Regional slow waves and spindles in human sleep

EEG slow-wave coherence changes in propofol-induced general anesthesia: experiment and theory

The electroencephalogram (EEG) patterns recorded during general anesthetic-induced coma are closely similar to those seen during slow-wave sleep, the deepest stage of natural sleep; both states show patterns dominated by large amplitude slow waves. Slow oscillations are believed to be important for memory consolidation during natural sleep. Tracking the emergence of slow-wave oscillations during transition to unconsciousness may help us to identify drug-induced alterations of the underlying brain state, and provide insight into the mechanisms of general anesthesia. Although cellular-based mechanisms have been proposed, the origin of the slow oscillation has not yet been unambiguously established. A recent theoretical study by Steyn-Ross et al. (2013) proposes that the slow oscillation is a network, rather than cellular phenomenon. Modeling anesthesia as a moderate reduction in gap-junction interneuronal coupling, they predict an unconscious state signposted by emergent low-frequency oscillations with chaotic dynamics in space and time. They suggest that anesthetic slow-waves arise from a competitive interaction between symmetry-breaking instabilities in space (Turing) and time (Hopf), modulated by gap-junction coupling strength. A significant prediction of their model is that EEG phase coherence will decrease as the cortex transits from Turing-Hopf balance (wake) to Hopf-dominated chaotic slow-waves (unconsciousness). Here, we investigate changes in phase coherence during induction of general anesthesia. After examining 128-channel EEG traces recorded from five volunteers undergoing propofol anesthesia, we report a significant drop in sub-delta band (0.05-1.5 Hz) slow-wave coherence between frontal, occipital, and frontal-occipital electrode pairs, with the most pronounced wake-vs.-unconscious coherence changes occurring at the frontal cortex.

Enhancement of sleep slow waves: underlying mechanisms and practical consequences

Even modest sleep restriction, especially the loss of sleep slow wave activity (SWA), is invariably associated with slower electroencephalogram (EEG) activity during wake, the occurrence of local sleep in an otherwise awake brain, and impaired performance due to cognitive and memory deficits. Recent studies not only confirm the beneficial role of sleep in memory consolidation, but also point to a specific role for sleep slow waves. Thus, the implementation of methods to enhance sleep slow waves without unwanted arousals or lightening of sleep could have significant practical implications. Here we first review the evidence that it is possible to enhance sleep slow waves in humans using transcranial direct-current stimulation (tDCS) and transcranial magnetic stimulation. Since these methods are currently impractical and their safety is questionable, especially for chronic long-term exposure, we then discuss novel data suggesting that it is possible to enhance slow waves using sensory stimuli. We consider the physiology of the K-complex (KC), a peripheral evoked slow wave, and show that, among different sensory modalities, acoustic stimulation is the most effective in increasing the magnitude of slow waves, likely through the activation of non-lemniscal ascending pathways to the thalamo-cortical system. In addition, we discuss how intensity and frequency of the acoustic stimuli, as well as exact timing and pattern of stimulation, affect sleep enhancement. Finally, we discuss automated algorithms that read the EEG and, in real-time, adjust the stimulation parameters in a closed-loop manner to obtain an increase in sleep slow waves and avoid undesirable arousals. In conclusion, while discussing the mechanisms that underlie the generation of sleep slow waves, we review the converging evidence showing that acoustic stimulation is safe and represents an ideal tool for slow wave sleep (SWS) enhancement.

Brain state dependent activity in the cortex and thalamus

Cortical and thalamocortical activity is highly state dependent, varying between patterns that are conducive to accurate sensory-motor processing, to states in which the brain is largely off-line and generating internal rhythms irrespective of the outside world. The generation of rhythmic activity occurs through the interaction of stereotyped patterns of connectivity together with intrinsic membrane and synaptic properties. One common theme in the generation of rhythms is the interaction of a positive feedback loop (e.g., recurrent excitation) with negative feedback control (e.g., inhibition, adaptation, or synaptic depression). The operation of these state-dependent activities has wide ranging effects from enhancing or blocking sensory-motor processing to the generation of pathological rhythms associated with psychiatric or neurological disorders.

Sleep disturbances of adult women suffering from fibromyalgia: a systematic review of observational studies

Although sleep complaints are often reported in patients with fibromyalgia syndrome (FMS), there is no conclusive evidence that these complaints represent symptomatic disorders of sleep physiology. Thus, the question of the role of sleep disturbances as an etiological or maintenance factor in FMS remains open. This study identifies the subjective and objective characteristics of sleep disturbances in adult women diagnosed with FMS. We carried out a systematic review of publications since 1990, the publication year of the American College of Rheumatology criteria of FMS. We selected empirical studies comparing sleep characteristics of adult women with FMS and healthy women or women with rheumatic diseases. We identified 42 articles. Patients with FMS were more likely to exhibit sleep complaints and also a less efficient, lighter and fragmented sleep. The evidence of a FMS signature on objective measures of sleep is inconsistent, however, as the majority of studies lacks statistical power. Current evidence cannot confirm the role played by sleep physiology in the pathogenesis or maintenance of FMS symptoms; nonetheless, it is clear that sleep disturbances are present in this syndrome.

