suggest intimate interactions between hippocampal sharp-wave ripples and ACC neurons in a state-dependent manner. Importantly, sharp-wave ripples and associated activation appear to regulate activity of a small population of ACC neurons, a process that may play a critical role in memory consolidation.

SIGNIFICANCE STATEMENT

The hippocampus communicates with the cortex for memory transformation. Memories of previous experiences become less dependent on the hippocampus and increasingly dependent on cortical areas, such as the anterior cingulate cortex (ACC). However, little evidence is available to directly support this hippocampus-to-cortex information transduction hypothesis of memory consolidation. Here we show that a subpopulation of ACC neurons becomes active just after hippocampal ripple activity, and that electrical stimulation of the hippocampus excites the same ACC neurons. In addition, the majority of ACC neurons are activated just before ripple activity during the sleep state, but not during the awake state. These results provide evidence supporting the hypothesis of hippocampus-to-cortex information flow for memory consolidation as well as reciprocal interaction between the hippocampus and the cortex.

Neural Markers of Responsiveness to the Environment in Human Sleep

Sleep is characterized by a loss of behavioral responsiveness. However, recent research has shown that the sleeping brain is not completely disconnected from its environment. How neural activity constrains the ability to process sensory information while asleep is yet unclear. Here, we instructed human volunteers to classify words with lateralized hand responses while falling asleep. Using an electroencephalographic (EEG) marker of motor preparation, we show how responsiveness is modulated across sleep. These modulations are tracked using classic event-related potential analyses complemented by Lempel-Ziv complexity (LZc), a measure shown to track arousal in sleep and anesthesia. Neural activity related to the semantic content of stimuli was conserved in light non-rapid eye movement (NREM) sleep. However, these processes were suppressed in deep NREM sleep and, importantly, also in REM sleep, despite the recovery of wake-like neural activity in the latter. In NREM sleep, sensory activations were counterbalanced by evoked down states, which, when present, blocked further processing of external information. In addition, responsiveness markers correlated positively with baseline complexity, which could be related to modulation in sleep depth. In REM sleep, however, this relationship was reversed. We therefore propose that, in REM sleep, endogenously generated processes compete with the processing of external input. Sleep can thus be seen as a self-regulated process in which external information can be processed in lighter stages but suppressed in deeper stages. Last, our results suggest drastically different gating mechanisms in NREM and REM sleep.

SIGNIFICANCE STATEMENT

Previous research has tempered the notion that sleepers are isolated from their environment. Here, we pushed this idea forward and examined, across all sleep stages, the brain's ability to flexibly process sensory information, up to the decision level. We extracted an EEG marker of motor preparation to determine the completion of the sensory processing chain and explored how it is constrained by baseline and evoked neural activity. In NREM sleep, slow waves elicited by stimuli appeared to block response preparation. We also used a novel analytic approach (Lempel-Ziv complexity) and showed that the ability to process external information correlates with neural complexity. A reversal of the correlation between complexity and motor indices in REM sleep suggests drastically different gating mechanisms across sleep stages.

Increased Stability and Breakdown of Brain Effective Connectivity During Slow-Wave Sleep: Mechanistic Insights from Whole-Brain Computational Modelling

Recent research has found that the human sleep cycle is characterised by changes in spatiotemporal patterns of brain activity. Yet, we are still missing a mechanistic explanation of the local neuronal dynamics underlying these changes. We used whole-brain computational modelling to study the differences in global brain functional connectivity and synchrony of fMRI activity in healthy humans during wakefulness and slow-wave sleep. We applied a whole-brain model based on the normal form of a supercritical Hopf bifurcation and studied the dynamical changes when adapting the bifurcation parameter for all brain nodes to best match wakefulness and slow-wave sleep. Furthermore, we analysed differences in effective connectivity between the two states. In addition to significant changes in functional connectivity, synchrony and metastability, this analysis revealed a significant shift of the global dynamic working point of brain dynamics, from the edge of the transition between damped to sustained oscillations during wakefulness, to a stable focus during slow-wave sleep. Moreover, we identified a significant global decrease in effective interactions during slow-wave sleep. These results suggest a mechanism for the empirical functional changes observed during slow-wave sleep, namely a global shift of the brain's dynamic working point leading to increased stability and decreased effective connectivity.

