Sleep oscillations in the thalamocortical system induce long-term neuronal plasticity

Possible mechanisms to improve sleep spindles via closed loop stimulation during slow wave sleep: A computational study

Sleep spindles are one of the prominent EEG oscillatory rhythms of non-rapid eye movement sleep. In the memory consolidation, these oscillations have an important role in the processes of long-term potentiation and synaptic plasticity. Moreover, the activity (spindle density and/or sigma power) of spindles has a linear association with learning performance in different paradigms. According to the experimental observations, the sleep spindle activity can be improved by closed loop acoustic stimulations (CLAS) which eventually improve memory performance. To examine the effects of CLAS on spindles, we propose a biophysical thalamocortical model for slow oscillations (SOs) and sleep spindles. In addition, closed loop stimulation protocols are applied on a thalamic network. Our model results show that the power of spindles is increased when stimulation cues are applied at the commencing of an SO Down-to-Up-state transition, but that activity gradually decreases when cues are applied with an increased time delay from this SO phase. Conversely, stimulation is not effective when cues are applied during the transition of an Up-to-Down-state. Furthermore, our model suggests that a strong inhibitory input from the reticular (RE) layer to the thalamocortical (TC) layer in the thalamic network shifts leads to an emergence of spindle activity at the Up-to-Down-state transition (rather than at Down-to-Up-state transition), and the spindle frequency is also reduced (8-11 Hz) by thalamic inhibition.

Sleep pressure modulates single-neuron synapse number in zebrafish

Sleep is a nearly universal behaviour with unclear functions1. The synaptic homeostasis hypothesis proposes that sleep is required to renormalize the increases in synaptic number and strength that occur during wakefulness2. Some studies examining either large neuronal populations3 or small patches of dendrites4 have found evidence consistent with the synaptic homeostasis hypothesis, but whether sleep merely functions as a permissive state or actively promotes synaptic downregulation at the scale of whole neurons is unclear. Here, by repeatedly imaging all excitatory synapses on single neurons across sleep-wake states of zebrafish larvae, we show that synapses are gained during periods of wake (either spontaneous or forced) and lost during sleep in a neuron-subtype-dependent manner. However, synapse loss is greatest during sleep associated with high sleep pressure after prolonged wakefulness, and lowest in the latter half of an undisrupted night. Conversely, sleep induced pharmacologically during periods of low sleep pressure is insufficient to trigger synapse loss unless adenosine levels are boosted while noradrenergic tone is inhibited. We conclude that sleep-dependent synapse loss is regulated by sleep pressure at the level of the single neuron and that not all sleep periods are equally capable of fulfilling the functions of synaptic homeostasis.

Neurocognitive Effects of Sleep Disruption in Children and Adolescents

A main childhood task is learning. In this task, the role of sleep is increasingly demonstrated. Although most literature examining this role focuses on preadolescence and middle adolescence, some studies apply napping designs in preschoolers. Studies overall conclude that without proper sleep a child's cognitive abilities suffer, but questions on how and to what extent linger. Observational studies show the hazards of potential confounders such as an individual's resilience to poor sleep as well as developmental risk factors (eg, disorders, stressors). A better understanding of cognitive sleep neuroscience may have a big impact on pediatric sleep research and clinical applications.

Accelerometer-based sleep metrics and gut microbiota during adolescence: Association findings from a Brazilian population-based birth cohort

BACKGROUND

Sleep and gut microbiota are emerging putative risk factors for several physical, mental, and cognitive conditions. Sleep deprivation has been shown to be linked with unhealthy microbiome environments in animal studies. However, in humans, the results are mixed. Epidemiological studies evaluating the effect of accelerometer-based sleep measures on gut microbiome are scarce. This study aims to explore the relationship between sleep duration and efficiency with the gut microbiota in adolescence.

METHODS

A subsample of 352 participants from the 2004 Pelotas (Brazil) Birth Cohort Study with sleep and fecal microbiota data available were included in the study. Sleep duration and sleep efficiency were obtained from actigraphy information at 11 years old whereas microbiota information from fecal samples was collected at 12 years. The fecal microbiota was analyzed via Illumina MiSeq (16S rRNA V3-V4 region) and the UNOISE pipeline. Alpha was assessed in QIIME2. Association measures for sleep variables and microbial α-diversity, and bacterial relative abundance were assessed through generalized models (linear and logistic regression), adjusting for maternal and child variables confounders.

RESULTS

Adjusted models showed that sleep duration was positively associated with Simpson index of α-diversity (β = 0.003; CI95 %: 0.00004; 0.01). Both sleep duration (OR = 0.43; CI95 % 0.25; 0.74) and efficiency (OR = 0.55; CI95 % 0.38; 0.78) were associated with lower Bacteroidetes abundance.

CONCLUSION

Our results suggest that sleep duration and efficiency are linked to gut microbiota diversity and composition even with 1-2 years gap from exposure to outcome. The findings support the role of sleep in the gut-brain axis as well as provide insights on how to improve microbiota health.

Orexin Receptor Antagonism: Normalizing Sleep Architecture in Old Age and Disease

Sleep is essential for human well-being, yet the quality and quantity of sleep reduce as age advances. Older persons (>65 years old) are more at risk of disorders accompanied and/or exacerbated by poor sleep. Furthermore, evidence supports a bidirectional relationship between disrupted sleep and Alzheimer's disease (AD) or related dementias. Orexin/hypocretin neuropeptides stabilize wakefulness, and several orexin receptor antagonists (ORAs) are approved for the treatment of insomnia in adults. Dysregulation of the orexin system occurs in aging and AD, positioning ORAs as advantageous for these populations. Indeed, several clinical studies indicate that ORAs are efficacious hypnotics in older persons and dementia patients and, as in adults, are generally well tolerated. ORAs are likely to be more effective when administered early in sleep/wake dysregulation to reestablish good sleep/wake-related behaviors and reduce the accumulation of dementia-associated proteinopathic substrates. Improving sleep in aging and dementia represents a tremendous opportunity to benefit patients, caregivers, and health systems.

Neuromodulation via muscarinic acetylcholine pathway can facilitate distinct, complementary, and sequential roles for NREM and REM states during sleep-dependent memory consolidation

Across vertebrate species, sleep consists of repeating cycles of NREM followed by REM. However, the respective functions of NREM, REM, and their stereotypic cycling pattern are not well understood. Using a simplified biophysical network model, we show that NREM and REM sleep can play differential and critical roles in memory consolidation primarily regulated, based on state-specific changes in cholinergic signaling. Within this network, decreasing and increasing muscarinic acetylcholine (ACh) signaling during bouts of NREM and REM, respectively, differentially alters neuronal excitability and excitatory/inhibitory balance. During NREM, deactivation of inhibitory neurons leads to network-wide disinhibition and bursts of synchronized activity led by firing in engram neurons. These features strengthen connections from the original engram neurons to less-active network neurons. In contrast, during REM, an increase in network inhibition suppresses firing in all but the most-active excitatory neurons, leading to competitive strengthening/pruning of the memory trace. We tested the predictions of the model against in vivo recordings from mouse hippocampus during active sleep-dependent memory storage. Consistent with modeling results, we find that functional connectivity between CA1 neurons changes differentially at transition from NREM to REM sleep during learning. Returning to the model, we find that an iterative sequence of state-specific activations during NREM/REM cycling is essential for memory storage in the network, serving a critical role during simultaneous consolidation of multiple memories. Together these results provide a testable mechanistic hypothesis for the respective roles of NREM and REM sleep, and their universal relative timing, in memory consolidation.