Tracking the sleep onset process: an empirical model of behavioral and physiological dynamics

The sleep onset process (SOP) is a dynamic process correlated with a multitude of behavioral and physiological markers. A principled analysis of the SOP can serve as a foundation for answering questions of fundamental importance in basic neuroscience and sleep medicine. Unfortunately, current methods for analyzing the SOP fail to account for the overwhelming evidence that the wake/sleep transition is governed by continuous, dynamic physiological processes. Instead, current practices coarsely discretize sleep both in terms of state, where it is viewed as a binary (wake or sleep) process, and in time, where it is viewed as a single time point derived from subjectively scored stages in 30-second epochs, effectively eliminating SOP dynamics from the analysis. These methods also fail to integrate information from both behavioral and physiological data. It is thus imperative to resolve the mismatch between the physiological evidence and analysis methodologies. In this paper, we develop a statistically and physiologically principled dynamic framework and empirical SOP model, combining simultaneously-recorded physiological measurements with behavioral data from a novel breathing task requiring no arousing external sensory stimuli. We fit the model using data from healthy subjects, and estimate the instantaneous probability that a subject is awake during the SOP. The model successfully tracked physiological and behavioral dynamics for individual nights, and significantly outperformed the instantaneous transition models implicit in clinical definitions of sleep onset. Our framework also provides a principled means for cross-subject data alignment as a function of wake probability, allowing us to characterize and compare SOP dynamics across different populations. This analysis enabled us to quantitatively compare the EEG of subjects showing reduced alpha power with the remaining subjects at identical response probabilities. Thus, by incorporating both physiological and behavioral dynamics into our model framework, the dynamics of our analyses can finally match those observed during the SOP.

Two distinct synchronization processes in the transition to sleep: a high-density electroencephalographic study

OBJECTIVES

To assess how the characteristics of slow waves and spindles change in the falling-asleep process.

DESIGN

Participants undergoing overnight high-density electroencephalographic recordings were awakened at 15- to 30-min intervals. One hundred forty-one falling-asleep periods were analyzed at the scalp and source level.

SETTING

Sleep laboratory.

PARTICIPANTS

Six healthy participants.

INTERVENTIONS

Serial awakenings.

RESULTS

The number and amplitude of slow waves followed two dissociated, intersecting courses during the transition to sleep: slow wave number increased slowly at the beginning and rapidly at the end of the falling-asleep period, whereas amplitude at first increased rapidly and then decreased linearly. Most slow waves occurring early in the transition to sleep had a large amplitude, a steep slope, involved broad regions of the cortex, predominated over frontomedial regions, and preferentially originated from the sensorimotor and the posteromedial parietal cortex. Most slow waves occurring later had a smaller amplitude and slope, involved more circumscribed parts of the cortex, and had more evenly distributed origins. Spindles were initially sparse, fast, and involved few cortical regions, then became more numerous and slower, and involved more areas.

CONCLUSIONS

Our results provide evidence for two types of slow waves, which follow dissociated temporal courses in the transition to sleep and have distinct cortical origins and distributions. We hypothesize that these two types of slow waves result from two distinct synchronization processes: (1) a "bottom-up," subcorticocortical, arousal system-dependent process that predominates in the early phase and leads to type I slow waves, and (2) a "horizontal," corticocortical synchronization process that predominates in the late phase and leads to type II slow waves. The dissociation between these two synchronization processes in time and space suggests that they may be differentially affected by experimental manipulations and sleep disorders.

Synchronization of isolated downstates (K-complexes) may be caused by cortically-induced disruption of thalamic spindling

Sleep spindles and K-complexes (KCs) define stage 2 NREM sleep (N2) in humans. We recently showed that KCs are isolated downstates characterized by widespread cortical silence. We demonstrate here that KCs can be quasi-synchronous across scalp EEG and across much of the cortex using electrocorticography (ECOG) and localized transcortical recordings (bipolar SEEG). We examine the mechanism of synchronous KC production by creating the first conductance based thalamocortical network model of N2 sleep to generate both spontaneous spindles and KCs. Spontaneous KCs are only observed when the model includes diffuse projections from restricted prefrontal areas to the thalamic reticular nucleus (RE), consistent with recent anatomical findings in rhesus monkeys. Modeled KCs begin with a spontaneous focal depolarization of the prefrontal neurons, followed by depolarization of the RE. Surprisingly, the RE depolarization leads to decreased firing due to disrupted spindling, which in turn is due to depolarization-induced inactivation of the low-threshold Ca²⁺ current (I_T). Further, although the RE inhibits thalamocortical (TC) neurons, decreased RE firing causes decreased TC cell firing, again because of disrupted spindling. The resulting abrupt removal of excitatory input to cortical pyramidal neurons then leads to the downstate. Empirically, KCs may also be evoked by sensory stimuli while maintaining sleep. We reproduce this phenomenon in the model by depolarization of either the RE or the widely-projecting prefrontal neurons. Again, disruption of thalamic spindling plays a key role. Higher levels of RE stimulation also cause downstates, but by directly inhibiting the TC neurons. SEEG recordings from the thalamus and cortex in a single patient demonstrated the model prediction that thalamic spindling significantly decreases before KC onset. In conclusion, we show empirically that KCs can be widespread quasi-synchronous cortical downstates, and demonstrate with the first model of stage 2 NREM sleep a possible mechanism whereby this widespread synchrony may arise.

EEG in the most common stage of human sleep is dominated by K-complexes (KCs) and sleep spindles (bursts of 10–14 Hz oscillations) occupying the thalamus and cortex. Recently, we discovered that KCs are brief moments when the cortex becomes almost completely silent. Here, using recordings directly from the cortex of epileptic patients, we show that KCs can be quasi-synchronous across widespread cortical areas, and ask what mechanism could produce such a phenomenon. We created the first network model of realistic cortical and thalamic neurons, which spontaneously generate KCs as well as sleep spindles. We showed that the membrane channels in the reticular nucleus of the thalamus can be inactivated by excitatory inputs from the cortex, and this disrupts the spindle-generating network, which can trigger widespread cortical silence. The model prediction that thalamic spindle disruption occurs prior to KC was then observed in simultaneous recordings from the human thalamus and cortex. Understanding the cellular and network mechanisms whereby KCs arise is crucial to understanding its roles in maintaining sleep and consolidating memories.