Correlating Interictal Spikes with Sigma and Delta Dynamics during Non-Rapid-Eye-Movement-Sleep

Interictal spikes (IS) are one of the major hallmarks of epilepsy. Understanding the factors promoting or suppressing IS would increase our comprehension of epilepsy and possibly open new avenues for therapy. Sleep strongly influences epileptic activity, and the modulatory effects of the different sleep stages on IS have been studied for decades. However, several aspects are still disputed, in particular the role of sleep spindles and slow waves in the activation of IS during Non-REM sleep. Here, we correlate the rate of IS with quantitative measures derived from stereo-EEG during one Non-REM cycle in 10 patients suffering from drug-resistant epilepsy due to type 2 focal cortical dysplasia. We show that the IS rate (ISR) is positively correlated with sigma power (a surrogate for sleep-spindle density) but negatively correlated with delta power (surrogate for slow wave activity). In addition, we present two new indices for quantifying the spatial and temporal instability of sleep. We found that both instability indices are correlated with a high ISR. The main contribution of this study is to confirm the suppressive effect of stable deep sleep on IS. This result might influence future guidelines for therapy of patients suffering from epilepsy and sleep disorders.

Using MEG to Understand the Progression of Light Sleep and the Emergence and Functional Roles of Spindles and K-Complexes

We used tomographic analysis of MEG signals to characterize regional spectral changes in the brain at sleep onset and during light sleep. We identified two key processes that may causally link to loss of consciousness during the quiet or "core" periods of NREM1. First, active inhibition in the frontal lobe leads to delta and theta spectral power increases. Second, activation suppression leads to sharp drop of spectral power in alpha and higher frequencies in posterior parietal cortex. During NREM2 core periods, the changes identified in NREM1 become more widespread, but focal increases also emerge in alpha and low sigma band power in frontal midline cortical structures, suggesting reemergence of some monitoring of internal and external environment. Just before spindles and K-complexes (KCs), the hallmarks of NREM2, we identified focal spectral power changes in pre-frontal cortex, mid cingulate, and areas involved in environmental and internal monitoring, i.e., the rostral and sub-genual anterior cingulate. During both spindles and KCs, alpha and low sigma bands increases. Spindles emerge after further active inhibition (increase in delta power) of the frontal areas responsible for environmental monitoring, while in posterior parietal cortex, power increases in low and high sigma bands. KCs are correlated with increase in alpha power in the monitoring areas. These specific regional changes suggest strong and varied vigilance changes for KCs, but vigilance suppression and sharpening of cognitive processing for spindles. This is consistent with processes designed to ensure accurate and uncorrupted memory consolidation. The changes during KCs suggest a sentinel role: evaluation of the salience of provoking events to decide whether to increase processing and possibly wake up, or to actively inhibit further processing of intruding influences. The regional spectral patterns of NREM1, NREM2, and their dynamic changes just before spindles and KCs reveal an edge effect facilitating the emergence of spindles and KCs and defining the precise loci where they might emerge. In the time domain, the spindles are seen in widespread areas of the cortex just as reported from analysis of intracranial data, consistent with the emerging consensus of a differential topography that depends on the kind of memory stored.

Neural oscillations demonstrate that general anesthesia and sedative states are neurophysiologically distinct from sleep

General anesthesia is a man-made neurophysiological state comprised of unconsciousness, amnesia, analgesia, and immobility along with maintenance of physiological stability. Growing evidence suggests that anesthetic-induced neural oscillations are a primary mechanism of anesthetic action. Each anesthetic drug class produces distinct oscillatory dynamics that can be related to the circuit mechanisms of drug action. Sleep is a naturally occurring state of decreased arousal that is essential for normal health. Physiological measurements (electrooculogram, electromyogram) and neural oscillatory (electroencephalogram) dynamics are used to empirically characterize sleep into rapid eye movement sleep and the three stages of non-rapid eye movement sleep. In this review, we discuss the differences between anesthesia- and sleep-induced altered states from the perspective of neural oscillations.

