The slow (<1 Hz) oscillation, along with its alternating UP and DOWN states in individual neurons, is a defining feature of the EEG during slow-wave sleep. Although this oscillation is well preserved across mammalian species, its physiological role remains unclear. Electrophysiological and computational evidence from cortex and thalamus now indicates that the slow oscillation UP states and the ‘activated’ state of wakefulness are remarkably similar dynamic entities. This is consistent with behavioural experiments suggesting that slow oscillation UP states provide a context for the replay and possible consolidation of previous experience. In this scenario, the T-type Ca2+ channel-dependent bursts of action potentials that initiate each UP state in thalamocortical neurons, might act as triggers for synaptic and cellular plasticity in corticothalamic networks.
Thalamocortical dynamics, the millisecond to second changes in activity of thalamocortical circuits, are central to perception, action and cognition. Generated by local circuitry and sculpted by neuromodulatory systems, these dynamics reflect the expression of vigilance states. In sleep, thalamocortical dynamics are thought to mediate “offline” functions including memory consolidation and synaptic scaling. Here, I discuss thalamocortical sleep dynamics and their modulation by the ascending arousal system and locally-released neurochemicals. I focus on modulation of these dynamics by electrically-silent astrocytes, highlighting the role of purinergic signaling in this glial form of communication. Astrocytes modulate cortical slow oscillations, sleep behavior, and sleep-dependent cognitive function. The discovery that astrocytes can modulate sleep dynamics and sleep-related behaviors suggests a new way of thinking about the brain, in which integrated circuits of neurons and glia control information processing and behavioral output.
sleep; thalamocortical circuitry; oscillations; memory; astrocytes; ATP; adenosine
The slow (<1 Hz) rhythm, the most significant EEG signature of non-rapid eye movement (NREM) sleep, is generally viewed as originating exclusively from neocortical networks. Here we argue that the full manifestation of this fundamental sleep oscillation within a corticothalamic module requires the dynamic interaction of three cardinal oscillators: a predominantly synaptically-based cortical oscillator and two intrinsic, conditional thalamic oscillators. The functional implications of this hypothesis are discussed in relation to other key EEG features of NREM sleep, with respect to coordinating activities in local and distant neuronal assemblies and in the context of facilitating cellular and network plasticity during slow wave sleep.
Rhythms at slow (<1 Hz) frequency of alternating Up and Down states occur during slow-wave sleep states, under deep anaesthesia and in cortical slices of mammals maintained in vitro. Such spontaneous oscillations result from the interplay between network reverberations nonlinearly sustained by a strong synaptic coupling and a fatigue mechanism inhibiting the neurons firing in an activity-dependent manner. Varying pharmacologically the excitability level of brain slices we exploit the network dynamics underlying slow rhythms, uncovering an intrinsic anticorrelation between Up and Down state durations. Besides, a non-monotonic change of Down state duration is also observed, which shrinks the distribution of the accessible frequencies of the slow rhythms. Attractor dynamics with activity-dependent self-inhibition predicts a similar trend even when the system excitability is reduced, because of a stability loss of Up and Down states. Hence, such cortical rhythms tend to display a maximal size of the distribution of Up/Down frequencies, envisaging the location of the system dynamics on a critical boundary of the parameter space. This would be an optimal solution for the system in order to display a wide spectrum of dynamical regimes and timescales.
Slow oscillations; Mean-field theory; IF neuron networks; Bifurcation analysis; Relaxation oscillators; Up and Down states; Cortical rhythms
Long-term plasticity contributes to memory formation and sleep plays a critical role in memory consolidation. However, it is unclear whether sleep slow oscillation by itself induces long-term plasticity that contributes to memory retention. Using in vivo pre-thalamic electrical stimulation at 1 Hz which itself does not induce immediate potentiation of evoked responses, we investigated how the cortical evoked response was modulated by different states of vigilance. We found that somatosensory evoked potentials during wake were enhanced after a slow-wave sleep episode (with or without stimulation during sleep) as compared to a previous wake episode. In vitro, we determined that this enhancement has a postsynaptic mechanism that is calcium-dependent, requires hyperpolarization periods (slow waves), and requires a co-activation of both AMPA and NMDA receptors. Our results suggest that long-term potentiation occurs during slow-wave sleep supporting its contribution to memory.