Cortical-subcortical interactions in hypersomnia disorders: mechanisms underlying cognitive and behavioral aspects of the sleep-wake cycle

Subcortical circuits mediating sleep–wake functions have been well characterized in animal models, and corroborated by more recent human studies. Disruptions in these circuits have been identified in hypersomnia disorders (HDs) such as narcolepsy and Kleine–Levin Syndrome, as well as in neurodegenerative disorders expressing excessive daytime sleepiness. However, the behavioral expression of sleep–wake functions is not a simple on-or-off state determined by subcortical circuits, but encompasses a complex range of behaviors determined by the interaction between cortical networks and subcortical circuits. While conceived as disorders of sleep, HDs are equally disorders of wake, representing a fundamental instability in neural state characterized by lapses of alertness during wake. These episodic lapses in alertness and wakefulness are also frequently seen in neurodegenerative disorders where electroencephalogram demonstrates abnormal function in cortical regions associated with cognitive fluctuations (CFs). Moreover, functional connectivity MRI shows instability of cortical networks in individuals with CFs. We propose that the inability to stabilize neural state due to disruptions in the sleep–wake control networks is common to the sleep and cognitive dysfunctions seen in hypersomnia and neurodegenerative disorders.

Global intracellular slow-wave dynamics of the thalamocortical system

It is widely accepted that corticothalamic neurons recruit the thalamus in slow oscillation, but global slow-wave thalamocortical dynamics have never been experimentally shown. We analyzed intracellular activities of neurons either from different cortical areas or from a variety of specific and nonspecific thalamic nuclei in relation to the phase of global EEG signal in ketamine-xylazine anesthetized mice. We found that, on average, slow-wave active states started off within frontal cortical areas as well as higher-order and intralaminar thalamus (posterior and parafascicular nuclei) simultaneously. Then, the leading edge of active states propagated in the anteroposterior/lateral direction over the cortex at ∼40 mm/s. The latest structure we recorded within the slow-wave cycle was the anterior thalamus, which followed active states of the retrosplenial cortex. Active states from different cortical areas tended to terminate simultaneously. Sensory thalamic ventral posterior medial and lateral geniculate nuclei followed cortical active states with major inhibitory and weak tonic-like "modulator" EPSPs. In these nuclei, sharp-rising, large-amplitude EPSPs ("drivers") were not modulated by cortical slow waves, suggesting their origin in ascending pathways. The thalamic active states in other investigated nuclei were composed of depolarization: some revealing "driver"- and "modulator"-like EPSPs, others showing "modulator"-like EPSPs only. We conclude that sensory thalamic nuclei follow the propagating cortical waves, whereas neurons from higher-order thalamic nuclei display "hub dynamics" and thus may contribute to the generation of cortical slow waves.

The dynamics of cortical neuronal activity in the first minutes after spontaneous awakening in rats and mice

STUDY OBJECTIVE

Upon awakening from sleep, a fully awake brain state is not reestablished immediately, but the origin and physiological properties of the distinct brain state during the first min after awakening are unclear. To investigate whether neuronal firing immediately upon arousal is different from the remaining part of the waking episode, we recorded and analyzed the dynamics of cortical neuronal activity in the first 15 min after spontaneous awakenings in freely moving rats and mice.

DESIGN

Intracortical recordings of the local field potential and neuronal activity in freely-moving mice and rats.

SETTING

Basic sleep research laboratory.

PATIENTS OR PARTICIPANTS

WKY adult male rats, C57BL/6 adult male mice.

INTERVENTIONS

N/A.

MEASUREMENTS AND RESULTS

In both species the average population spiking activity upon arousal was initially low, though substantial variability in the dynamics of firing activity was apparent between individual neurons. A distinct population of neurons was found that was virtually silent in the first min upon awakening. The overall lower population spiking initially after awakening was associated with the occurrence of brief periods of generalized neuronal silence (OFF periods), whose frequency peaked immediately after awakening and then progressively declined. OFF periods incidence upon awakening was independent of ongoing locomotor activity but was sensitive to immediate preceding sleep/wake history. Notably, in both rats and mice if sleep before a waking episode was enriched in rapid eye movement sleep, the incidence of OFF periods was initially higher as compared to those waking episodes preceded mainly by nonrapid eye movement sleep.

CONCLUSION

We speculate that an intrusion of sleep-like patterns of cortical neuronal activity into the wake state immediately after awakening may account for some of the changes in the behavior and cognitive function typical of what is referred to as sleep inertia.

CITATION

Vyazovskiy VV, Cui N, Rodriguez AV, Funk C, Cirelli C, Tononi G. The dynamics of cortical neuronal activity in the first minutes after spontaneous awakening in rats and mice.

Sound asleep: processing and retention of slow oscillation phase-targeted stimuli

The sleeping brain retains some residual information processing capacity. Although direct evidence is scarce, a substantial literature suggests the phase of slow oscillations during deep sleep to be an important determinant for stimulus processing. Here, we introduce an algorithm for predicting slow oscillations in real-time. Using this approach to present stimuli directed at both oscillatory up and down states, we show neural stimulus processing depends importantly on the slow oscillation phase. During ensuing wakefulness, however, we did not observe differential brain or behavioral responses to these stimulus categories, suggesting no enduring memories were formed. We speculate that while simpler forms of learning may occur during sleep, neocortically based memories are not readily established during deep sleep.