Significance statement

Using a simplified computational model and in vivo recordings from mouse hippocampus, we show that NREM and REM sleep can play differential roles in memory consolidation. The specific neurophysiological features of the two sleep states allow for expansion of memory traces (during NREM) and prevention of overlap between different memory traces (during REM). These features are likely essential in the context of storing more than one new memory simultaneously within a brain network.

Correlation Between Cued Fear Memory Retrieval and Oscillatory Network Inhibition in the Amygdala Is Disrupted by Acute REM Sleep Deprivation

The basolateral amygdaloid complex (BLA) is critically involved in emotional behaviors, such as aversive memory formation. In particular, fear memory after cued fear conditioning is strongly associated with the BLA, whereas both the BLA and hippocampus are essential for contextual fear memory formation. In the present study, we examined the effects of acute (3 h) sleep deprivation (SD) on BLA-associated fear memory in juvenile (P24-32) rats and performed in vitro electrophysiology using whole-cell patch clamping from the basolateral nucleus (BA) of the BLA. BA projection neurons exhibit the network oscillation, i.e., spontaneous oscillatory bursts of inhibitory transmission at 0.1-3 Hz, as previously reported. In the present study, SD either before or after fear conditioning (FC) disturbed the acquisition of tone-associated fear memory without significant effects on contextual fear memory. FC reduced the power of the oscillatory activity, but SD did not further reduce the oscillation power. Oscillation power was correlated with tone-associated freezing rate (FR) in SD-free fear-conditioned rats, but this relation was disrupted in SD treated group. Rhythm index (RI), the rhythmicity of the oscillation, quantified by autocorrelation analysis, also correlated with tone-associated FR in the combined data, including FC alone and FC with SD. These results suggest that slow network oscillation in the amygdala contributes to the formation of amygdala-dependent fear memory in relation to sleep.

Orexin 2 receptor antagonism sex-dependently improves sleep/wakefulness and cognitive performance in tau transgenic mice

BACKGROUND AND PURPOSE

Tau pathology contributes to a bidirectional relationship between sleep disruption and neurodegenerative disease. Tau transgenic rTg4510 mice model tauopathy symptoms, including sleep/wake disturbances, which manifest as marked hyperarousal. This phenotype can be prevented by early transgene suppression; however, whether hyperarousal can be rescued after onset is unknown.

EXPERIMENTAL APPROACH

Three 8-week experiments were conducted with wild-type and rTg4510 mice after age of onset of hyperarousal (4.5 months): (1) Tau transgene suppression with doxycycline (200 ppm); (2) inactive phase rapid eye movement (REM) sleep enhancement with the dual orexin receptor antagonist suvorexant (50 mg·kg-1 ·day-1 ); or (3) Active phase non-NREM (NREM) and REM sleep enhancement using the selective orexin 2 (OX2 ) receptor antagonist MK-1064 (40 mg·kg-1 ·day-1 ). Sleep was assessed using polysomnography, cognition using the Barnes maze, and tau pathology using immunoblotting and/or immunohistochemistry.

KEY RESULTS

Tau transgene suppression improved tauopathy and hippocampal-dependent spatial memory, but did not modify hyperarousal. Pharmacological rescue of REM sleep deficits did not improve spatial memory or tau pathology. In contrast, normalising hyperarousal by increasing both NREM and REM sleep via OX2 receptor antagonism restored spatial memory, independently of tauopathy, but only in male rTg4510 mice. OX2 receptor antagonism induced only short-lived hypnotic responses in female rTg4510 mice and did not improve spatial memory, indicating a tau- and sex-dependent disruption of OX2 receptor signalling.

CONCLUSIONS AND IMPLICATIONS

Pharmacologically reducing hyperarousal corrects tau-induced sleep/wake and cognitive deficits. Tauopathy causes sex-dependent disruptions of OX2 receptor signalling/function, which may have implications for choice of hypnotic therapeutics in tauopathies.

Closed-loop auditory stimulation of sleep slow oscillations: Basic principles and best practices

Sleep is essential for our physical and mental well-being. During sleep, despite the paucity of overt behavior, our brain remains active and exhibits a wide range of coupled brain oscillations. In particular slow oscillations are characteristic for sleep, however whether they are directly involved in the functions of sleep, or are mere epiphenomena, is not yet fully understood. To disentangle the causality of these relationships, experiments utilizing techniques to detect and manipulate sleep oscillations in real-time are essential. In this review, we first overview the theoretical principles of closed-loop auditory stimulation (CLAS) as a method to study the role of slow oscillations in the functions of sleep. We then describe technical guidelines and best practices to perform CLAS and analyze results from such experiments. We further provide an overview of how CLAS has been used to investigate the causal role of slow oscillations in various sleep functions. We close by discussing important caveats, open questions, and potential topics for future research.

Human REM sleep recalibrates neural activity in support of memory formation

The proposed mechanisms of sleep-dependent memory consolidation involve the overnight regulation of neural activity at both synaptic and whole-network levels. Now, there is a lack of in vivo data in humans elucidating if, and how, sleep and its varied stages balance neural activity, and if such recalibration benefits memory. We combined electrophysiology with in vivo two-photon calcium imaging in rodents as well as intracranial and scalp electroencephalography (EEG) in humans to reveal a key role for non-oscillatory brain activity during rapid eye movement (REM) sleep to mediate sleep-dependent recalibration of neural population dynamics. The extent of this REM sleep recalibration predicted the success of overnight memory consolidation, expressly the modulation of hippocampal-neocortical activity, favoring remembering rather than forgetting. The findings describe a non-oscillatory mechanism how human REM sleep modulates neural population activity to enhance long-term memory.

Bidirectional Relationship Between Sleep and Depression

Patients with depression almost inevitably exhibit abnormalities in sleep, such as shortened latency to enter rapid eye movement (REM) sleep and decrease in electroencephalogram delta power during non-REM sleep. Insufficient sleep can be stressful, and the accumulation of stress leads to the deterioration of mental health and contributes to the development of psychiatric disorders. Thus, it is likely that depression and sleep are bidirectionally related, i.e. development of depression contributes to sleep disturbances and vice versa. However, the relation between depression and sleep seems complicated. For example, acute sleep deprivation can paradoxically improve depressive symptoms. Thus, it is difficult to conclude whether sleep has beneficial or harmful effects in patients with depression. How antidepressants affect sleep in patients with depression might provide clues to understanding the effects of sleep, but caution is required considering that antidepressants have diverse effects other than sleep. Recent animal studies support the bidirectional relation between depression and sleep, and animal models of depression are expected to be beneficial for the identification of neuronal circuits that connect stress, sleep, and depression. This review provides a comprehensive overview regarding the current knowledge of the relationship between depression and sleep.