There are three major oscillations in thalamocortical systems during the state of sleep with synchronization of the electroencephalogram: 1. Spindles (7 Hz to 14 Hz) are generated in the thalamus at sleep onset and are blocked during arousal or rapid-eye-movement sleep by cholinergic systems that decouple the synchronizing network of the reticular thalamic nucleus. 2. Delta potentials (1 Hz to 4 Hz) appear during late stages of electroencephalogram-synchronized sleep. At the thalamic level they are produced by the interplay between two intrinsic currents of neurons with cortical projections. Delta rhythm is suppressed by cholinergic and noradrenergic systems. 3. A slow oscillation (< 1 Hz) is generated in the cerebral cortex and has a pivotal role in grouping the thalamic-generated sleep rhythms within wave-complexes recurring periodically, every two to five seconds. The slow rhythm is blocked by cholinergic and noradrenergic projections. Sleep rhythms consist of long-lasting inhibitory components that obliterate synaptic transmission and disconnect the brain from the outside world.
The cerebral cortex is permanently active during both awake and sleep states. This ongoing cortical activity has an impact on synaptic transmission and short-term plasticity. An activity pattern generated by the cortical network is a slow rhythmic activity that alternates up (active) and down (silent) states, a pattern occurring during slow wave sleep, anesthesia and even in vitro. Here we have studied 1) how network activity affects short term synaptic plasticity and, 2) how synaptic transmission varies in up versus down states.
Intracellular recordings obtained from cortex in vitro and in vivo were used to record synaptic potentials, while presynaptic activation was achieved either with electrical or natural stimulation. Repetitive activation of layer 4 to layer 2/3 synaptic connections from ferret visual cortex slices displayed synaptic augmentation that was larger and longer lasting in active than in silent slices. Paired-pulse facilitation was also significantly larger in an active network and it persisted for longer intervals (up to 200 ms) than in silent slices. Intracortical synaptic potentials occurring during up states in vitro increased their amplitude while paired-pulse facilitation disappeared. Both intracortical and thalamocortical synaptic potentials were also significantly larger in up than in down states in the cat visual cortex in vivo. These enhanced synaptic potentials did not further facilitate when pairs of stimuli were given, thus paired-pulse facilitation during up states in vivo was virtually absent. Visually induced synaptic responses displayed larger amplitudes when occurring during up versus down states. This was further tested in rat barrel cortex, where a sensory activated synaptic potential was also larger in up states.
These results imply that synaptic transmission in an active cortical network is more secure and efficient due to larger amplitude of synaptic potentials and lesser short term plasticity.
The most prominent EEG events in sleep are slow waves, reflecting a slow (<1 Hz) oscillation between up and down states in cortical neurons. It is unknown whether slow oscillations are synchronous across the majority or the minority of brain regions—are they a global or local phenomenon? To examine this, we recorded simultaneously scalp EEG, intracerebral EEG, and unit firing in multiple brain regions of neurosurgical patients. We find that most sleep slow waves and the underlying active and inactive neuronal states occur locally. Thus, especially in late sleep, some regions can be active while others are silent. We also find that slow waves can propagate, usually from medial prefrontal cortex to the medial temporal lobe and hippocampus. Sleep spindles, the other hallmark of NREM sleep EEG, are likewise predominantly local. Thus, intracerebral communication during sleep is constrained because slow and spindle oscillations often occur out-of-phase in different brain regions.
Sleep slow waves are the main phenomenon underlying NREM sleep. They are homeostatically regulated, they are thought to be linked to learning and plasticity processes and, at the same time, they are associated with marked changes in cortical information processing. Using transcranial magnetic stimulation (TMS) and high-density (hd) EEG we can measure slow waves, induce/measure plastic changes in the cerebral cortex and we can directly assess cortico-cortical information transmission. In this manuscript we review the results of recent experiments in which TMS/hd-EEG is used to demonstrate (i) a causal link between cortical plastic changes and sleep slow waves and (ii) a causal link between slow waves and the decreased ability of thalamocortical circuits to integrate information and to generate conscious experience during NREM sleep. The data presented here suggest a unifying mechanism linking slow waves, plasticity and cortical information integration; moreover, they suggest that TMS can be used as a non-pharmacological mean to controllably induce slow waves in the human cerebral cortex.