Electrical source imaging of sleep spindles

To identify and compare cortical source generators of slow and fast sleep spindles in healthy subjects, electroencephalographic (EEG) signals were obtained from 256 channels, and sources on neuroanatomical Montreal Neurological Institute (MNI) space estimated with low-resolution brain electromagnetic tomography analysis (LORETA). Spindle activity was recorded in 18 healthy volunteers during daytime napping. Because of lack of sleep or excessive artifacts, data from 13 subjects were analyzed off-line. Spindles were visually scored, marked, and bandpass filtered (slow 10-12 Hz or fast 12-14 Hz). EEG was segmented on the marker, and segments separately averaged. LORETA projected cortical sources on the MNI brain. Maximal intra- and inter-individual intensities were compared using the Wilcoxon test (P < .05) and cortical sources distribution compared using a chi2 test. Two to three slow spindles generators were consistently identified in frontal lobes, with additional sources in parietal and limbic lobes in half cases. Fast spindles had multiple temporo-parietal sources, with an inconstant frontal source. Inter-individual (P = 0.44), and intra-individual (P = 0.09 slow and P = 0.10 fast spindles) source intensities were comparable. Slow spindles sources were preferentially concentrated over frontal cortices in comparison with fast spindles (P = 0.0009). Our results demonstrate multiple, synchronous, and equipotent spindles cortical generators in healthy subjects, with more anterior generators for slow spindles.

When does this cortical area drop off? Principal component structuring of the EEG spectrum yields yes-or-no criteria of local sleep onset

The traditional sleep scoring approach has been invented long before the recognition of strictly local nature of the sleep process. It considers sleep as a whole-organism behavior state, and, thus, it cannot be used for identification of sleep onset in a separate brain region. Therefore, this paper was aimed on testing whether the practically useful, simple and reliable yes-or-no criterion of sleep onset in a particular cortical region might be developed through applying principal component analysis to the electroencephalographic (EEG) spectra. The resting EEG was recorded with 2-hour intervals throughout 43-61-hour prolongation of wakefulness, and during 12 20-minute attempts to nap in the course of 24-hour wakefulness (15 and 18 adults, respectively). The EEG power spectra were averaged on 1-min intervals of each resting EEG record and on 1-min intervals of each napping attempt, respectively. Since we earlier demonstrated that scores on the first and second principal components of the EEG spectrum exhibit dramatic changes during the sleep onset period, a zero-crossing buildup of the first score and a zero-crossing decline of the second score were examined as possible yes-or-no markers of regional sleep onsets. The results suggest that, irrespective of electrode location, sleep onset criterion and duration of preceding wakefulness, a highly significant zero-crossing decline of the second principal component score always occurred within 1-minute interval of transition from wakefulness to sleep. Therefore, it was concluded that such zero-crossing decline can serve as a reliable, simple, and practically useful yes-or-no marker of drop off event in a given cortical area.

The first-night effect suppresses the strength of slow-wave activity originating in the visual areas during sleep

Our visual system is plastic and adaptive in response to the stimuli and environments we experience. Although visual adaptation and plasticity have been extensively studied while participants are awake, little is known about what happens while they are asleep. It has been documented that sleep structure as measured by sleep stages using polysomnography is altered specifically in the first sleep session due to exposure to a new sleep environment, known as the first-night effect (FNE). However, the impact of the FNE on spontaneous oscillations in the visual system is poorly understood. How does the FNE affect the visual system during sleep? To address this question, the present study examined whether the FNE modifies the strength of slow-wave activity (SWA, 1-4Hz)-the dominant spontaneous brain oscillation in slow-wave sleep-in the visual areas. We measured the strength of SWA originating in the visual areas during the first and the second sleep sessions. Magnetoencephalography, polysomnography, and magnetic resonance imaging were used to localize the source of SWA to the visual areas. The visual areas were objectively defined using retinotopic mapping and an automated anatomical parcellation technique. The results showed that the strength of SWA was reduced in the first sleep session in comparison to the second sleep session, especially during slow-wave sleep, in the ventral part of the visual areas. These results suggest that environmental novelty may affect the visual system through suppression of SWA. The impact of the FNE may not be negligible in vision research.

Network-dependent modulation of brain activity during sleep

Brain activity dynamically changes even during sleep. A line of neuroimaging studies has reported changes in functional connectivity and regional activity across different sleep stages such as slow-wave sleep (SWS) and rapid-eye-movement (REM) sleep. However, it remains unclear whether and how the large-scale network activity of human brains changes within a given sleep stage. Here, we investigated modulation of network activity within sleep stages by applying the pairwise maximum entropy model to brain activity obtained by functional magnetic resonance imaging from sleeping healthy subjects. We found that the brain activity of individual brain regions and functional interactions between pairs of regions significantly increased in the default-mode network during SWS and decreased during REM sleep. In contrast, the network activity of the fronto-parietal and sensory-motor networks showed the opposite pattern. Furthermore, in the three networks, the amount of the activity changes throughout REM sleep was negatively correlated with that throughout SWS. The present findings suggest that the brain activity is dynamically modulated even in a sleep stage and that the pattern of modulation depends on the type of the large-scale brain networks.

Sleep-spindle detection: crowdsourcing and evaluating performance of experts, non-experts and automated methods

Sleep spindles are discrete, intermittent patterns of brain activity observed in human electroencephalographic data. Increasingly, these oscillations are of biological and clinical interest because of their role in development, learning and neurological disorders. We used an Internet interface to crowdsource spindle identification by human experts and non-experts, and we compared their performance with that of automated detection algorithms in data from middle- to older-aged subjects from the general population. We also refined methods for forming group consensus and evaluating the performance of event detectors in physiological data such as electroencephalographic recordings from polysomnography. Compared to the expert group consensus gold standard, the highest performance was by individual experts and the non-expert group consensus, followed by automated spindle detectors. This analysis showed that crowdsourcing the scoring of sleep data is an efficient method to collect large data sets, even for difficult tasks such as spindle identification. Further refinements to spindle detection algorithms are needed for middle- to older-aged subjects.