BDNF Val66Met polymorphism is associated with consolidation of episodic memory during sleep

The brain-derived neurotrophic factor (BDNF) is an essential regulator of synaptic plasticity, a candidate neurobiological mechanism underlying learning and memory. A functional polymorphism in the BDNF gene, Val66Met (rs6265), has been linked to memory and cognition in healthy individuals and clinical populations. Sleep contributes to memory consolidation, yet information about the possible role of BDNF in this process is scarce. To address this question, we investigated the relationship between the BDNF Val66Met genotype and consolidation of episodic declarative and procedural (motor) non-declarative memories in healthy adults. The carriers of Met66 allele, as compared with Val66 homozygotes, showed stronger forgetting overnight (24hours after encoding), but not over shorter time (immediately or 20minutes after word list presentation). There was no effect of Val66Met genotype on motor learning. These data suggest that BDNF plays a role in neuroplasticity underlying episodic memory consolidation during sleep.

A sleep-active basalocortical pathway crucial for generation and maintenance of chronic pain

Poor sleep is associated with the risk of developing chronic pain, but how sleep contributes to pain chronicity remains unclear. Here we show that following peripheral nerve injury, cholinergic neurons in the anterior nucleus basalis (aNB) of the basal forebrain are increasingly active during nonrapid eye movement (NREM) sleep in a mouse model of neuropathic pain. These neurons directly activate vasoactive intestinal polypeptide-expressing interneurons in the primary somatosensory cortex (S1), causing disinhibition of pyramidal neurons and allodynia. The hyperactivity of aNB neurons is caused by the increased inputs from the parabrachial nucleus (PB) driven by the injured peripheral afferents. Inhibition of this pathway during NREM sleep, but not wakefulness, corrects neuronal hyperactivation and alleviates pain. Our results reveal that the PB-aNB-S1 pathway during sleep is critical for the generation and maintenance of chronic pain. Inhibiting this pathway during the sleep phase could be important for treating neuropathic pain.

Synapse-Specific Modulation of Synaptic Responses by Brain States in Hippocampal Pathways

Synaptic changes play a major role in memory processes. Modulation of synaptic responses by brain states remains, however, poorly understood in hippocampal networks, even in basal conditions. We recorded evoked synaptic responses at five hippocampal pathways in freely moving male rats. We showed that, at the perforant path to dentate gyrus (PP-DG) synapse, responses increase during wakefulness compared with sleep. At the Schaffer collaterals to CA1 (SC-CA1) synapse, responses increase during non-REM sleep (NREM) compared with the other states. During REM sleep (REM), responses decreased at the PP-DG and SC-CA1 synapses compared with NREM, while they increased at the fornix to nucleus accumbens synapse (Fx-NAc) during REM compared with the other states. In contrast, responses at the fornix to medial PFC synapse (Fx-PFC) and at the fornix to amygdala synapse (Fx-Amy) were weakly modulated by vigilance states. Extended sleep periods led to synaptic changes at PP-DG and Fx-Amy synapses but not at the other synapses. Synaptic responses were also linked to local oscillations and were highly correlated between Fx-PFC and Fx-NAc but not between Fx-Amy and these synapses. These results reveal synapse-specific modulations that may contribute to memory consolidation during the sleep-wake cycle.SIGNIFICANCE STATEMENT Surprisingly, the cortical network dynamics remains poorly known at the synaptic level. We tested the hypothesis that brain states would modulate synaptic changes in the same way at different cortical connections. To tackle this issue, we implemented an approach to explore the synaptic behavior of five connections upstream and downstream the rat hippocampus. Our study reveals that synaptic responses are modulated in a highly synapse-specific manner by wakefulness and sleep states as well as by local oscillations at these connections. Moreover, we found rapid synaptic changes during wake and sleep transitions as well as synaptic down and upregulations after extended periods of sleep. These synaptic changes are likely related to the mechanisms of sleep-dependent memory consolidation.

To sleep or not to sleep - Effects on memory in normal aging and disease

Sleep behavior undergoes significant changes across the lifespan, and aging is associated with marked alterations in sleep amounts and quality. The primary sleep changes in healthy older adults include a shift in sleep timing, reduced slow-wave sleep, and impaired sleep maintenance. However, neurodegenerative and psychiatric disorders are more common among the elderly, which further worsen their sleep health. Irrespective of the cause, insufficient sleep adversely affects various bodily functions including energy metabolism, mood, and cognition. In this review, we will focus on the cognitive changes associated with inadequate sleep during normal aging and the underlying neural mechanisms.

Intracellular chloride regulation mediates local sleep pressure in the cortex

Extended wakefulness is associated with reduced performance and the build-up of sleep pressure. In the cortex, this manifests as changes in network activity. These changes show local variation depending on the waking experience, and their underlying mechanisms represent targets for overcoming the effects of tiredness. Here, we reveal a central role for intracellular chloride regulation, which sets the strength of postsynaptic inhibition via GABAA receptors in cortical pyramidal neurons. Wakefulness results in depolarizing shifts in the equilibrium potential for GABAA receptors, reflecting local activity-dependent processes during waking and involving changes in chloride cotransporter activity. These changes underlie electrophysiological and behavioral markers of local sleep pressure within the cortex, including the levels of slow-wave activity during non-rapid eye movement sleep and low-frequency oscillatory activity and reduced performance levels in the sleep-deprived awake state. These findings identify chloride regulation as a crucial link between sleep-wake history, cortical activity and behavior.

Remembrance of things perceived: Adding thalamocortical function to artificial neural networks

Recent research has illuminated the complexity and importance of the thalamocortical system but it has been difficult to identify what computational functions it performs. Meanwhile, deep-learning artificial neural networks (ANNs) based on bio-inspired models of purely cortical circuits have achieved surprising success solving sophisticated cognitive problems associated historically with human intelligence. Nevertheless, the limitations and shortcomings of artificial intelligence (AI) based on such ANNs are becoming increasingly clear. This review considers how the addition of thalamocortical connectivity and its putative functions related to cortical attention might address some of those shortcomings. Such bio-inspired models are now providing both testable theories of biological cognition and improved AI technology, much of which is happening outside the usual academic venues.