The Phenomenal Contents and Neural Correlates of Spontaneous Thoughts across Wakefulness, NREM Sleep, and REM Sleep

Thoughts occur during wake as well as during dreaming sleep. Using experience sampling combined with high-density EEG, we investigated the phenomenal qualities and neural correlates of spontaneously occurring thoughts across wakefulness, non-rapid eye movement (NREM) sleep, and REM sleep. Across all states, thoughts were associated with activation of a region of the midcingulate cortex. Thoughts during wakefulness additionally involved a medial prefrontal region, which was associated with metacognitive thoughts during wake. Phenomenologically, waking thoughts had more metacognitive content than thoughts during both NREM and REM sleep, whereas thoughts during REM sleep had a more social content. Together, these results point to a core neural substrate for thoughts, regardless of behavioral state, within the midcingulate cortex, and suggest that medial prefrontal regions may contribute to metacognitive content in waking thoughts.

Local aspects of sleep and wakefulness

Slow waves, the hallmark of NREM (Non Rapid Eye Movement) sleep, are not uniformly distributed across the cortical surface, but can occur locally and asynchronously across brain regions. Their regional distribution and amplitude is affected by brain maturation and by time spent awake, mediated in part by experience-dependent changes in synaptic strength. Recent studies have shown that local low-frequency oscillations (<10Hz) can also occur in REM sleep and during wakefulness, leading to region-specific cognitive errors. Local decreases and increases of slow wave activity in posterior brain regions have been linked to the occurrence of dreaming and to unconscious sleep, respectively. Finally, the coexistence of local sleep-like and wake-like patterns in different brain areas is characteristic of several sleep disorders and may offer insights into these conditions.

Coordination of cortical and thalamic activity during non-REM sleep in humans

Every night, the human brain produces thousands of downstates and spindles during non-REM sleep. Previous studies indicate that spindles originate thalamically and downstates cortically, loosely grouping spindle occurrence. However, the mechanisms whereby the thalamus and cortex interact in generating these sleep phenomena remain poorly understood. Using bipolar depth recordings, we report here a sequence wherein: (1) convergent cortical downstates lead thalamic downstates; (2) thalamic downstates hyperpolarize thalamic cells, thus triggering spindles; and (3) thalamic spindles are focally projected back to cortex, arriving during the down-to-upstate transition when the cortex replays memories. Thalamic intrinsic currents, therefore, may not be continuously available during non-REM sleep, permitting the cortex to control thalamic spindling by inducing downstates. This archetypical cortico-thalamo-cortical sequence could provide the global physiological context for memory consolidation during non-REM sleep.

During non-REM sleep, the thalamus produces spindles and the cortex produces downstates, but the interaction between these two areas in these sleep phenomena is not understood. Here, authors describe the dynamic loop between the thalamus and cortex that organizes the production of spindles and downstates in the human brain.

To sleep or not to sleep: neuronal and ecological insights

Daily, animals need to decide when to stop engaging in cognitive processes and behavioral responses to the environment, and go to sleep. The main processes regulating the daily organization of sleep and wakefulness are circadian rhythms and homeostatic sleep pressure. In addition, motivational processes such as food seeking and predator evasion can modulate sleep/wake behaviors. Here, we discuss the principal processes regulating the propensity to stay awake or go to sleep-focusing on neuronal and behavioral aspects. We first introduce the neuronal populations involved in sleep/wake regulation. Next, we describe the circadian and homeostatic drives for sleep. Then, we highlight studies demonstrating various effects of motivational processes on sleep/wake behaviors, and discuss possible neuronal mechanisms underlying their control.

Deciphering Neural Codes of Memory during Sleep

Memories of experiences are stored in the cerebral cortex. Sleep is critical for the consolidation of hippocampal memory of wake experiences into the neocortex. Understanding representations of neural codes of hippocampal-neocortical networks during sleep would reveal important circuit mechanisms in memory consolidation and provide novel insights into memory and dreams. Although sleep-associated ensemble spike activity has been investigated, identifying the content of memory in sleep remains challenging. Here we revisit important experimental findings on sleep-associated memory (i.e., neural activity patterns in sleep that reflect memory processing) and review computational approaches to the analysis of sleep-associated neural codes (SANCs). We focus on two analysis paradigms for sleep-associated memory and propose a new unsupervised learning framework ('memory first, meaning later') for unbiased assessment of SANCs.

