Sleep-dependent improvement in visuomotor learning: a causal role for slow waves

Effects of sleep deprivation on retrieval and reconsolidation of morphine reward memory in rats

Relapse induced by exposure to cues associated with drugs of abuse is a major challenge to the treatment of drug addiction. Drug seeking can be inhibited by manipulation of the reconsolidation of drug-related memory. Sleep has been proposed to be involved in various memory processes. However, the role of sleep in drug reward memory is not clear. The present study used conditioned place preference to examine the effects of total sleep deprivation on retrieval and reconsolidation of morphine reward memory in rats. Six-hour total sleep deprivation had no effect on the retrieval of morphine reward memory. However, sleep deprivation from 0-6 h, but not 6-12 h, after re-exposure disrupted the reconsolidation of morphine reward memory. This impairment was not attributable to the formation of an aversive associative memory between the drug-paired context and sleep deprivation. Our findings suggest that sleep plays a critical role in morphine reward memory reconsolidation, and sleep deprivation may be a potential non-pharmacotherapy for the management of relapse associated with drug-related memory.

Local sleep homeostasis in the avian brain: convergence of sleep function in mammals and birds?

The function of the brain activity that defines slow wave sleep (SWS) and rapid eye movement (REM) sleep in mammals is unknown. During SWS, the level of electroencephalogram slow wave activity (SWA or 0.5-4.5 Hz power density) increases and decreases as a function of prior time spent awake and asleep, respectively. Such dynamics occur in response to waking brain use, as SWA increases locally in brain regions used more extensively during prior wakefulness. Thus, SWA is thought to reflect homeostatically regulated processes potentially tied to maintaining optimal brain functioning. Interestingly, birds also engage in SWS and REM sleep, a similarity that arose via convergent evolution, as sleeping reptiles and amphibians do not show similar brain activity. Although birds deprived of sleep show global increases in SWA during subsequent sleep, it is unclear whether avian sleep is likewise regulated locally. Here, we provide, to our knowledge, the first electrophysiological evidence for local sleep homeostasis in the avian brain. After staying awake watching David Attenborough's The Life of Birds with only one eye, SWA and the slope of slow waves (a purported marker of synaptic strength) increased only in the hyperpallium--a primary visual processing region--neurologically connected to the stimulated eye. Asymmetries were specific to the hyperpallium, as the non-visual mesopallium showed a symmetric increase in SWA and wave slope. Thus, hypotheses for the function of mammalian SWS that rely on local sleep homeostasis may apply also to birds.

The cortical topography of local sleep

In a recent series of experiments, we demonstrated that a visuomotor adaptation task, 12 hours of left arm immobilization, and rapid transcranial magnetic stimulation (rTMS) during waking can each induce local changes in the topography of electroencephalographic (EEG) slow wave activity (SWA) during subsequent non-rapid eye movement (NREM) sleep. However, the poor spatial resolution of EEG and the difficulty of relating scalp potentials to the activity of the underlying cortex limited the interpretation of these results. In order to better understand local cortical regulation of sleep, we used source modeling to show that plastic changes in specific cortical areas during waking produce correlated changes in SWA during sleep in those same areas. We found that implicit learning of a visuomotor adaptation task induced an increase in SWA in right premotor and sensorimotor cortices when compared to a motor control. These same areas have previously been shown to be selectively involved in the performance of this task. We also found that arm immobilization resulted in a decrease in SWA in sensorimotor cortex. Inducing cortical potentiation with repetitive transcranial magnetic stimulation (rTMS) caused an increase in SWA in the targeted area and a decrease in SWA in the contralateral cortex. Finally, we report the first evidence that these modulations in SWA may be related to the dynamics of individual slow waves. We conclude that there is a local, plasticity dependent component to sleep regulation and confirm previous inferences made from the scalp data.