John EK, Potesta CV, Gallagher MJ, Macdonald RL, Zhou C. Sleep slow-wave oscillations trigger seizures in a genetic epilepsy model of Dravet syndrome

Sleep is the preferential period when epileptic spike-wave discharges appear in human epileptic patients, including genetic epileptic seizures such as Dravet syndrome with multiple mutations including SCN1A mutation and GABAA receptor γ2 subunit Gabrg2Q390X mutation in patients, which presents more severe epileptic symptoms in female patients than male patients. However, the seizure onset mechanism during sleep still remains unknown. Our previous work has shown that the sleep-like state-dependent homeostatic synaptic potentiation can trigger epileptic spike-wave discharges in one transgenic heterozygous Gabrg2+/Q390X knock-in mouse model.1 Here, using this heterozygous knock-in mouse model, we hypothesized that slow-wave oscillations themselves in vivo could trigger epileptic seizures. We found that epileptic spike-wave discharges in heterozygous Gabrg2+/Q390X knock-in mice exhibited preferential incidence during non-rapid eye movement sleep period, accompanied by motor immobility/facial myoclonus/vibrissal twitching and more frequent spike-wave discharge incidence appeared in female heterozygous knock-in mice than male heterozygous knock-in mice. Optogenetically induced slow-wave oscillations in vivo significantly increased epileptic spike-wave discharge incidence in heterozygous Gabrg2+/Q390X knock-in mice with longer duration of non-rapid eye movement sleep or quiet-wakeful states. Furthermore, suppression of slow-wave oscillation-related homeostatic synaptic potentiation by 4-(diethylamino)-benzaldehyde injection (i.p.) greatly attenuated spike-wave discharge incidence in heterozygous knock-in mice, suggesting that slow-wave oscillations in vivo did trigger seizure activity in heterozygous knock-in mice. Meanwhile, sleep spindle generation in wild-type littermates and heterozygous Gabrg2+/Q390X knock-in mice involved the slow-wave oscillation-related homeostatic synaptic potentiation that also contributed to epileptic spike-wave discharge generation in heterozygous Gabrg2+/Q390X knock-in mice. In addition, EEG spectral power of delta frequency (0.1-4 Hz) during non-rapid eye movement sleep was significantly larger in female heterozygous Gabrg2+/Q390X knock-in mice than that in male heterozygous Gabrg2+/Q390X knock-in mice, which likely contributes to the gender difference in seizure incidence during non-rapid eye movement sleep/quiet-wake states of human patients. Overall, all these results indicate that slow-wave oscillations in vivo trigger the seizure onset in heterozygous Gabrg2+/Q390X knock-in mice, preferentially during non-rapid eye movement sleep period and likely generate the sex difference in seizure incidence between male and female heterozygous Gabrg2+/Q390X knock-in mice.

Deterministic and Stochastic Components of Cortical Down States: Dynamics and Modulation

Slow oscillations are an emergent activity of the cerebral cortex network consisting of alternating periods of activity (Up states) and silence (Down states). Up states are periods of persistent cortical activity that share properties with that of underlying wakefulness. However, the occurrence of Down states is almost invariably associated with unconsciousness, both in animal models and clinical studies. Down states have been attributed relevant functions, such as being a resetting mechanism or breaking causal interactions between cortical areas. But what do Down states consist of? Here, we explored in detail the network dynamics (e.g., synchronization and phase) during these silent periods in vivo (male mice), in vitro (ferrets, either sex), and in silico, investigating various experimental conditions that modulate them: anesthesia levels, excitability (electric fields), and excitation/inhibition balance. We identified metastability as two complementary phases composing such quiescence states: a highly synchronized "deterministic" period followed by a low-synchronization "stochastic" period. The balance between these two phases determines the dynamical properties of the resulting rhythm, as well as the responsiveness to incoming inputs or refractoriness. We propose detailed Up and Down state cycle dynamics that bridge cortical properties emerging at the mesoscale with their underlying mechanisms at the microscale, providing a key to understanding unconscious states.SIGNIFICANCE STATEMENT The cerebral cortex expresses slow oscillations consisting of Up (active) and Down (silent) states. Such activity emerges not only in slow wave sleep, but also under anesthesia and in brain lesions. Down states functionally disconnect the network, and are associated with unconsciousness. Based on a large collection of data, novel data analysis approaches and computational modeling, we thoroughly investigate the nature of Down states. We identify two phases: a highly synchronized "deterministic" period, followed by a low-synchronization "stochastic" period. The balance between these two phases determines the dynamic properties of the resulting rhythm and responsiveness to incoming inputs. This finding reconciles different theories of slow rhythm generation and provides clues about how the brain switches from conscious to unconscious brain states.

Differential thalamocortical interactions in slow and fast spindle generation: A computational model

Cortical slow oscillations (SOs) and thalamocortical sleep spindles are two prominent EEG rhythms of slow wave sleep. These EEG rhythms play an essential role in memory consolidation. In humans, sleep spindles are categorized into slow spindles (8-12 Hz) and fast spindles (12-16 Hz), with different properties. Slow spindles that couple with the up-to-down phase of the SO require more experimental and computational investigation to disclose their origin, functional relevance and most importantly their relation with SOs regarding memory consolidation. To examine slow spindles, we propose a biophysical thalamocortical model with two independent thalamic networks (one for slow and the other for fast spindles). Our modeling results show that fast spindles lead to faster cortical cell firing, and subsequently increase the amplitude of the cortical local field potential (LFP) during the SO down-to-up phase. Slow spindles also facilitate cortical cell firing, but the response is slower, thereby increasing the cortical LFP amplitude later, at the SO up-to-down phase of the SO cycle. Neither the SO rhythm nor the duration of the SO down state is affected by slow spindle activity. Furthermore, at a more hyperpolarized membrane potential level of fast thalamic subnetwork cells, the activity of fast spindles decreases, while the slow spindles activity increases. Together, our model results suggest that slow spindles may facilitate the initiation of the following SO cycle, without however affecting expression of the SO Up and Down states.

Information maximization explains state-dependent synaptic plasticity and memory reorganization during non-rapid eye movement sleep

Slow waves during the non-rapid eye movement (NREM) sleep reflect the alternating up and down states of cortical neurons; global and local slow waves promote memory consolidation and forgetting, respectively. Furthermore, distinct spike-timing-dependent plasticity (STDP) operates in these up and down states. The contribution of different plasticity rules to neural information coding and memory reorganization remains unknown. Here, we show that optimal synaptic plasticity for information maximization in a cortical neuron model provides a unified explanation for these phenomena. The model indicates that the optimal synaptic plasticity is biased toward depression as the baseline firing rate increases. This property explains the distinct STDP observed in the up and down states. Furthermore, it explains how global and local slow waves predominantly potentiate and depress synapses, respectively, if the background firing rate of excitatory neurons declines with the spatial scale of waves as the model predicts. The model provides a unifying account of the role of NREM sleep, bridging neural information coding, synaptic plasticity, and memory reorganization.

Neuronal-spiking-based closed-loop stimulation during cortical ON- and OFF-states in freely moving mice

The slow oscillation is a central neuronal dynamic during sleep, and is generated by alternating periods of high and low neuronal activity (ON- and OFF-states). Mounting evidence causally links the slow oscillation to sleep's functions, and it has recently become possible to manipulate the slow oscillation non-invasively and phase-specifically. These developments represent promising clinical avenues, but they also highlight the importance of improving our understanding of how ON/OFF-states affect incoming stimuli and what role they play in neuronal plasticity. Most studies using closed-loop stimulation rely on the electroencephalogram and local field potential signals, which reflect neuronal ON- and OFF-states only indirectly. Here we develop an online detection algorithm based on spiking activity recorded from laminar arrays in mouse motor cortex. We find that online detection of ON- and OFF-states reflects specific phases of spontaneous local field potential slow oscillation. Our neuronal-spiking-based closed-loop procedure offers a novel opportunity for testing the functional role of slow oscillation in sleep-related restorative processes and neural plasticity.