sleep homeostasis; synaptic plasticity; consciousness; slow oscillations; bistability
Activity-dependent dendritic Ca2+ signals play a critical role in multiple forms of non-linear cellular output and plasticity. In thalamocortical neurons, despite the well-established spatial separation of sensory and cortical inputs onto proximal and distal dendrites respectively, little is known about the spatio-temporal dynamics of intrinsic dendritic Ca2+ signalling during the different state-dependent firing patterns that are characteristic of these neurons. Here we demonstrate that T-type Ca2+ channels are expressed throughout the entire dendritic tree of rat thalamocortical neurons and that they mediate regenerative propagation of low threshold spikes, typical of, but not exclusive to sleep states, resulting in global dendritic Ca2+ influx. In contrast, actively backpropagating action potentials, typical of wakefulness, result in smaller Ca2+ influxes that can temporally summate to produce dendritic Ca2+ accumulations which are linearly related to firing frequency but spatially confined to proximal dendritic regions. Furthermore, dendritic Ca2+ transients evoked by both action potentials and low threshold spikes are shaped by Ca2+ uptake by sarco/endoplasmic reticulum Ca2+ ATP-ases, but do not rely upon Ca2+-induced Ca2+ release. Our data demonstrate that thalamocortical neurons are endowed with intrinsic dendritic Ca2+ signalling properties that are spatially and temporally modified in a behavioural state-dependent manner, and suggest that backpropagating action potentials faithfully inform proximal sensory but not distal corticothalamic synapses of neuronal output whereas corticothalamic synapses only “detect” Ca2+ signals associated with low threshold spikes.
thalamocortical; dendrites; T-type calcium channel; low threshold spike; action potential; calcium extrusion
During non-rapid eye movement (NREM) sleep and certain types of anaesthesia, neurons in the neocortex and thalamus exhibit a distinctive slow (<1 Hz) oscillation that consists of alternating UP and DOWN membrane potential states and which correlates with a pronounced slow (<1 Hz) rhythm in the EEG. Whilst several studies have claimed that the slow oscillation is generated exclusively in neocortical networks and then transmitted to other brain areas, substantial evidence exists to suggest that the full expression of the slow oscillation in an intact thalamocortical network requires the balanced interaction of oscillator systems in both the neocortex and thalamus. Within such a scenario, we have previously argued that the powerful low-threshold Ca2+ potential (LTCP)-mediated burst of action potentials that initiates the UP states in individual thalamocortical neurons may be a vital signal for instigating UP states in related cortical areas. To investigate these issues we constructed a computational model of the thalamocortical network which encompasses the important known aspects of the slow oscillation that have been garnered from earlier in vivo and in vitro experiments. By using this model we confirm that the overall expression of the slow oscillation is intricately reliant on intact connections between thalamus and cortex. In particular, we demonstrate that UP state-related LTCP-mediated bursts in thalamocortical neurons are proficient in triggering synchronous UP states in cortical networks, thereby bringing about a synchronous slow oscillation in the whole network. The importance of LTCP-mediated action potential bursts in the slow oscillation is also underlined by the observation that their associated dendritic Ca2+ signals are the only ones that inform corticothalamic synapses of the thalamocortical neuron output, since they, but not those elicited by tonic action potential firing, reach the distal dendritic sites where these synapses are located.
thalamic neurons; cortical neurons; probabilistic network model; dendrites; intrinsic calcium signalling
Neurons of the central nervous system display a broad spectrum of intrinsic electrophysiological properties that are absent in the traditional “integrate-and-fire” model. A network of neurons with these properties interacting through synaptic receptors with many time scales can produce complex patterns of activity that cannot be intuitively predicted. Computational methods, tightly linked to experimental data, provide insights into the dynamics of neural networks. We review this approach for the case of bursting neurons of the thalamus, with a focus on thalamic and thalamocortical slow-wave oscillations. At the single-cell level, intrinsic bursting or oscillations can be explained by interactions between calcium- and voltage-dependent channels. At the network level, the genesis of oscillations, their initiation, propagation, termination, and large-scale synchrony can be explained by interactions between neurons with a variety of intrinsic cellular properties through different types of synaptic receptors. These interactions can be altered by neuromodulators, which can dramatically shift the large-scale behavior of the network, and can also be disrupted in many ways, resulting in pathological patterns of activity, such as seizures. We suggest a coherent framework that accounts for a large body of experimental data at the ion-channel, single-cell, and network levels. This framework suggests physiological roles for the highly synchronized oscillations of slow-wave sleep.