Hippocampal memory consolidation during sleep: a comparison of mammals and birds

The transition from wakefulness to sleep is marked by pronounced changes in brain activity. The brain rhythms that characterize the two main types of mammalian sleep, slow-wave sleep (SWS) and rapid eye movement (REM) sleep, are thought to be involved in the functions of sleep. In particular, recent theories suggest that the synchronous slow-oscillation of neocortical neuronal membrane potentials, the defining feature of SWS, is involved in processing information acquired during wakefulness. According to the Standard Model of memory consolidation, during wakefulness the hippocampus receives input from neocortical regions involved in the initial encoding of an experience and binds this information into a coherent memory trace that is then transferred to the neocortex during SWS where it is stored and integrated within preexisting memory traces. Evidence suggests that this process selectively involves direct connections from the hippocampus to the prefrontal cortex (PFC), a multimodal, high-order association region implicated in coordinating the storage and recall of remote memories in the neocortex. The slow-oscillation is thought to orchestrate the transfer of information from the hippocampus by temporally coupling hippocampal sharp-wave/ripples (SWRs) and thalamocortical spindles. SWRs are synchronous bursts of hippocampal activity, during which waking neuronal firing patterns are reactivated in the hippocampus and neocortex in a coordinated manner. Thalamocortical spindles are brief 7-14 Hz oscillations that may facilitate the encoding of information reactivated during SWRs. By temporally coupling the readout of information from the hippocampus with conditions conducive to encoding in the neocortex, the slow-oscillation is thought to mediate the transfer of information from the hippocampus to the neocortex. Although several lines of evidence are consistent with this function for mammalian SWS, it is unclear whether SWS serves a similar function in birds, the only taxonomic group other than mammals to exhibit SWS and REM sleep. Based on our review of research on avian sleep, neuroanatomy, and memory, although involved in some forms of memory consolidation, avian sleep does not appear to be involved in transferring hippocampal memories to other brain regions. Despite exhibiting the slow-oscillation, SWRs and spindles have not been found in birds. Moreover, although birds independently evolved a brain region--the caudolateral nidopallium (NCL)--involved in performing high-order cognitive functions similar to those performed by the PFC, direct connections between the NCL and hippocampus have not been found in birds, and evidence for the transfer of information from the hippocampus to the NCL or other extra-hippocampal regions is lacking. Although based on the absence of evidence for various traits, collectively, these findings suggest that unlike mammalian SWS, avian SWS may not be involved in transferring memories from the hippocampus. Furthermore, it suggests that the slow-oscillation, the defining feature of mammalian and avian SWS, may serve a more general function independent of that related to coordinating the transfer of information from the hippocampus to the PFC in mammals. Given that SWS is homeostatically regulated (a process intimately related to the slow-oscillation) in mammals and birds, functional hypotheses linked to this process may apply to both taxonomic groups.

Mapping of cortical activity in the first two decades of life: a high-density sleep electroencephalogram study

Evidence that electroencephalography (EEG) slow-wave activity (SWA) (EEG spectral power in the 1-4.5 Hz band) during non-rapid eye movement sleep (NREM) reflects plastic changes is increasing (Tononi and Cirelli, 2006). Regional assessment of gray matter development from neuroimaging studies reveals a posteroanterior trajectory of cortical maturation in the first three decades of life (Shaw et al., 2008). Our aim was to test whether this regional cortical maturation is reflected in regional changes of sleep SWA. We evaluated all-night high-density EEG (128 channels) in 55 healthy human subjects (2.4-19.4 years) and assessed age-related changes in NREM sleep topography. As in adults, we observed frequency-specific topographical distributions of sleep EEG power in all subjects. However, from early childhood to late adolescence, the location on the scalp showing maximal SWA underwent a shift from posterior to anterior regions. This shift along the posteroanterior axis was only present in the SWA frequency range and remained stable across the night. Changes in the topography of SWA during sleep parallel neuroimaging study findings indicating cortical maturation starts early in posterior areas and spreads rostrally over the frontal cortex. Thus, SWA might reflect the underlying processes of cortical maturation. In the future, sleep SWA assessments may be used as a clinical tool to detect aberrations in cortical maturation.

Sleep and synaptic renormalization: a computational study

Recent evidence indicates that net synaptic strength in cortical and other networks increases during wakefulness and returns to a baseline level during sleep. These homeostatic changes in synaptic strength are accompanied by corresponding changes in sleep slow wave activity (SWA) and in neuronal firing rates and synchrony. Other evidence indicates that sleep is associated with an initial reactivation of learned firing patterns that decreases over time. Finally, sleep can enhance performance of learned tasks, aid memory consolidation, and desaturate the ability to learn. Using a large-scale model of the corticothalamic system equipped with a spike-timing dependent learning rule, in agreement with experimental results, we demonstrate a net increase in synaptic strength in the waking mode associated with an increase in neuronal firing rates and synchrony. In the sleep mode, net synaptic strength decreases accompanied by a decline in SWA. We show that the interplay of activity and plasticity changes implements a control loop yielding an exponential, self-limiting renormalization of synaptic strength. Moreover, when the model "learns" a sequence of activation during waking, the learned sequence is preferentially reactivated during sleep, and reactivation declines over time. Finally, sleep-dependent synaptic renormalization leads to increased signal-to-noise ratios, increased resistance to interference, and desaturation of learning capabilities. Although the specific mechanisms implemented in the model cannot capture the variety and complexity of biological substrates, and will need modifications in line with future evidence, the present simulations provide a unified, parsimonious account for diverse experimental findings coming from molecular, electrophysiological, and behavioral approaches.