Sleep and wake cycles dynamically modulate hippocampal inhibitory synaptic plasticity

Sleep is an essential process that consolidates memories by modulating synapses through poorly understood mechanisms. Here, we report that GABAergic synapses in hippocampal CA1 pyramidal neurons undergo daily rhythmic alterations. Specifically, wake inhibits phasic inhibition, whereas it promotes tonic inhibition compared to sleep. We further utilize a model of chemically induced inhibitory long-term potentiation (iLTP) to examine inhibitory plasticity. Intriguingly, while CA1 pyramidal neurons in both wake and sleep mice undergo iLTP, wake mice have a much higher magnitude. We also employ optogenetics and observe that inhibitory inputs from parvalbumin-, but not somatostatin-, expressing interneurons contribute to dynamic iLTP during sleep and wake. Finally, we demonstrate that synaptic insertion of α5-GABAA receptors underlies the wake-specific enhancement of iLTP at parvalbumin-synapses, which is independent of time of the day. These data reveal a previously unappreciated daily oscillation of inhibitory LTP in hippocampal neurons and uncover a dynamic contribution of inhibitory synapses in memory mechanisms across sleep and wake.

Understanding the roles of central and autonomic activity during sleep in the improvement of working memory and episodic memory

The last decade has seen significant progress in identifying sleep mechanisms that support cognition. Most of these studies focus on the link between electrophysiological events of the central nervous system during sleep and improvements in different cognitive domains, while the dynamic shifts of the autonomic nervous system across sleep have been largely overlooked. Recent studies, however, have identified significant contributions of autonomic inputs during sleep to cognition. Yet, there remain considerable gaps in understanding how central and autonomic systems work together during sleep to facilitate cognitive improvement. In this article we examine the evidence for the independent and interactive roles of central and autonomic activities during sleep and wake in cognitive processing. We specifically focus on the prefrontal-subcortical structures supporting working memory and mechanisms underlying the formation of hippocampal-dependent episodic memory. Our Slow Oscillation Switch Model identifies separate and competing underlying mechanisms supporting the two memory domains at the synaptic, systems, and behavioral levels. We propose that sleep is a competitive arena in which both memory domains vie for limited resources, experimentally demonstrated when boosting one system leads to a functional trade-off in electrophysiological and behavioral outcomes. As these findings inevitably lead to further questions, we suggest areas of future research to better understand how the brain and body interact to support a wide range of cognitive domains during a single sleep episode.

Global and non-Global slow oscillations differentiate in their depth profiles

Sleep slow oscillations (SOs, 0.5-1.5 Hz) are thought to organize activity across cortical and subcortical structures, leading to selective synaptic changes that mediate consolidation of recent memories. Currently, the specific mechanism that allows for this selectively coherent activation across brain regions is not understood. Our previous research has shown that SOs can be classified on the scalp as Global, Local or Frontal, where Global SOs are found in most electrodes within a short time delay and gate long-range information flow during NREM sleep. The functional significance of space-time profiles of SOs hinges on testing if these differential SOs scalp profiles are mirrored by differential depth structure of SOs in the brain. In this study, we built an analytical framework to allow for the characterization of SO depth profiles in space-time across cortical and sub-cortical regions. To test if the two SO types could be differentiated in their cortical-subcortical activity, we trained 30 machine learning classification algorithms to distinguish Global and non-Global SOs within each individual, and repeated this analysis for light (Stage 2, S2) and deep (slow wave sleep, SWS) NREM stages separately. Multiple algorithms reached high performance across all participants, in particular algorithms based on k-nearest neighbors classification principles. Univariate feature ranking and selection showed that the most differentiating features for Global vs. non-Global SOs appeared around the trough of the SO, and in regions including cortex, thalamus, caudate nucleus, and brainstem. Results also indicated that differentiation during S2 required an extended network of current from cortical-subcortical regions, including all regions found in SWS and other basal ganglia regions, and amygdala and hippocampus, suggesting a potential functional differentiation in the role of Global SOs in S2 vs. SWS. We interpret our results as supporting the potential functional difference of Global and non-Global SOs in sleep dynamics.

John EK, Potesta CV, Gallagher MJ, Zhou C. Artificial sleep-like up/down-states induce synaptic plasticity in cortical neurons from mouse brain slices

During non-rapid eye movement (NREM) sleep, cortical neuron activity alternates between a depolarized (firing, up-state) and a hyperpolarized state (down-state) coinciding with delta electroencephalogram (EEG) slow-wave oscillation (SWO, 0. 5-4 Hz) in vivo. Recently, we have found that artificial sleep-like up/down-states can potentiate synaptic strength in layer V cortical neurons ex vivo. Using mouse coronal brain slices, whole cell voltage-clamp recordings were made from layer V cortical pyramidal neurons to record spontaneous excitatory synaptic currents (sEPSCs) and inhibitory synaptic currents (sIPSCs). Artificial sleep-like up/down-states (as SWOs, 0.5 Hz, 10 min, current clamp mode) were induced by injecting sinusoidal currents into layer V cortical neurons. Baseline pre-SWO recordings were recorded for 5 min and post-SWO recordings for at least 25-30 min. Compared to pre-SWO sEPSCs or sIPSCs, post-SWO sEPSCs or sIPSCs in layer V cortical neurons exhibited significantly larger amplitudes and a higher frequency for 30 min. This finding suggests that both sEPSCs and sIPSCs could be potentiated in layer V cortical neurons by the low-level activity of SWOs, and sEPSCs and sIPSCs maintained a balance in layer V cortical neurons during pre- and post-SWO periods. Overall, this study presents an ex vivo method to show SWO's ability to induce synaptic plasticity in layer V cortical neurons, which may underlie sleep-related synaptic potentiation for sleep-related memory consolidation in vivo.

Brain Stimulation for Improving Sleep and Memory

Over the past few decades, the importance of sleep has become increasingly recognized for many physiologic functions, including cognition. Many studies have reported the deleterious effect of sleep loss or sleep disruption on cognitive performance. Beyond ensuring adequate sleep quality and duration, discovering methods to enhance sleep to augment its restorative effects is important to improve learning in many populations, such as the military, students, age-related cognitive decline, and cognitive disorders.

Sleep loss and addiction

Reducing sleep hours is a risk factor for developing cardiovascular, metabolic, and psychiatric disorders. Furthermore, previous studies have shown that reduction in sleep time is a factor that favors relapse in addicted patients. Additionally, animal models have demonstrated that both sleep restriction and sleep deprivation increase the preference for alcohol, methylphenidate, and the self-administration of cocaine. Therefore, the present review discusses current knowledge about the influence of sleep hours reduction on addictivebehaviors; likewise, we discuss the neuronal basis underlying the sleep reduction-addiction relationship, like the role of the orexin and dopaminergic system and neuronal plasticity (i.e., delta FosB expression). Potentially, chronic sleep restriction could increase brain vulnerability and promote addictive behavior.