EEG rhythms reflect the synchronized activity of underlying biological neuronal network oscillations, and certain predominant frequencies are typically linked to certain behavioral states. For instance, slow wave activity characterized by sleep slow oscillation (SO) emerges normally during slow-wave sleep (SWS). In this mini-review we will first give a background leading up to the present day association between specific oscillations and their functional relevance for learning and memory consolidation. Following, some principles on oscillatory activity are summarized and finally results of studies employing slowly oscillating transcranial electric stimulation are given. We underscore that oscillatory transcranial electric stimulation presents a tool to study principles of cortical network function.
tACS; tDCS; sleep; memory; learning; brain rhythms
Cortico-thalamo-cortical circuits mediate sensation and generate neural network oscillations associated with slow-wave sleep and various epilepsies. Cortical input to sensory thalamus is thought to mainly evoke feed-forward synaptic inhibition of thalamocortical (TC) cells via reticular thalamic nucleus (nRT) neurons, especially during oscillations. This relies on a stronger synaptic strength in the cortico-nRT pathway than in the cortico-TC pathway, allowing the feed-forward inhibition of TC cells to overcome direct cortico-TC excitation. We found a systemic and specific reduction in strength in GluA4-deficient (Gria4–/–) mice of one excitatory synapse of the rhythmogenic cortico-thalamo-cortical system, the cortico-nRT projection, and observed that the oscillations could still be initiated by cortical inputs via the cortico-TC-nRT-TC pathway. These results reveal a previously unknown mode of cortico-thalamo-cortical transmission, bypassing direct cortico-nRT excitation, and describe a mechanism for pathological oscillation generation. This mode could be active under other circumstances, representing a previously unknown channel of cortico-thalamo-cortical information processing.
The slow (<1 Hz) rhythm is an EEG hallmark of resting sleep. In thalamocortical (TC) neurons this rhythm correlates with a slow (<1 Hz) oscillation comprising recurring UP and DOWN membrane potential states. Recently, we showed that metabotropic glutamate receptor (mGluR) activation brings about an intrinsic slow oscillation in thalamocortical (TC) neurons of the cat dorsal lateral geniculate nucleus (LGN) in vitro which is identical to that observed in vivo. The aim of this study was to further assess the properties of this oscillation and compare them with those observed in TC neurons of three other thalamic nuclei in the cat (ventrobasal complex, VB; medial geniculate body, MGB; ventral lateral nucleus, VL) and two thalamic nuclei in rats and mice (LGN and VB). Slow oscillations were evident in all of these additional structures and shared several basic properties including, i) the stereotypical, rhythmic alternation between distinct UP and DOWN states with the UP state always commencing with a low-threshold Ca2+ potential (LTCP), and ii) an inverse relationship between frequency and injected current so that slow oscillations always increase in frequency with hyperpolarization, often culminating in delta (δ) activity at ~1-4 Hz. However, beyond these common properties there were important differences in expression between different nuclei. Most notably, 44% of slow oscillations in the cat LGN possessed UP states that comprised sustained tonic firing and/or high-threshold (HT) bursting. In contrast, slow oscillations in cat VB, MGB and VL TC neurons exhibited such UP states in only 16%, 11% and 10% of cases, respectively, whereas slow oscillations in the LGN and VB of rats and mice did so in <12% of cases. Thus, the slow oscillation is a common feature of TC neurons that displays clear species- and nuclei-related differences. The potential functional significance of these results is discussed.