A transition in brain state during propofol-induced unconsciousness

Rhythmic oscillations shape cortical dynamics during active behavior, sleep, and general anesthesia. Cross-frequency phase-amplitude coupling is a prominent feature of cortical oscillations, but its role in organizing conscious and unconscious brain states is poorly understood. Using high-density EEG and intracranial electrocorticography during gradual induction of propofol general anesthesia in humans, we discovered a rapid drug-induced transition between distinct states with opposite phase-amplitude coupling and different cortical source distributions. One state occurs during unconsciousness and may be similar to sleep slow oscillations. A second state occurs at the loss or recovery of consciousness and resembles an enhanced slow cortical potential. These results provide objective electrophysiological landmarks of distinct unconscious brain states, and could be used to help improve EEG-based monitoring for general anesthesia.

NREM and REM Sleep: Complementary Roles in Recovery after Wakefulness

The overall function of sleep is hypothesized to provide "recovery" after preceding waking activities, thereby ensuring optimal functioning during subsequent wakefulness. However, the functional significance of the temporal dynamics of sleep, manifested in the slow homeostatic process and the alternation between non-rapid eye movement (NREM) and REM sleep remains unclear. We propose that NREM and REM sleep have distinct and complementary contributions to the overall function of sleep. Specifically, we suggest that cortical slow oscillations, occurring within specific functionally interconnected neuronal networks during NREM sleep, enable information processing, synaptic plasticity, and prophylactic cellular maintenance ("recovery process"). In turn, periodic excursions into an activated brain state-REM sleep-appear to be ideally placed to perform "selection" of brain networks, which have benefited from the process of "recovery," based on their offline performance. Such two-stage modus operandi of the sleep process would ensure that its functions are fulfilled according to the current need and in the shortest time possible. Our hypothesis accounts for the overall architecture of normal sleep and opens up new perspectives for understanding pathological conditions associated with abnormal sleep patterns.

Comparison of sleep spindles and theta oscillations in the hippocampus

Several network patterns allow for information exchange between the neocortex and the entorhinal-hippocampal complex, including theta oscillations and sleep spindles. How neurons are organized in these respective patterns is not well understood. We examined the cellular-synaptic generation of sleep spindles and theta oscillations in the waking rat and during rapid eye movement (REM) sleep by simultaneously recording local field and spikes in the regions and layers of the hippocampus and entorhinal cortex (EC). We show the following: (1) current source density analysis reveals that similar anatomical substrates underlie spindles and theta in the hippocampus, although the hippocampal subregions are more synchronized during spindles than theta; (2) the spiking of putative principal cells and interneurons in the CA1, CA3, and dentate gyrus subregions of the hippocampus, as well as layers 2, 3, and 5 of medial EC, are significantly phase locked to spindles detected in CA1; (3) the relationship between local field potential (LFP) phase and unit spiking differs between spindles and theta; (4) individual hippocampal principal cells generally do not fire in a rhythmic manner during spindles; (5) power in gamma (30-90 Hz) and epsilon (>90 Hz) bands of hippocampal LFP is modulated by the phase of spindle oscillations; and (6) unit firing rates during spindles were not significantly affected by whether spindles occurred during non-REM or transitions between non-REM and REM sleep. Thus, despite the similar current generator inputs and macroscopic appearance of the LFP, the organization of neuronal firing patterns during spindles bears little resemblance to that of theta oscillations.

Plumes of neuronal activity propagate in three dimensions through the nuclear avian brain

BACKGROUND

In mammals, the slow-oscillations of neuronal membrane potentials (reflected in the electroencephalogram as high-amplitude, slow-waves), which occur during non-rapid eye movement sleep and anesthesia, propagate across the neocortex largely as two-dimensional traveling waves. However, it remains unknown if the traveling nature of slow-waves is unique to the laminar cytoarchitecture and associated computational properties of the neocortex.

RESULTS

We demonstrate that local field potential slow-waves and correlated multiunit activity propagate as complex three-dimensional plumes of neuronal activity through the avian brain, owing to its non-laminar, nuclear neuronal cytoarchitecture.

CONCLUSIONS

The traveling nature of slow-waves is not dependent upon the laminar organization of the neocortex, and is unlikely to subserve functions unique to this pattern of neuronal organization. Finally, the three-dimensional geometry of propagating plumes may reflect computational properties not found in mammals that contributed to the evolution of nuclear neuronal organization and complex cognition in birds.

Hippocampal sleep spindles preceding neocortical sleep onset in humans

The coexistence of regionally dissociated brain activity patterns -with some brain areas being active while other already showing sleep signs- may occur throughout all vigilance states including the transition from wakefulness to sleep and may account for both physiological as well as pathological events. These dissociated electrophysiological states are often characterized by multi-domain cognitive and behavioral impairment such as amnesia for events immediately preceding sleep. By performing simultaneous intracerebral electroencephalographic recordings from hippocampal as well as from distributed neocortical sites in neurosurgical patients, we observed that sleep spindles consistently occurred in the hippocampus several minutes before sleep onset. In addition, hippocampal spindle detections consistently preceded neocortical events, with increasing delays along the cortical antero-posterior axis. Our results support the notion that wakefulness and sleep are not mutually exclusive states, but rather part of a continuum resulting from the complex interaction between diffuse neuromodulatory systems and intrinsic properties of the different thalamocortical modules. This interaction may account for the occurrence of dissociated activity across different brain structures characterizing both physiological and pathological conditions.