A Biologically Inspired Neural Network Model to Gain Insight Into the Mechanisms of Post-Traumatic Stress Disorder and Eye Movement Desensitization and Reprocessing Therapy

Eye movement desensitization and reprocessing (EMDR) therapy is a well-established therapeutic method to treat post-traumatic stress disorder (PTSD). However, how EMDR exerts its therapeutic action has been studied in many types of research but still needs to be completely understood. This is in part due to limited knowledge of the neurobiological mechanisms underlying EMDR, and in part to our incomplete understanding of PTSD. In order to model PTSD, we used a biologically inspired computational model based on firing rate units, encompassing the cortex, hippocampus, and amygdala. Through the modulation of its parameters, we fitted real data from patients treated with EMDR or classical exposure therapy. This allowed us to gain insights into PTSD mechanisms and to investigate how EMDR achieves trauma remission.

Spatiotemporal patterns of spontaneous brain activity: a mini-review

The brain exists in a state of constant activity in the absence of any external sensory input. The spatiotemporal patterns of this spontaneous brain activity have been studied using various recording and imaging techniques. This has enabled considerable progress to be made in elucidating the cellular and network mechanisms that are involved in the observed spatiotemporal dynamics. This mini-review outlines different spatiotemporal dynamic patterns that have been identified in four commonly used modalities: electrophysiological recordings, optical imaging, functional magnetic resonance imaging, and electroencephalography. Signal sources for each modality, possible sources of the observed dynamics, and future directions are also discussed.

Brain Synchronization and Multivariate Autoregressive (MVAR) Modeling in Cognitive Neurodynamics

This paper is a review of cognitive neurodynamics research as it pertains to recent advances in Multivariate Autoregressive (MVAR) modeling. Long-range synchronization between the frontoparietal network (FPN) and forebrain subcortical systems occurs when multiple neuronal actions are coordinated across time (Chafee and Goldman-Rakic, 1998), resulting in large-scale measurable activity in the EEG. This paper reviews the power and advantages of the MVAR method to analyze long-range synchronization between brain regions (Kaminski et al., 2016; Kaminski and Blinowska, 2017). It explores the synchronization expressed in neurocognitive networks that is observable in the local field potential (LFP), an EEG-like signal, and in fMRI time series. In recent years, the surge in MVAR modeling in cognitive neurodynamics experiments has highlighted the effectiveness of the method, particularly in analyzing continuous neural signals such as EEG and fMRI (Pereda et al., 2005). MVAR modeling has been particularly useful in identifying causality, a multichannel time-series measure that can only be consistently computed with multivariate processes. Due to the multivariate nature of neuronal communication, multiple non-linear multivariate-analysis models are successful, presenting results with much greater accuracy and speed than non-linear univariate-analysis methods. Granger’s framework provides causal information about neuronal flow using neural time and frequency analysis, comprising the basis of the MVAR model. Recent advancements in MVAR modeling have included Directed Transfer Function (DTF) and Partial Directed Coherence (PDC), multivariate methods based on MVAR modeling that are capable of determining causal influences and directed propagation of EEG activity. The related Granger causality is an increasingly popular tool for measuring directed functional interactions from time series data.

Relations between sleep patterns early in life and brain development: A review

Sleep supports healthy cognitive functioning in adults. Over the past decade, research has emerged advancing our understanding of sleep’s role in cognition during development. Infancy and early childhood are marked by unique changes in sleep physiology and sleep patterns as children transition from biphasic to monophasic sleep. Growing evidence suggests that, during development, there are parallel changes in sleep and the brain and that sleep may modulate brain structure and activity and vice versa. In this review, we survey studies of sleep and brain development across childhood. By summarizing these findings, we provide a unique understanding of the importance of healthy sleep for healthy brain and cognitive development. Moreover, we discuss gaps in our understanding, which will inform future research.

State-dependent effects of neural stimulation on brain function and cognition

Invasive and non-invasive brain stimulation methods are widely used in neuroscience to establish causal relationships between distinct brain regions and the sensory, cognitive and motor functions they subserve. When combined with concurrent brain imaging, such stimulation methods can reveal patterns of neuronal activity responsible for regulating simple and complex behaviours at the level of local circuits and across widespread networks. Understanding how fluctuations in physiological states and task demands might influence the effects of brain stimulation on neural activity and behaviour is at the heart of how we use these tools to understand cognition. Here we review the concept of such 'state-dependent' changes in brain activity in response to neural stimulation, and consider examples from research on altered states of consciousness (for example, sleep and anaesthesia) and from task-based manipulations of selective attention and working memory. We relate relevant findings from non-invasive methods used in humans to those obtained from direct electrical and optogenetic stimulation of neuronal ensembles in animal models. Given the widespread use of brain stimulation as a research tool in the laboratory and as a means of augmenting or restoring brain function, consideration of the influence of changing physiological and cognitive states is crucial for increasing the reliability of these interventions.

Order matters: sleep spindles contribute to memory consolidation only when followed by rapid-eye-movement sleep

Sleep is known to benefit memory consolidation, but little is known about the contribution of sleep stages within the sleep cycle. The sequential hypothesis proposes that memories are first replayed during nonrapid-eye-movement (NREM or N) sleep and then integrated into existing networks during rapid-eye-movement (REM or R) sleep, two successive critical steps for memory consolidation. However, it lacks experimental evidence as N always precedes R sleep in physiological conditions. We tested this sequential hypothesis in patients with central hypersomnolence disorder, including patients with narcolepsy who present the unique, anti-physiological peculiarity of frequently falling asleep in R sleep before entering N sleep. Patients performed a visual perceptual learning task before and after daytime naps stopped after one sleep cycle, starting in N or R sleep and followed by the other stage (i.e. N-R vs. R-N sleep sequence). We compared over-nap changes in performance, reflecting memory consolidation, depending on the sleep sequence during the nap. Thirty-six patients who slept for a total of 67 naps were included in the analysis. Results show that sleep spindles are associated with memory consolidation only when N is followed by R sleep, that is in physiologically ordered N-R naps, thus providing support to the sequential hypothesis in humans. In addition, we found a negative effect of rapid-eye-movements in R sleep on perceptual consolidation, highlighting the complex role of sleep stages in the balance to remember and to forget.

Tinnitus: at a crossroad between phantom perception and sleep

Sensory disconnection from the environment is a hallmark of sleep and is crucial for sleep maintenance. It remains unclear, however, whether internally generated percepts—phantom percepts—may overcome such disconnection and, in turn, how sleep and its effect on sensory processing and brain plasticity may affect the function of the specific neural networks underlying such phenomena. A major hurdle in addressing this relationship is the methodological difficulty to study sensory phantoms, due to their subjective nature and lack of control over the parameters or neural activity underlying that percept. Here, we explore the most prevalent phantom percept, subjective tinnitus—or tinnitus for short—as a model to investigate this. Tinnitus is the permanent perception of a sound with no identifiable corresponding acoustic source. This review offers a novel perspective on the functional interaction between brain activity across the sleep–wake cycle and tinnitus. We discuss characteristic features of brain activity during tinnitus in the awake and the sleeping brain and explore its effect on sleep functions and homeostasis. We ask whether local changes in cortical activity in tinnitus may overcome sensory disconnection and prevent the occurrence of global restorative sleep and, in turn, how accumulating sleep pressure may temporarily alleviate the persistence of a phantom sound. Beyond an acute interaction between sleep and neural activity, we discuss how the effects of sleep on brain plasticity may contribute to aberrant neural circuit activity and promote tinnitus consolidation. Tinnitus represents a unique window into understanding the role of sleep in sensory processing. Clarification of the underlying relationship may offer novel insights into therapeutic interventions in tinnitus management.