State-Dependent Modulation of Slow Wave Motifs towards Awakening

Slow cortical waves that propagate across the cerebral cortex forming large-scale spatiotemporal propagation patterns are a hallmark of non-REM sleep and anesthesia, but also occur during resting wakefulness. To investigate how the spatial temporal properties of slow waves change with the depth of anesthetic, we optically imaged population voltage transients generated by mouse layer 2/3 pyramidal neurons across one or two cortical hemispheres dorsally with a genetically encoded voltage indicator (GEVI). From deep barbiturate anesthesia to light barbiturate sedation, depolarizing wave events recruiting at least 50% of the imaged cortical area consistently appeared as a conserved repertoire of distinct wave motifs. Toward awakening, the incidence of individual motifs changed systematically (the motif propagating from visual to motor areas increased while that from somatosensory to visual areas decreased) and both local and global cortical dynamics accelerated. These findings highlight that functional endogenous interactions between distant cortical areas are not only constrained by anatomical connectivity, but can also be modulated by the brain state.

Different Effects of Sleep Deprivation and Torpor on EEG Slow-Wave Characteristics in Djungarian Hamsters

It has been shown previously in Djungarian hamsters that the initial electroencephalography (EEG) slow-wave activity (power in the 0.5–4.0 Hz band; SWA) in non-rapid eye movement (NREM) sleep following an episode of daily torpor is consistently enhanced, similar to the SWA increase after sleep deprivation (SD). However, it is unknown whether the network mechanisms underlying the SWA increase after torpor and SD are similar. EEG slow waves recorded in the neocortex during sleep reflect synchronized transitions between periods of activity and silence among large neuronal populations. We therefore set out to investigate characteristics of individual cortical EEG slow waves recorded during NREM sleep after 4 h SD and during sleep after emergence from an episode of daily torpor in adult male Djungarian hamsters. We found that during the first hour after both SD and torpor, the SWA increase was associated with an increase in slow-wave incidence and amplitude. However, the slopes of single slow waves during NREM sleep were steeper in the first hour after SD but not after torpor, and, in contrast to sleep after SD, the magnitude of change in slopes after torpor was unrelated to the changes in SWA. Furthermore, slow-wave slopes decreased progressively within the first 2 h after SD, while a progressive increase in slow-wave slopes was apparent during the first 2 h after torpor. The data suggest that prolonged waking and torpor have different effects on cortical network activity underlying slow-wave characteristics, while resulting in a similar homeostatic sleep response of SWA. We suggest that sleep plays an important role in network homeostasis after both waking and torpor, consistent with a recovery function for both states.

Promising techniques to illuminate neuromodulatory control of the cerebral cortex in sleeping and waking states

Sleep, a common event in daily life, has clear benefits for brain function, but what goes on in the brain when we sleep remains unclear. Sleep was long regarded as a silent state of the brain because the brain seemingly lacks interaction with the surroundings during sleep. Since the discovery of electrical activities in the brain at rest, electrophysiological methods have revealed novel concepts in sleep research. During sleep, the brain generates oscillatory activities that represent characteristic states of sleep. In addition to electrophysiology, opto/chemogenetics and two-photon Ca²⁺ imaging methods have clarified that the sleep/wake states organized by neuronal and glial ensembles in the cerebral cortex are transitioned by neuromodulators. Even with these methods, however, it is extremely difficult to elucidate how and when neuromodulators spread, accumulate, and disappear in the extracellular space of the cortex. Thus, real-time monitoring of neuromodulator dynamics at high spatiotemporal resolution is required for further understanding of sleep. Toward direct detection of neuromodulator behavior during sleep and wakefulness, in this review, we discuss developing imaging techniques based on the activation of G-protein-coupled receptors that allow for visualization of neuromodulator dynamics.