High-density electroencephalography as an innovative tool to explore sleep physiology and sleep related disorders

High density EEG represents a promising tool to achieve new insights regarding sleep physiology and pathology. It combines the advantages of an EEG technique as an optimal temporal resolution with the spatial resolution of the neuroimaging. So far its application in sleep research contributed to better characterize some of the peculiar microstructural figures of sleep such as spindles and K-complexes, and to understand the fundamental relationships between sleep and synaptic plasticity, learning and consciousness. Its application is not limited to neurophysiology, being recently also applied to study some sleep related psychiatric and neurological disorders such as depression, schizophrenia, attention-deficit hyperactivity disorder, and stroke. adding some interesting new pieces in the pathophysiological puzzle of these diseases. Due to its non-invasive, repetitive and reliable tempo-spatial resolution it is reasonable that the field of application of this tool will be soon enlarged to other areas of neuroscience. The present review aims to offer a complete overview regarding the use of high density EEG over the last decade in sleep research and sleep medicine, including its possible future perspective.

Sleep and the price of plasticity: from synaptic and cellular homeostasis to memory consolidation and integration

Sleep is universal, tightly regulated, and its loss impairs cognition. But why does the brain need to disconnect from the environment for hours every day? The synaptic homeostasis hypothesis (SHY) proposes that sleep is the price the brain pays for plasticity. During a waking episode, learning statistical regularities about the current environment requires strengthening connections throughout the brain. This increases cellular needs for energy and supplies, decreases signal-to-noise ratios, and saturates learning. During sleep, spontaneous activity renormalizes net synaptic strength and restores cellular homeostasis. Activity-dependent down-selection of synapses can also explain the benefits of sleep on memory acquisition, consolidation, and integration. This happens through the offline, comprehensive sampling of statistical regularities incorporated in neuronal circuits over a lifetime. This Perspective considers the rationale and evidence for SHY and points to open issues related to sleep and plasticity.

Sleep and synaptic homeostasis

In the last decades a substantial knowledge about sleep mechanisms has been accumulated. However, the function of sleep still remains elusive. The difficulty with unraveling sleep's function may arise from the lack of understanding of how the multitude of processes associated with waking and sleep-from gene expression and single neuron activity to the whole brain dynamics and behavior-functionally and mechanistically relate to each other. Therefore, novel conceptual frameworks, which integrate and take into account the variety of phenomena occurring during waking and sleep at different levels, will likely lead to advances in our understanding of the function of sleep, above and beyond what merely descriptive or correlative approaches can provide. One such framework, the synaptic homeostasis hypothesis, focuses on wake- and sleep-dependent changes in synaptic strength. The core claim of this hypothesis is that learning and experience during wakefulness are associated with a net increase in synaptic strength. In turn, the proposed function of sleep is to provide synaptic renormalization, which has important implications with respect to energy needs, intracranial space, metabolic supplies, and, importantly, enables further plastic changes. In this article we review the empirical evidence for this hypothesis, which was obtained at several levels-from gene expression and cellular excitability to structural synaptic modifications and behavioral outcomes. We conclude that although the mechanisms behind the proposed role of sleep in synaptic homeostasis are undoubtedly complex, this conceptual framework offers a unique opportunity to provide mechanistic and functional explanation for many previously disparate observations, and define future research strategies.

Scaling brain size, keeping timing: evolutionary preservation of brain rhythms

Despite the several-thousand-fold increase of brain volume during the course of mammalian evolution, the hierarchy of brain oscillations remains remarkably preserved, allowing for multiple-time-scale communication within and across neuronal networks at approximately the same speed, irrespective of brain size. Deployment of large-diameter axons of long-range neurons could be a key factor in the preserved time management in growing brains. We discuss the consequences of such preserved network constellation in mental disease, drug discovery, and interventional therapies.

Sleep and Alzheimer disease pathology--a bidirectional relationship

Factors other than age and genetics may increase the risk of developing Alzheimer disease (AD). Accumulation of the amyloid-β (Aβ) peptide in the brain seems to initiate a cascade of key events in the pathogenesis of AD. Moreover, evidence is emerging that the sleep-wake cycle directly influences levels of Aβ in the brain. In experimental models, sleep deprivation increases the concentration of soluble Aβ and results in chronic accumulation of Aβ, whereas sleep extension has the opposite effect. Furthermore, once Aβ accumulates, increased wakefulness and altered sleep patterns develop. Individuals with early Aβ deposition who still have normal cognitive function report sleep abnormalities, as do individuals with very mild dementia due to AD. Thus, sleep and neurodegenerative disease may influence each other in many ways that have important implications for the diagnosis and treatment of AD.

Essential thalamic contribution to slow waves of natural sleep

Slow waves represent one of the prominent EEG signatures of non-rapid eye movement (non-REM) sleep and are thought to play an important role in the cellular and network plasticity that occurs during this behavioral state. These slow waves of natural sleep are currently considered to be exclusively generated by intrinsic and synaptic mechanisms within neocortical territories, although a role for the thalamus in this key physiological rhythm has been suggested but never demonstrated. Combining neuronal ensemble recordings, microdialysis, and optogenetics, here we show that the block of the thalamic output to the neocortex markedly (up to 50%) decreases the frequency of slow waves recorded during non-REM sleep in freely moving, naturally sleeping-waking rats. A smaller volume of thalamic inactivation than during sleep is required for observing similar effects on EEG slow waves recorded during anesthesia, a condition in which both bursts and single action potentials of thalamocortical neurons are almost exclusively dependent on T-type calcium channels. Thalamic inactivation more strongly reduces spindles than slow waves during both anesthesia and natural sleep. Moreover, selective excitation of thalamocortical neurons strongly entrains EEG slow waves in a narrow frequency band (0.75-1.5 Hz) only when thalamic T-type calcium channels are functionally active. These results demonstrate that the thalamus finely tunes the frequency of slow waves during non-REM sleep and anesthesia, and thus provide the first conclusive evidence that a dynamic interplay of the neocortical and thalamic oscillators of slow waves is required for the full expression of this key physiological EEG rhythm.