Slow oscillation–spindle coupling strength predicts real-life gross-motor learning in adolescents and adults

Previously, we demonstrated that precise temporal coordination between slow oscillations (SOs) and sleep spindles indexes declarative memory network development (Hahn et al., 2020). However, it is unclear whether these findings in the declarative memory domain also apply in the motor memory domain. Here, we compared adolescents and adults learning juggling, a real-life gross-motor task. Juggling performance was impacted by sleep and time of day effects. Critically, we found that improved task proficiency after sleep lead to an attenuation of the learning curve, suggesting a dynamic juggling learning process. We employed individualized cross-frequency coupling analyses to reduce inter- and intragroup variability of oscillatory features. Advancing our previous findings, we identified a more precise SO–spindle coupling in adults compared to adolescents. Importantly, coupling precision over motor areas predicted overnight changes in task proficiency and learning curve, indicating that SO–spindle coupling relates to the dynamic motor learning process. Our results provide first evidence that regionally specific, precisely coupled sleep oscillations support gross-motor learning.

Sleep and homeostatic control of plasticity

Sleep homeostasis is a complex neurobiologic phenomenon involving a number of molecular pathways, neurotransmitter release, synaptic activity, and factors modulating neural networks. Sleep plasticity allows for homeostatic optimization of neural networks and the replay-based consolidation of specific circuits, especially important for cognition, behavior, and information processing. Furthermore, research is currently moving from an essentially brain-focused to a more comprehensive view involving other systems, such as the immune system, hormonal status, and metabolic pathways. When dysfunctional, these systems contribute to sleep loss and fragmentation as well as to sleep need. In this chapter, the implications of neural plasticity and sleep homeostasis for the diagnosis and treatment of some major sleep disorders, such as insomnia and sleep deprivation, obstructive sleep apnea syndrome, restless legs syndrome, REM sleep behavior disorder, and narcolepsy are discussed in detail with their therapeutical implications. This chapter highlights that sleep is necessary for the maintenance of an optimal brain function and is sensitive to both genetic background and environmental enrichment. Even in pathologic conditions, sleep acts as a resilient plastic state that consolidates prior information and prioritizes network activity for efficient brain functioning.

Sleep promotes the formation of dendritic filopodia and spines near learning-inactive existing spines

Changes in synaptic connections are believed to underlie long-term memory storage. Previous studies have suggested that sleep is important for synapse formation after learning, but how sleep is involved in the process of synapse formation remains unclear. To address this question, we used transcranial two-photon microscopy to investigate the effect of postlearning sleep on the location of newly formed dendritic filopodia and spines of layer 5 pyramidal neurons in the primary motor cortex of adolescent mice. We found that newly formed filopodia and spines were partially clustered with existing spines along individual dendritic segments 24 h after motor training. Notably, posttraining sleep was critical for promoting the formation of dendritic filopodia and spines clustered with existing spines within 8 h. A fraction of these filopodia was converted into new spines and contributed to clustered spine formation 24 h after motor training. This sleep-dependent spine formation via filopodia was different from retraining-induced new spine formation, which emerged from dendritic shafts without prior presence of filopodia. Furthermore, sleep-dependent new filopodia and spines tended to be formed away from existing spines that were active at the time of motor training. Taken together, these findings reveal a role of postlearning sleep in regulating the number and location of new synapses via promoting filopodial formation.

Molecular Mechanisms of Memory Consolidation That Operate During Sleep

The role of sleep for brain function has been in the focus of interest for many years. It is now firmly established that sleep and the corresponding brain activity is of central importance for memory consolidation. Less clear are the underlying molecular mechanisms and their specific contribution to the formation of long-term memory. In this review, we summarize the current knowledge of such mechanisms and we discuss the several unknowns that hinder a deeper appreciation of how molecular mechanisms of memory consolidation during sleep impact synaptic function and engram formation.

Sleep-learning impairs subsequent awake-learning

Although we can learn new information while asleep, we usually cannot consciously remember the sleep-formed memories - presumably because learning occurred in an unconscious state. Here, we ask whether sleep-learning expedites the subsequent awake-learning of the same information. To answer this question, we reanalyzed data (Züst et al., 2019, Curr Biol) from napping participants, who learned new semantic associations between pseudowords and translation-words (guga-ship) while in slow-wave sleep. They retrieved sleep-formed associations unconsciously on an implicit memory test following awakening. Then, participants took five runs of paired-associative learning to probe carry-over effects of sleep-learning on awake-learning. Surprisingly, sleep-learning diminished awake-learning when participants learned semantic associations that were congruent to sleep-learned associations (guga-boat). Yet, learning associations that conflicted with sleep-learned associations (guga-coin) was unimpaired relative to learning new associations (resun-table; baseline). We speculate that the impeded wake-learning originated in a deficient synaptic downscaling and resulting synaptic saturation in neurons that were activated during both sleep-learning and awake-learning.

Differential Effects of a Nap on Motor Sequence Learning-Related Functional Connectivity Between Young and Older Adults

In older adults, motor sequence learning (MSL) is largely intact. However, consolidation of newly learned motor sequences is impaired compared to younger adults, and there is evidence that brain areas supporting enhanced consolidation via sleep degrade with age. It is known that brain activity in hippocampal-cortical-striatal areas is important for sleep-dependent, off-line consolidation of motor-sequences. Yet, the intricacies of how both age and sleep alter communication within this network of brain areas, which facilitate consolidation, are not known. In this study, 37 young (age 20-35) and 49 older individuals (age 55-75) underwent resting state functional magnetic resonance imaging (fMRI) before and after training on a MSL task as well as after either a nap or a period of awake rest. Young participants who napped showed strengthening of functional connectivity (FC) between motor, striatal, and hippocampal areas, compared to older subjects regardless of sleep condition. Follow-up analyses revealed this effect was driven by younger participants who showed an increase in FC between striatum and motor cortices, as well as older participants who showed decreased FC between the hippocampus, striatum, and precuneus. Therefore, different effects of sleep were observed in younger vs. older participants, where young participants primarily showed increased communication in the striatal-motor areas, while older participants showed decreases in key nodes of the default mode network and striatum. Performance gains correlated with FC changes in young adults, and this association was much greater in participants who napped compared to those who stayed awake. Performance gains also correlated with FC changes in older adults, but only in those who napped. This study reveals that, while there is no evidence of time-dependent forgetting/deterioration of performance, older adults exhibit a completely different pattern of FC changes during consolidation compared to younger adults, and lose the benefit that sleep affords to memory consolidation.

Replay in Deep Learning: Current Approaches and Missing Biological Elements

Replay is the reactivation of one or more neural patterns that are similar to the activation patterns experienced during past waking experiences. Replay was first observed in biological neural networks during sleep, and it is now thought to play a critical role in memory formation, retrieval, and consolidation. Replay-like mechanisms have been incorporated in deep artificial neural networks that learn over time to avoid catastrophic forgetting of previous knowledge. Replay algorithms have been successfully used in a wide range of deep learning methods within supervised, unsupervised, and reinforcement learning paradigms. In this letter, we provide the first comprehensive comparison between replay in the mammalian brain and replay in artificial neural networks. We identify multiple aspects of biological replay that are missing in deep learning systems and hypothesize how they could be used to improve artificial neural networks.