The neural correlates of dreaming

Consciousness never fades during wake. However, if awakened from sleep, sometimes we report dreams and sometimes no experiences. Traditionally, dreaming has been identified with REM sleep, characterized by a wake-like, globally ‘activated’, high-frequency EEG. However, dreaming also occurs in NREM sleep, characterized by prominent low-frequency activity. This challenges our understanding of the neural correlates of conscious experiences in sleep. Using high-density EEG, we contrasted the presence and absence of dreaming within NREM and REM sleep. In both NREM and REM sleep, reports of dream experience were associated with a local decrease in low-frequency activity in posterior cortical regions. High-frequency activity within these regions correlated with specific dream contents. Monitoring this posterior ‘hot zone’ predicted whether an individual reported dreaming or the absence of experiences during NREM sleep in real time, suggesting that it may constitute a core correlate of conscious experiences in sleep.

Sleep-based memory processing facilitates grammatical generalization: Evidence from targeted memory reactivation

Generalization-the ability to abstract regularities from specific examples and apply them to novel instances-is an essential component of language acquisition. Generalization not only depends on exposure to input during wake, but may also improve offline during sleep. Here we examined whether targeted memory reactivation during sleep can influence grammatical generalization. Participants gradually acquired the grammatical rules of an artificial language through an interactive learning procedure. Then, phrases from the language (experimental group) or stimuli from an unrelated task (control group) were covertly presented during an afternoon nap. Compared to control participants, participants re-exposed to the language during sleep showed larger gains in grammatical generalization. Sleep cues produced a bias, not necessarily a pure gain, suggesting that the capacity for memory replay during sleep is limited. We conclude that grammatical generalization was biased by auditory cueing during sleep, and by extension, that sleep likely influences grammatical generalization in general.

Behavioral state classification in epileptic brain using intracranial electrophysiology

OBJECTIVE

Automated behavioral state classification can benefit next generation implantable epilepsy devices. In this study we explored the feasibility of automated awake (AW) and slow wave sleep (SWS) classification using wide bandwidth intracranial EEG (iEEG) in patients undergoing evaluation for epilepsy surgery.

APPROACH

Data from seven patients (age [Formula: see text], 4 women) who underwent intracranial depth electrode implantation for iEEG monitoring were included. Spectral power features (0.1-600 Hz) spanning several frequency bands from a single electrode were used to train and test a support vector machine classifier.

MAIN RESULTS

Classification accuracy of 97.8  ±  0.3% (normal tissue) and 89.4  ±  0.8% (epileptic tissue) across seven subjects using multiple spectral power features from a single electrode was achieved. Spectral power features from electrodes placed in normal temporal neocortex were found to be more useful (accuracy 90.8  ±  0.8%) for sleep-wake state classification than electrodes located in normal hippocampus (87.1  ±  1.6%). Spectral power in high frequency band features (Ripple (80-250 Hz), Fast Ripple (250-600 Hz)) showed comparable performance for AW and SWS classification as the best performing Berger bands (Alpha, Beta, low Gamma) with accuracy  ⩾90% using a single electrode contact and single spectral feature.

SIGNIFICANCE

Automated classification of wake and SWS should prove useful for future implantable epilepsy devices with limited computational power, memory, and number of electrodes. Applications include quantifying patient sleep patterns and behavioral state dependent detection, prediction, and electrical stimulation therapies.

Amyloid-β-Induced Changes in Molecular Clock Properties and Cellular Bioenergetics