Emergence of sensory patterns during sleep highlights differential dynamics of REM and non-REM sleep stages

Despite the profound reduction in conscious awareness associated with sleep, sensory cortex remains highly active during the different sleep stages, exhibiting complex interactions between different cortical sites. The potential functional significance of such spatial patterns and how they change between different sleep stages is presently unknown. In this electrocorticography study of human patients, we examined this question by studying spatial patterns of activity (broadband gamma power) that emerge during sleep (sleep patterns) and comparing them to the functional organization of sensory cortex that is activated by naturalistic stimuli during the awake state. Our results show a high correlation (p < 10(-4), permutation test) between the sleep spatial patterns and the functional organization found during wakefulness. Examining how the sleep patterns changed through the night highlighted a stage-specific difference, whereby the repertoire of such patterns was significantly larger during rapid eye movement (REM) sleep compared with non-REM stages. These results reveal that intricate spatial patterns of sensory functional organization emerge in a stage-specific manner during sleep.

Medroxyprogesterone acetate is associated with increased sleep spindles during non-rapid eye movement sleep in women referred for polysomnography

Sleep spindles are characteristic electroencephalographic waveforms that may play functionally significant roles in sleep-dependent memory consolidation, cortical development, and neuropsychiatric disorders. Circumstantial evidence has connected endogenous progesterone and its metabolites to the production of sleep spindles; however, the effects of exogenous progestins on sleep spindles have not been described in women. We examined differences in sleep spindle frequency and morphology in a clinical sample of women (n=21) referred for polysomnography taking depot medroxyprogesterone acetate (MPA), relative to a matched comparison group. Consistent with our hypotheses, women taking MPA demonstrated significantly higher sleep spindle density and maximal amplitude relative to comparison patients. Our results suggest that progestins potentiate the generation of sleep spindles, which may have significant implications for research that examines the role of these waveforms in learning, development, and mental illness.

Development of Brain EEG Connectivity across Early Childhood: Does Sleep Play a Role?

Sleep has beneficial effects on brain function and learning, which are reflected in plastic changes in the cortex. Early childhood is a time of rapid maturation in fundamental skills-e.g., language, cognitive control, working memory-that are predictive of future functioning. Little is currently known about the interactions between sleep and brain maturation during this developmental period. We propose coherent electroencephalogram (EEG) activity during sleep may provide unique insight into maturational processes of functional brain connectivity. Longitudinal sleep EEG assessments were performed in eight healthy subjects at ages 2, 3 and 5 years. Sleep EEG coherence increased across development in a region- and frequency-specific manner. Moreover, although connectivity primarily decreased intra-hemispherically across a night of sleep, an inter-hemispheric overnight increase occurred in the frequency range of slow waves (0.8-2 Hz), theta (4.8-7.8 Hz) and sleep spindles (10-14 Hz), with connectivity changes of up to 20% across a night of sleep. These findings indicate sleep EEG coherence reflects processes of brain maturation-i.e., programmed unfolding of neuronal networks-and moreover, sleep-related alterations of brain connectivity during the sensitive maturational window of early childhood.

How we fall asleep: regional and temporal differences in electroencephalographic synchronization at sleep onset

OBJECTIVES

We hypothesized that the brain shows specific and predictable patterns of spatial and temporal differences during sleep onset (SO) reflecting a temporal uncoupling of electrical activity between different cortical regions and a dissociated wakelike and sleeplike electrocortical activity in different cortical areas.

METHODS

We analyzed full-scalp electroencephalographic (EEG) recordings of 40 healthy subjects to investigate spatial and temporal changes of EEG activity across the wake-sleep transition. We quantified EEG sleep recordings by a fast Fourier transform (FFT) algorithm and by a better oscillation (BOSC) detection method to the EEG signals, which measured oscillatory activity within a signal containing a nonrhythmic portion.

RESULTS

The most representative spatial change at SO is the frontalization of slow-wave activity (SWA), while the θ activity, which mostly shares a similar temporal and spatial pattern with SWA, exhibits a temporo-occipital diffusion. The time course of these oscillations confirms that the changes of the dominant waves coexist with topographic changes. The waking occipital prevalence of α oscillations is progressively replaced by an occipital prevalence of θ oscillations. On the other hand, more anterior areas show a wide synchronization pattern mainly expressed by slow waves just below 4 Hz and by spindle oscillations.

CONCLUSIONS

The whole pattern of results confirms that the centrofrontal areas showed an earlier synchronization (i.e., they fall asleep first). This finding implies a coexistence of wakelike and sleeplike electrical activity during sleep in different cortical areas. It also implies that the process of progressive brain disconnection from the external world as we fall asleep does not necessarily affect primary and higher-order cortices at the same time.