Slow wave activity during sleep: functional and therapeutic implications

Electroencephalographic slow-wave activity (EEG SWA) is an electrophysiological signature of slow (0.5 to 4.0 Hz), synchronized, oscillatory neocortical activity. In healthy individuals, EEG SWA is maximally expressed during non-rapid-eye-movement (non-REM) sleep, and intensifies as a function of prior wake duration. Many of the cellular and network mechanisms generating EEG SWA have been identified, but a number of questions remain unanswered. For example, although EEG SWA is a marker of sleep need, its precise relationship with sleep homeostasis and its roles in the brain are unknown. In this review, the authors discuss their current understanding of the neural mechanisms and possible functions of EEG SWA.

Sleep homeostasis in the rat is preserved during chronic sleep restriction

Sleep is homeostatically regulated in all animal species that have been carefully studied so far. The best characterized marker of sleep homeostasis is slow wave activity (SWA), the EEG power between 0.5 and 4 Hz during nonrapid eye movement (NREM) sleep. SWA reflects the accumulation of sleep pressure as a function of duration and/or intensity of prior wake: it increases after spontaneous wake and short-term (3-24 h) sleep deprivation and decreases during sleep. However, recent evidence suggests that during chronic sleep restriction (SR) sleep may be regulated by both allostatic and homeostatic mechanisms. Here, we performed continuous, almost completely artifact-free EEG recordings from frontal, parietal, and occipital cortex in freely moving rats (n = 11) during and after 5 d of SR. During SR, rats were allowed to sleep during the first 4 h of the light period (4S(+)) but not during the following 20 h (20S(-)). During the daily 20S(-) most sleep was prevented, whereas the number of short (<20 s) sleep attempts increased. Low-frequency EEG power (1-6 Hz) in both sleep and wake also increased during 20S(-), most notably in the occipital cortex. In all animals NREM SWA increased above baseline levels during the 4S(+) periods and in post-SR recovery. The SWA increase was more pronounced in frontal cortex, and its magnitude was determined by the efficiency of SR. Analysis of cumulative slow wave energy demonstrated that the loss of SWA during SR was compensated by the end of the second recovery day. Thus, the homeostatic regulation of sleep is preserved under conditions of chronic SR.

Specific increases within global decreases: a functional magnetic resonance imaging investigation of five days of motor sequence learning

Our capacity to learn movement sequences is fundamental to our ability to interact with the environment. Although different brain networks have been linked with different stages of learning, there is little evidence for how these networks change across learning. We used functional magnetic resonance imaging to identify the specific contributions of the cerebellum and primary motor cortex (M1) during early learning, consolidation, and retention of a motor sequence task. Performance was separated into two components: accuracy (the more explicit, rapidly learned, stimulus-response association component) and synchronization (the more procedural, slowly learned component). The network of brain regions active during early learning was dominated by the cerebellum, premotor cortex, basal ganglia, presupplementary motor area, and supplementary motor area as predicted by existing models. Across days of learning, as performance improved, global decreases were found in the majority of these regions. Importantly, within the context of these global decreases, we found specific regions of the left M1 and right cerebellar VIIIA/VIIB that were positively correlated with improvements in synchronization performance. Improvements in accuracy were correlated with increases in hippocampus, BA 9/10, and the putamen. Thus, the two behavioral measures, accuracy and synchrony, were found to be related to two different sets of brain regions-suggesting that these networks optimize different components of learning. In addition, M1 activity early on day 1 was shown to be predictive of the degree of consolidation on day 2. Finally, functional connectivity between M1 and cerebellum in late learning points to their interaction as a mechanism underlying the long-term representation and expression of a well learned skill.

Sleep and brain energy levels: ATP changes during sleep

Sleep is one of the most pervasive biological phenomena, but one whose function remains elusive. Although many theories of function, indirect evidence, and even common sense suggest sleep is needed for an increase in brain energy, brain energy levels have not been directly measured with modern technology. We here report that ATP levels, the energy currency of brain cells, show a surge in the initial hours of spontaneous sleep in wake-active but not in sleep-active brain regions of rat. The surge is dependent on sleep but not time of day, since preventing sleep by gentle handling of rats for 3 or 6 h also prevents the surge in ATP. A significant positive correlation was observed between the surge in ATP and EEG non-rapid eye movement delta activity (0.5-4.5 Hz) during spontaneous sleep. Inducing sleep and delta activity by adenosine infusion into basal forebrain during the normally active dark period also increases ATP. Together, these observations suggest that the surge in ATP occurs when the neuronal activity is reduced, as occurs during sleep. The levels of phosphorylated AMP-activated protein kinase (P-AMPK), well known for its role in cellular energy sensing and regulation, and ATP show reciprocal changes. P-AMPK levels are lower during the sleep-induced ATP surge than during wake or sleep deprivation. Together, these results suggest that sleep-induced surge in ATP and the decrease in P-AMPK levels set the stage for increased anabolic processes during sleep and provide insight into the molecular events leading to the restorative biosynthetic processes occurring during sleep.