EEG; delta waves; T-type calcium channels; metabotropic glutamate receptor
This review summarizes the brain mechanisms controlling sleep and wakefulness. Wakefulness promoting systems cause low-voltage, fast activity in the electroencephalogram (EEG). Multiple interacting neurotransmitter systems in the brain stem, hypothalamus, and basal forebrain converge onto common effector systems in the thalamus and cortex. Sleep results from the inhibition of wake-promoting systems by homeostatic sleep factors such as adenosine and nitric oxide and GABAergic neurons in the preoptic area of the hypothalamus, resulting in large-amplitude, slow EEG oscillations. Local, activity-dependent factors modulate the amplitude and frequency of cortical slow oscillations. Non-rapid-eye-movement (NREM) sleep results in conservation of brain energy and facilitates memory consolidation through the modulation of synaptic weights. Rapid-eye-movement (REM) sleep results from the interaction of brain stem cholinergic, aminergic, and GABAergic neurons which control the activity of glutamatergic reticular formation neurons leading to REM sleep phenomena such as muscle atonia, REMs, dreaming, and cortical activation. Strong activation of limbic regions during REM sleep suggests a role in regulation of emotion. Genetic studies suggest that brain mechanisms controlling waking and NREM sleep are strongly conserved throughout evolution, underscoring their enormous importance for brain function. Sleep disruption interferes with the normal restorative functions of NREM and REM sleep, resulting in disruptions of breathing and cardiovascular function, changes in emotional reactivity, and cognitive impairments in attention, memory, and decision making.
The 8–15 Hz thalamocortical oscillations known as sleep spindles are a universal feature of mammalian non-REM sleep, during which they are presumed to shape activity-dependent plasticity in neocortical networks. The cortex is hypothesized to contribute to initiation and termination of spindles, but the mechanisms by which it implements these roles are unknown. We used dual-site local field potential and multiple single-unit recordings in the thalamic reticular nucleus (TRN) and medial prefrontal cortex (mPFC) of freely behaving rats at rest to investigate thalamocortical network dynamics during natural sleep spindles. During each spindle epoch, oscillatory activity in mPFC and TRN increased in frequency from onset to offset, accompanied by a consistent phase precession of TRN spike times relative to the cortical oscillation. In mPFC, the firing probability of putative pyramidal cells was highest at spindle initiation and termination times. We thus identified “early” and “late” cell subpopulations and found that they had distinct properties: early cells generally fired in synchrony with TRN spikes, whereas late cells fired in antiphase to TRN activity and also had higher firing rates than early cells. The accelerating and highly structured temporal pattern of thalamocortical network activity over the course of spindles therefore reflects the engagement of distinct subnetworks at specific times across spindle epochs. We propose that early cortical cells serve a synchronizing role in the initiation and propagation of spindle activity, whereas the subsequent recruitment of late cells actively antagonizes the thalamic spindle generator by providing asynchronous feedback.
The need to sleep grows with the duration of wakefulness and dissipates with time spent asleep, a process called sleep homeostasis. What are the consequences of staying awake on brain cells, and why is sleep needed? Surprisingly, we do not know whether the firing of cortical neurons is affected by how long an animal has been awake or asleep. Here we found that after sustained wakefulness cortical neurons fire at higher frequencies in all behavioral states. During early NREM sleep after sustained wakefulness, periods of population activity (ON) are short, frequent, and associated with synchronous firing, while periods of neuronal silence are long and frequent. After sustained sleep, firing rates and synchrony decrease, while the duration of ON periods increases. Changes in firing patterns in NREM sleep correlate with changes in slow-wave-activity, a marker of sleep homeostasis. Thus, the systematic increase of firing during wakefulness is counterbalanced by staying asleep.
slow wave sleep; slow oscillations; EEG; rat; cerebral cortex; multi-unit recording
During non-rapid eye movement sleep and certain types of anaesthesia, neurons in the neocortex and thalamus exhibit a distinctive slow (<1 Hz) oscillation that consists of alternating UP and DOWN membrane potential states and which correlates with a pronounced slow (<1 Hz) rhythm in the electroencephalogram. While several studies have claimed that the slow oscillation is generated exclusively in neocortical networks and then transmitted to other brain areas, substantial evidence exists to suggest that the full expression of the slow oscillation in an intact thalamocortical (TC) network requires the balanced interaction of oscillator systems in both the neocortex and thalamus. Within such a scenario, we have previously argued that the powerful low-threshold Ca2+ potential (LTCP)-mediated burst of action potentials that initiates the UP states in individual TC neurons may be a vital signal for instigating UP states in related cortical areas. To investigate these issues we constructed a computational model of the TC network which encompasses the important known aspects of the slow oscillation that have been garnered from earlier in vivo and in vitro experiments. Using this model we confirm that the overall expression of the slow oscillation is intricately reliant on intact connections between the thalamus and the cortex. In particular, we demonstrate that UP state-related LTCP-mediated bursts in TC neurons are proficient in triggering synchronous UP states in cortical networks, thereby bringing about a synchronous slow oscillation in the whole network. The importance of LTCP-mediated action potential bursts in the slow oscillation is also underlined by the observation that their associated dendritic Ca2+ signals are the only ones that inform corticothalamic synapses of the TC neuron output, since they, but not those elicited by tonic action potential firing, reach the distal dendritic sites where these synapses are located.