Ageing is an inevitable biological process that results in a progressive structural and functional decline, as well as biochemical alterations that altogether lead to reduced ability to adapt to environmental changes. As clock oscillations and clock-controlled rhythms are not resilient to the aging process, aging of the circadian system may also increase susceptibility to age-related pathologies such as Alzheimer's disease (AD). Besides the amyloid-beta protein (Aβ)-induced metabolic decline and neuronal toxicity in AD, numerous studies have demonstrated that the disruption of sleep and circadian rhythms is one of the common and earliest signs of the disease. In this study, we addressed the questions of whether Aβ contributes to an abnormal molecular circadian clock leading to a bioenergetic imbalance. For this purpose, we used different oscillator cellular models: human skin fibroblasts, human glioma cells, as well as mouse primary cortical and hippocampal neurons. We first evaluated the circadian period length, a molecular clock property, in the presence of different Aβ species. We report here that physiologically relevant Aβ_1–42 concentrations ranging from 10 to 500 nM induced an increase of the period length in human skin fibroblasts, human A172 glioma cells as well as in mouse primary neurons whereas the reverse control peptide Aβ_42-1, which is devoid of toxic action, did not influence the circadian period length within the same concentration range. To better understand the underlying mechanisms that are involved in the Aβ-related alterations of the circadian clock, we examined the cellular metabolic state in the human primary skin fibroblast model. Notably, under normal conditions, ATP levels displayed circadian oscillations, which correspond to the respective circadian pattern of mitochondrial respiration. In contrast, Aβ_1–42 treatment provoked a strong dampening in the metabolic oscillations of ATP levels as well as mitochondrial respiration and in addition, induced an increased oxidized state. Overall, we gain here new insights into the deleterious cycle involved in Aβ-induced decay of the circadian rhythms leading to metabolic deficits, which may contribute to the failure in mitochondrial energy metabolism associated with the pathogenesis of AD.

Neural mechanisms of vocal imitation: The role of sleep replay in shaping mirror neurons

Learning by imitation involves not only perceiving another individual's action to copy it, but also the formation of a memory trace in order to gradually establish a correspondence between the sensory and motor codes, which represent this action through sensorimotor experience. Memory and sensorimotor processes are closely intertwined. Mirror neurons, which fire both when the same action is performed or perceived, have received considerable attention in the context of imitation. An influential view of memory processes considers that the consolidation of newly acquired information or skills involves an active offline reprocessing of memories during sleep within the neuronal networks that were initially used for encoding. Here, we review the recent advances in the field of mirror neurons and offline processes in the songbird. We further propose a theoretical framework that could establish the neurobiological foundations of sensorimotor learning by imitation. We propose that the reactivation of neuronal assemblies during offline periods contributes to the integration of sensory feedback information and the establishment of sensorimotor mirroring activity at the neuronal level.

Acoustic Enhancement of Sleep Slow Oscillations and Concomitant Memory Improvement in Older Adults

Acoustic stimulation methods applied during sleep in young adults can increase slow wave activity (SWA) and improve sleep-dependent memory retention. It is unknown whether this approach enhances SWA and memory in older adults, who generally have reduced SWA compared to younger adults. Additionally, older adults are at risk for age-related cognitive impairment and therefore may benefit from non-invasive interventions. The aim of this study was to determine if acoustic stimulation can increase SWA and improve declarative memory in healthy older adults. Thirteen participants 60–84 years old completed one night of acoustic stimulation and one night of sham stimulation in random order. During sleep, a real-time algorithm using an adaptive phase-locked loop modeled the phase of endogenous slow waves in midline frontopolar electroencephalographic recordings. Pulses of pink noise were delivered when the upstate of the slow wave was predicted. Each interval of five pulses (“ON interval”) was followed by a pause of approximately equal length (“OFF interval”). SWA during the entire sleep period was similar between stimulation and sham conditions, whereas SWA and spindle activity were increased during ON intervals compared to matched periods during the sham night. The increases in SWA and spindle activity were sustained across almost the entire five-pulse ON interval compared to matched sham periods. Verbal paired-associate memory was tested before and after sleep. Overnight improvement in word recall was significantly greater with acoustic stimulation compared to sham and was correlated with changes in SWA between ON and OFF intervals. Using the phase-locked-loop method to precisely target acoustic stimulation to the upstate of sleep slow oscillations, we were able to enhance SWA and improve sleep-dependent memory storage in older adults, which strengthens the theoretical link between sleep and age-related memory integrity.