Consciousness in humans and non-human animals: recent advances and future directions

This joint article reflects the authors' personal views regarding noteworthy advances in the neuroscience of consciousness in the last 10 years, and suggests what we feel may be promising future directions. It is based on a small conference at the Samoset Resort in Rockport, Maine, USA, in July of 2012, organized by the Mind Science Foundation of San Antonio, Texas. Here, we summarize recent advances in our understanding of subjectivity in humans and other animals, including empirical, applied, technical, and conceptual insights. These include the evidence for the importance of fronto-parietal connectivity and of “top-down” processes, both of which enable information to travel across distant cortical areas effectively, as well as numerous dissociations between consciousness and cognitive functions, such as attention, in humans. In addition, we describe the development of mental imagery paradigms, which made it possible to identify covert awareness in non-responsive subjects. Non-human animal consciousness research has also witnessed substantial advances on the specific role of cortical areas and higher order thalamus for consciousness, thanks to important technological enhancements. In addition, much progress has been made in the understanding of non-vertebrate cognition relevant to possible conscious states. Finally, major advances have been made in theories of consciousness, and also in their comparison with the available evidence. Along with reviewing these findings, each author suggests future avenues for research in their field of investigation.

Sleep-dependent synaptic down-selection (I): modeling the benefits of sleep on memory consolidation and integration

Sleep can favor the consolidation of both procedural and declarative memories, promote gist extraction, help the integration of new with old memories, and desaturate the ability to learn. It is often assumed that such beneficial effects are due to the reactivation of neural circuits in sleep to further strengthen the synapses modified during wake or transfer memories to different parts of the brain. A different possibility is that sleep may benefit memory not by further strengthening synapses, but rather by renormalizing synaptic strength to restore cellular homeostasis after net synaptic potentiation in wake. In this way, the sleep-dependent reactivation of neural circuits could result in the competitive down-selection of synapses that are activated infrequently and fit less well with the overall organization of memories. By using computer simulations, we show here that synaptic down-selection is in principle sufficient to explain the beneficial effects of sleep on the consolidation of procedural and declarative memories, on gist extraction, and on the integration of new with old memories, thereby addressing the plasticity-stability dilemma.

Age dependency of trauma-induced neocortical epileptogenesis

Trauma and brain infection are the primary sources of acquired epilepsy, which can occur at any age and may account for a high incidence of epilepsy in developing countries. We have explored the hypothesis that penetrating cortical wounds cause deafferentation of the neocortex, which triggers homeostatic plasticity and lead to epileptogenesis (Houweling etal., 2005). In partial deafferentation experiments of adult cats, acute seizures occurred in most preparations and chronic seizures occurred weeks to months after the operation in 65% of the animals (Nita etal., 2006,2007; Nita and Timofeev, 2007). Similar deafferentation of young cats (age 8-12 months) led to some acute seizures, but we never observed chronic seizure activity even though there was enhanced slow-wave activity in the partially deafferented hemisphere during quiet wakefulness. This suggests that despite a major trauma, the homeostatic plasticity in young animals was able to restore normal levels of cortical excitability, but in fully adult cats the mechanisms underlying homeostatic plasticity may lead to an unstable cortical state. To test this hypothesis we made an undercut in the cortex of an elderly cat. After several weeks this animal developed seizure activity. These observations may lead to an intervention after brain trauma that prevents epileptogenesis from occurring in adults.

Sleep Spindles: Where They Come From, What They Do

Sleep spindles are extensively studied electroencephalographic rhythms that recur periodically during non-rapid eye movement sleep and that are associated with rhythmic discharges of neurons throughout the thalamocortical system. Their occurrence thus constrains many aspects of the communication between thalamus and cortex, ranging from sensory transmission, to cortical plasticity and learning, to development and disease. I review these functional aspects in conjunction with novel findings on the cellular and molecular makeup of spindle-pacemaking circuits. A highlight in the search of roles for sleep spindles is the repeated finding that spindles correlate with memory consolidation in humans and animals. By illustrating that spindles are at the forefront understanding on how the brain might benefit from sleep rhythms, I hope to stimulate further experimentation.

Determination of neural state classification metrics from the power spectrum of human ECoG

Brain electrical activity exhibits scale-free dynamics that follow power law scaling. Previous works have shown that broadband spectral power exhibits state-dependent scaling with a log frequency exponent that systematically varies with neural state. However, the frequency ranges which best characterize biological state are not consistent across brain location or subject. An adaptive piecewise linear fitting solution was developed to extract features for classification of brain state. Performance was evaluated by comparison to an a posteriori based feature search method. This analysis, using the 1/ƒ characteristics of the human ECoG signal, demonstrates utility in advancing the ability to perform automated brain state discrimination.

Sleep, its regulation and possible mechanisms of sleep disturbances

The state of sleep consists of different phases that proceed in successive, tightly regulated order through the night forming a physiological program, which for each individual is different but stabile from one night to another. Failure to accomplish this program results in feeling of unrefreshing sleep and tiredness in the morning. The program core is constructed by genetic factors but regulated by circadian rhythm and duration and intensity of day time brain activity. Many environmental factors modulate sleep, including stress, health status and ingestion of vigilance-affecting nutrients or medicines (e.g. caffeine). Acute sleep loss results in compromised cognitive performance, memory deficits, depressive mood and involuntary sleep episodes during the day. Moreover, prolonged sleep curtailment has many adverse health effects, as evidenced by both epidemiological and experimental studies. These effects include increased risk for depression, type II diabetes, obesity and cardiovascular diseases. In addition to voluntary restriction of sleep, shift work, irregular working hours, jet lag and stress are important factors that induce curtailed or bad quality sleep and/or insomnia. This review covers the current theories on the function of normal sleep and describes current knowledge on the physiologic effects of sleep loss. It provides insights into the basic mechanisms of the regulation of wakefulness and sleep creating a theoretical background for understanding different disturbances of sleep.