thalamic neurons; cortical neurons; probabilistic network model; dendrites; intrinsic calcium signalling
During Slow Wave Sleep (SWS), cortical activity is dominated by endogenous processes modulated by slow oscillations (0.1–1 Hz): cell ensembles fluctuate between states of sustained activity (UP states) and silent epochs (DOWN states). We investigate here the temporal structure of ensemble activity during UP states by means of multiple single unit recordings in the prefrontal cortex of naturally sleeping rats. As previously shown, the firing rate of each PFC cell peaks at a distinct time lag after the DOWN/UP transition in a consistent order. We show here that, conversely, the latency of the first spike after the UP state onset depends primarily on the session-averaged firing rates of cells (which can be considered as an indirect measure of their intrinsic excitability). This latency can be explained by a simple homogeneous process (Poisson model) of cell firing, with sleep averaged firing rates employed as parameters. Thus, at DOWN/UP transitions, neurons are affected both by a slow process, possibly originating in the cortical network, modulating the time course of firing for each cell, and by a fast, relatively stereotyped reinstatement of activity, related mostly to global activity levels.
UP/DOWN states; cerebral cortex; sleep; replay; electrophysiology; ensemble recordings; spike sequence; synfire chains
In slow neocortical paroxysmal oscillations, the de- and hyperpolarizing envelopes in neocortical neurons are large compared with slow sleep oscillations. Increased local synchrony of membrane potential oscillations during seizure is reflected in larger electroencephalographic oscillations and the appearance of spike- or polyspike-wave complex recruitment at 2- to 3-Hz frequencies. The oscillatory mechanisms underlying this paroxysmal activity were investigated in computational models of cortical networks. The extracellular K+ concentration ([K+]o) was continuously computed based on neuronal K+ currents and K+ pumps as well as glial buffering. An increase of [K+]o triggered a transition from normal awake-like oscillations to 2- to 3-Hz seizure-like activity. In this mode, the cells fired periodic bursts and nearby neurons oscillated highly synchronously; in some cells depolarization led to spike inactivation lasting 50–100 ms. A [K+]o increase, sufficient to produce oscillations could result from excessive firing (e.g., induced by external stimulation) or inability of K+ regulatory system (e.g., when glial buffering was blocked). A combination of currents including high-threshold Ca2+, persistent Na+ and hyperpolarization-activated depolarizing (Ih) currents was sufficient to maintain 2- to 3-Hz activity. In a network model that included lateral K+ diffusion between cells, increase of [K+]o in a small region was generally sufficient to maintain paroxysmal oscillations in the whole network. Slow changes of [K+]o modulated the frequency of bursting and, in some case, led to fast oscillations in the 10- to 15-Hz frequency range, similar to the fast runs observed during seizures in vivo. These results suggest that modifications of the intrinsic currents mediated by increase of [K+]o can explain the range of neocortical paroxysmal oscillations in vivo.