Slow-wave sleep deficiency and enhancement: implications for insomnia and its management

In humans, slow-wave sleep (SWS) consists of stages 3 and 4 of non rapid eye movement (nonREM) sleep. The low-frequency, high-amplitude slow waves that dominate the electroencephalogram (EEG) during SWS can be quantified as slow-wave activity (SWA). SWS and SWA are regulated very accurately in response to variations in the duration and intensity of wakefulness and sleep. SWA declines more or less independently of circadian phase during the course of a sleep episode, indicating that it is primarily under homeostatic rather than circadian control. An age-related decline in SWS and SWA is well established. In some studies, apprehension, depression and insomnia have been associated with reductions in SWS and SWA. Experimental reductions of SWS through SWS deprivation (without altering total sleep time or REM duration) have been reported to lead to an increase in daytime sleep propensity and reductions in performance. SWS and SWA are therefore thought to contribute to the recovery processes that occur during sleep. Most currently prescribed hypnotics, such as the benzodiazepines and Z-drugs, suppress SWA. Some compounds have been shown to enhance SWS and SWA in healthy volunteers through GAT-1 inhibition, GABA-A modulation, GABA-B modulation, and 5HT2(A) antagonism. Pharmacological enhancement of SWS has also been observed in insomnia. The effects of SWS enhancement on other sleep parameters will be discussed.

The effects of experimental sleep fragmentation on cognitive processing

OBJECTIVE

The primary objective of this study was to characterize the association between cyclic alternating pattern (CAP) and neurocognitive performance in a group of normal subjects before and after two nights of experimentally-induced sleep fragmentation.

SUBJECTS AND METHODS

Fifteen healthy subjects underwent one night of uninterrupted and two sequential nights of experimental sleep fragmentation achieved by auditory and mechanical stimuli. Eight subjects were re-examined using a similar paradigm with three nights of uninterrupted sleep. Sleep was polygraphically recorded and CAP analysis was performed for all recordings. A battery of neurocognitive tests was performed for spatial attention, inhibition of return, mental rotation, and Stroop color word test in the afternoon following the first and third night of sleep under fragmented and non-fragmented conditions.

RESULTS

With sleep fragmentation, the percentage of slow-wave sleep was dramatically reduced and there was a twofold increase in total CAP rate across all NREM sleep stages. Moreover, the number of all CAP A subtypes/hour of sleep (index) was significantly increased. Total CAP rate during the non-fragmented night correlated with reaction times. Similarly, the percentages of A1 and A3 subtypes were negatively and positively correlated with reaction times, respectively. Of the neurocognitive test battery, however, only values obtained from some subtests of the mental rotation test showed a significant improvement after sleep fragmentation.

CONCLUSIONS

The results of this study suggest that CAP A1 subtypes are associated with higher cognitive functioning, whereas CAP A3 subtypes are associated with lower cognitive functioning in young healthy subjects. The lack of cognitive functioning impairment after sleep fragmentation may be due to persistence and even enhancement of transient slow-wave activity contained in CAP A1 subtypes which also caused a significant enhancement of the EEG power spectrum in the lower frequencies.

The effects of morning training on night sleep: a behavioral and EEG study

The consolidation of memories in a variety of learning processes benefits from post-training sleep, and recent work has suggested a role for sleep slow wave activity (SWA). Previous studies using a visuomotor learning task showed a local increase in sleep SWA in right parietal cortex, which was correlated with post-sleep performance enhancement. In these as in most similar studies, learning took place in the evening, shortly before sleep. Thus, it is currently unknown whether learning a task in the morning, followed by the usual daily activities, would also result in a local increase in sleep SWA during the night, and in a correlated enhancement in performance the next day. To answer this question, a group of subjects performed a visuomotor learning task in the morning and was retested the following morning. Whole night sleep was recorded with high-density EEG. We found an increase of SWA over the right posterior parietal areas that was most evident during the second sleep cycle. Performance improved significantly the following morning, and the improvement was positively correlated with the SWA increase in the second sleep cycle. These results suggest that training-induced changes in sleep SWA and post-sleep improvements do not depend upon the time interval between original training and sleep.