The Overnight Retention of Novel Metaphors Associates With Slow Oscillation-Spindle Coupling but Not With Respiratory Phase at Encoding

Accumulating evidence emphasizes the relevance of oscillatory synchrony in memory consolidation during sleep. Sleep spindles promote memory retention, especially when occurring in the depolarized upstate of slow oscillation (SO). A less studied topic is the inter-spindle synchrony, i.e. the temporal overlap and phasic coherence between spindles perceived in different electroencephalography channels. In this study, we examined how synchrony between SOs and spindles, as well as between simultaneous spindles, is associated with the retention of novel verbal metaphors. Moreover, we combined the encoding of the metaphors with respiratory phase (inhalation/exhalation) with the aim of modulating the strength of memorized items, as previous studies have shown that inhalation entrains neural activity, thereby benefiting memory in a waking condition. In the current study, 27 young adults underwent a two-night mixed-design study with a 12-h delayed memory task during both sleep and waking conditions. As expected, we found better retention over the delay containing sleep, and this outcome was strongly associated with the timing of SO–spindle coupling. However, no associations were observed regarding inter-spindle synchrony or respiratory phase. These findings contribute to a better understanding of the importance of SO–spindle coupling for memory. In contrast, the observed lack of association with inter-spindle synchrony may emphasize the local nature of spindle-related plasticity.

Coupling between motor cortex and striatum increases during sleep over long-term skill learning

The strength of cortical connectivity to the striatum influences the balance between behavioral variability and stability. Learning to consistently produce a skilled action requires plasticity in corticostriatal connectivity associated with repeated training of the action. However, it remains unknown whether such corticostriatal plasticity occurs during training itself or 'offline' during time away from training, such as sleep. Here, we monitor the corticostriatal network throughout long-term skill learning in rats and find that non-rapid-eye-movement (NREM) sleep is a relevant period for corticostriatal plasticity. We first show that the offline activation of striatal NMDA receptors is required for skill learning. We then show that corticostriatal functional connectivity increases offline, coupled to emerging consistent skilled movements, and coupled cross-area neural dynamics. We then identify NREM sleep spindles as uniquely poised to mediate corticostriatal plasticity, through interactions with slow oscillations. Our results provide evidence that sleep shapes cross-area coupling required for skill learning.

Cerebral capillary blood flow upsurge during REM sleep is mediated by A2a receptors

Sleep is generally viewed as a period of recovery, but how the supply of cerebral blood flow (CBF) changes across sleep/wake states has remained unclear. Here, we directly observe red blood cells (RBCs) within capillaries, where the actual substance exchange between the blood and neurons/glia occurs, by two-photon microscopy. Across multiple cortical areas, average capillary CBF is largely increased during rapid eye movement (REM) sleep, whereas it does not differ between periods of active wakefulness and non-REM sleep. Capillary RBC flow during REM sleep is further elevated following REM sleep deprivation, suggesting that capillary CBF reflects REM sleep pressure. At the molecular level, signaling via adenosine A2a receptors is crucial; in A2a-KO mice, capillary CBF upsurge during REM sleep is dampened, and effects of REM sleep pressure are abolished. These results provide evidence regarding the dynamics of capillary CBF across sleep/wake states and insights to the underlying mechanisms.

Occipital sleep spindles predict sequence learning in a visuo-motor task

STUDY OBJECTIVES

The brain appears to use internal models to successfully interact with its environment via active predictions of future events. Both internal models and the predictions derived from them are based on previous experience. However, it remains unclear how previously encoded information is maintained to support this function, especially in the visual domain. In the present study, we hypothesized that sleep consolidates newly encoded spatio-temporal regularities to improve predictions afterwards.

METHODS

We tested this hypothesis using a novel sequence-learning paradigm that aimed to dissociate perceptual from motor learning. We recorded behavioral performance and high-density electroencephalography (EEG) in male human participants during initial training and during testing two days later, following an experimental night of sleep (n = 16, including high-density EEG recordings) or wakefulness (n = 17).

RESULTS

Our results show sleep-dependent behavioral improvements correlated with sleep-spindle activity specifically over occipital cortices. Moreover, event-related potential (ERP) responses indicate a shift of attention away from predictable to unpredictable sequences after sleep, consistent with enhanced automaticity in the processing of predictable sequences.

CONCLUSIONS

These findings suggest a sleep-dependent improvement in the prediction of visual sequences, likely related to visual cortex reactivation during sleep spindles. Considering that controls in our experiments did not fully exclude oculomotor contributions, future studies will need to address the extent to which these effects depend on purely perceptual versus oculomotor sequence learning.

Weak closed-loop vibrational stimulation improves the depth of slow-wave sleep and declarative memory consolidation

Sleep is a unique behavioral state that affects body functions and memory. Although previous studies suggested stimulation methods to enhance sleep, a new method is required that is practical for long-term and unconstrained use by people. In this study, we used a novel closed-loop vibration stimulation method that delivers a stimulus in interaction with the intrinsic heart rhythm and examined the effects of stimulation on sleep and memory. Twelve volunteers participated in the experiment and each underwent one adaptation night and two experimental conditions-a stimulation condition (STIM) and a no-stimulation condition (SHAM). The heart rate variability analysis showed a significant increase in the normalized high frequency and the normalized low frequency significantly decreased under the STIM during the slow-wave sleep (SWS) stage. Furthermore, the synchronization ratio between the heartbeat and the stimulus significantly increased under the STIM in the SWS stage. From the electroencephalogram (EEG) spectral analysis, EEG relative powers of slow-wave activity and theta frequency bands showed a significant increase during the STIM in the SWS stage. Additionally, memory retention significantly increased under the STIM compared to the SHAM. These findings suggest that the closed-loop stimulation improves the SWS-stage depth and memory retention, and further provides a new technique for sleep enhancement.

Stability of neocortical synapses across sleep and wake states during the critical period in rats

Sleep is important for brain plasticity, but its exact function remains mysterious. An influential but controversial idea is that a crucial function of sleep is to drive widespread downscaling of excitatory synaptic strengths. Here, we used real-time sleep classification, ex vivo measurements of postsynaptic strength, and in vivo optogenetic monitoring of thalamocortical synaptic efficacy to ask whether sleep and wake states can constitutively drive changes in synaptic strength within the neocortex of juvenile rats. We found that miniature excitatory postsynaptic current amplitudes onto L4 and L2/3 pyramidal neurons were stable across sleep- and wake-dense epochs in both primary visual (V1) and prefrontal cortex (PFC). Further, chronic monitoring of thalamocortical synaptic efficacy in V1 of freely behaving animals revealed stable responses across even prolonged periods of natural sleep and wake. Together, these data demonstrate that sleep does not drive widespread downscaling of synaptic strengths during the highly plastic critical period in juvenile animals. Whether this remarkable stability across sleep and wake generalizes to the fully mature nervous system remains to be seen.