There is a general consensus that sleep is strictly linked to memory, learning, and, in general, to the mechanisms of neural plasticity, and that this link may directly affect recovery processes. In fact, a coherent pattern of empirical findings points to beneficial effect of sleep on learning and plastic processes, and changes in synaptic plasticity during wakefulness induce coherent modifications in EEG slow wave cortical topography during subsequent sleep. However, the specific nature of the relation between sleep and synaptic plasticity is not clear yet. We reported findings in line with two models conflicting with respect to the underlying mechanisms, that is, the “synaptic homeostasis hypothesis” and the “consolidation” hypothesis, and some recent results that may reconcile them. Independently from the specific mechanisms involved, sleep loss is associated with detrimental effects on plastic processes at a molecular and electrophysiological level. Finally, we reviewed growing evidence supporting the notion that plasticity-dependent recovery could be improved managing sleep quality, while monitoring EEG during sleep may help to explain how specific rehabilitative paradigms work. We conclude that a better understanding of the sleep-plasticity link could be crucial from a rehabilitative point of view.
When the brain is awake, neurons in the cerebral cortex fire irregularly and the electroencephalogram (EEG) displays low amplitude, high frequency fluctuations. After falling asleep, neurons start oscillating between ON periods, when they fire as during wake, and OFF periods, when they stop firing altogether, and the EEG displays high amplitude slow waves. But what happens to neuronal firing after a long period of wake? We show here in freely behaving rats that, after prolonged wake, cortical neurons can go briefly “OFF line” as they do in sleep, accompanied by slower waves in the local EEG. Strikingly, neurons often go OFF line in one cortical area and not in another. During these periods of “local sleep”, whose incidence increases with wake duration, rats appear awake, active, and display a wake EEG. However, they are progressively impaired in a sugar pellet reaching task. Thus, though both the EEG and behavior indicate wakefulness, local populations of neurons in the cortex may be falling asleep, with negative consequences on performance.
slow wave sleep; slow oscillations; EEG; cerebral cortex; multi-unit recording; reaching task; sleep deprivation
The amount and architecture of vigilance states are governed by two distinct processes, which occur at different time scales. The first, a slow one, is related to a wake/sleep dependent homeostatic Process S, which occurs on a time scale of hours, and is reflected in the dynamics of NREM sleep EEG slow-wave activity. The second, a fast one, is manifested in a regular alternation of two sleep states – NREM and REM sleep, which occur, in rodents, on a time scale of ∼5–10 minutes. Neither the mechanisms underlying the time constants of these two processes – the slow one and the fast one, nor their functional significance are understood. Notably, both processes are primarily apparent during sleep, while their potential manifestation during wakefulness is obscured by ongoing behaviour. Here, we find, in mice provided with running wheels, that the two sleep processes become clearly apparent also during waking at the level of behavior and brain activity. Specifically, the slow process was manifested in the total duration of waking periods starting from dark onset, while the fast process was apparent in a regular occurrence of running bouts during the waking periods. The dynamics of both processes were stable within individual animals, but showed large interindividual variability. Importantly, the two processes were not independent: the periodic structure of waking behaviour (fast process) appeared to be a strong predictor of the capacity to sustain continuous wakefulness (slow process). The data indicate that the temporal organization of vigilance states on both the fast and the slow time scales may arise from a common neurophysiologic mechanism.
Gamma oscillations (40–120 Hz), usually associated with waking functions, can be recorded in the deepest stages of sleep in animals. The full details of their large-scale coordination across multiple cortical networks are still unknown. Furthermore, it is not known whether oscillations with similar characteristics are also present in the human brain. In this study, we examined the existence of gamma oscillations during polysomnographically defined sleep–wake states using large-scale microelectrode recordings (up to 56 channels), with single-cell and spike-time precision, in epilepsy patients. We report that low (40–80 Hz) and high (80–120 Hz) gamma oscillations recurrently emerged over time windows of several hundreds of milliseconds in all investigated cortical areas during slow-wave sleep. These patterns were correlated with positive peaks of EEG slow oscillations and marked increases in local cellular discharges, suggesting that they were associated with cortical UP states. These gamma oscillations frequently appeared at approximately the same time in many different cortical areas, including homotopic regions, forming large spatial patterns. Coincident firings with millisecond precision were strongly enhanced during gamma oscillations but only between cells within the same cortical area. Furthermore, in a significant number of cases, cortical gamma oscillations tended to occur within 100 ms after hippocampal ripple/sharp wave complexes. These data confirm and extend earlier animal studies reporting that gamma oscillations are transiently expressed during UP states during sleep. We speculate that these high-frequency patterns briefly restore “microwake” activity and are important for consolidation of memory traces acquired during previous awake periods.