Neuronal substrates for initiation, maintenance, and structural organization of sleep/wake states

Animals continuously alternate between sleep and wake states throughout their life. The daily organization of sleep and wakefulness is orchestrated by circadian, homeostatic, and motivational processes. Over the last decades, much progress has been made toward determining the neuronal populations involved in sleep/wake regulation. Here, we will discuss how the application of advanced in vivo tools for cell type–specific manipulations now permits the functional interrogation of different features of sleep/wake state regulation: initiation, maintenance, and structural organization. We will specifically focus on recent studies examining the roles of wake-promoting neuronal populations.

Differences in electroencephalographic non-rapid-eye movement sleep slow-wave characteristics between young and old mice

Changes in sleep pattern are typical for the normal aging process. However, aged mice show an increase in the amount of sleep, whereas humans show a decrease when aging. Mice are considered an important model in aging studies, and this divergence warrants further investigation. Recently, insights into the network dynamics of cortical activity during sleep were obtained by investigating characteristics of individual electroencephalogram (EEG) slow waves in young and elderly humans. In this study, we investigated, for the first time, the parameters of EEG slow waves, including their incidence, amplitude, duration and slopes, in young (6 months) and older (18-24 months) C57BL/6J mice during undisturbed 24 h, and after a 6-h sleep deprivation (SD). As expected, older mice slept more but, in contrast to humans, absolute NREM sleep EEG slow-wave activity (SWA, spectral power density between 0.5-4 Hz) was higher in the older mice, as compared to the young controls. Furthermore, slow waves in the older mice were characterized by increased amplitude, steeper slopes and fewer multipeak waves, indicating increased synchronization of cortical neurons in aging, opposite to what was found in humans. Our results suggest that older mice, in contrast to elderly humans, live under a high sleep pressure.

Automated selective disruption of slow wave sleep

BACKGROUND

Slow wave sleep (SWS) plays an important role in neurophysiologic restoration. Experimentally testing the effect of SWS disruption previously required highly time-intensive and subjective methods. Our goal was to develop an automated and objective protocol to reduce SWS without affecting sleep architecture.

NEW METHOD

We developed a custom Matlab™ protocol to calculate electroencephalogram spectral power every 10s live during a polysomnogram, exclude artifact, and, if measurements met criteria for SWS, deliver increasingly louder tones through earphones. Middle-aged healthy volunteers (n=10) each underwent 2 polysomnograms, one with the SWS disruption protocol and one with sham condition.

RESULTS

The SWS disruption protocol reduced SWS compared to sham condition, as measured by spectral power in the delta (0.5-4Hz) band, particularly in the 0.5-2Hz range (mean 20% decrease). A compensatory increase in the proportion of total spectral power in the theta (4-8Hz) and alpha (8-12Hz) bands was seen, but otherwise normal sleep features were preserved. N3 sleep decreased from 20±34 to 3±6min, otherwise there were no significant changes in total sleep time, sleep efficiency, or other macrostructural sleep characteristics.

COMPARISON WITH EXISTING METHOD

This novel SWS disruption protocol produces specific reductions in delta band power similar to existing methods, but has the advantage of being automated, such that SWS disruption can be performed easily in a highly standardized and operator-independent manner.

CONCLUSION

This automated SWS disruption protocol effectively reduces SWS without impacting overall sleep architecture.

The missing link between sleep disorders and age-related dementia: recent evidence and plausible mechanisms

Sleep disorders are among the most common clinical problems and possess a significant concern for the geriatric population. More importantly, while around 40% of elderly adults have sleep-related complaints, sleep disorders are more frequently associated with co-morbidities including age-related neurodegenerative diseases and mild cognitive impairment. Recently, increasing evidence has indicated that disturbed sleep may not only serve as the consequence of brain atrophy, but also contribute to the pathogenesis of dementia and, therefore, significantly increase dementia risk. Since the current therapeutic interventions lack efficacies to prevent, delay or reverse the pathological progress of dementia, a better understanding of underlying mechanisms by which sleep disorders interact with the pathogenesis of dementia will provide possible targets for the prevention and treatment of dementia. In this review, we briefly describe the physiological roles of sleep in learning/memory, and specifically update the recent research evidence demonstrating the association between sleep disorders and dementia. Plausible mechanisms are further discussed. Moreover, we also evaluate the possibility of sleep therapy as a potential intervention for dementia.