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Insufficient sleep impairs glucose regulation, increasing the risk of diabetes. However, what it is about the human sleeping brain that regulates blood sugar remains unknown. In an examination of over 600 humans, we demonstrate that the coupling of non-rapid eye movement (NREM) sleep spindles and slow oscillations the night before is associated with improved next-day peripheral glucose control. We further show that this sleep-associated glucose pathway may influence glycemic status through altered insulin sensitivity, rather than through altered pancreatic beta cell function. Moreover, we replicate these associations in an independent dataset of over 1,900 adults. Of therapeutic significance, the coupling between slow oscillations and spindles was the most significant sleep predictor of next-day fasting glucose, even more so than traditional sleep markers, relevant to the possibility of an electroencephalogram (EEG) index of hyperglycemia. Taken together, these findings describe a sleeping-brain-body framework of optimal human glucose homeostasis, offering a potential prognostic sleep signature of glycemic control.

Newly formed memories require strengthening and reorganisation (consolidation) after learning. The sleeping brain is hallmarked by slow oscillations (SOs), spindles, and ripples. These rhythms drive neuronal firing rates with increasing efficacy and spatiotemporal precision. While SOs set global windows of excitability and inhibition, nested spindles serve as relays that partially reactivate cortical learning networks, facilitate hippocampal ripples, and bias the content of associated memory reactivation. Ripples activate local memory circuits and drive hippocampal–cortical pattern completion to strengthen and reorganise memory traces. SO–spindle–ripple coupling thus governs consolidation processes at the neuronal (synaptic) and brain-wide (systems) level. How do passing moments turn into lasting memories? Sheltered from external tasks and distractions, sleep constitutes an optimal state for the brain to reprocess and consolidate previous experiences. Recent work suggests that consolidation is governed by the intricate interaction of slow oscillations (SOs), spindles, and ripples – electrophysiological sleep rhythms that orchestrate neuronal processing and communication within and across memory circuits. This review describes how sequential SO–spindle–ripple coupling provides a temporally and spatially fine-tuned mechanism to selectively strengthen target memories across hippocampal and cortical networks. Coupled sleep rhythms might be harnessed not only to enhance overnight memory retention, but also to combat memory decline associated with healthy ageing and neurodegenerative diseases. [ABSTRACT FROM AUTHOR]

Closed‐loop auditory stimulation (CLAS) is a brain modulation technique in which sounds are timed to enhance or disrupt endogenous neurophysiological events. CLAS of slow oscillation up‐states in sleep is becoming a popular tool to study and enhance sleep's functions, as it increases slow oscillations, evokes sleep spindles and enhances memory consolidation of certain tasks. However, few studies have examined the specific neurophysiological mechanisms involved in CLAS, in part because of practical limitations to available tools. To evaluate evidence for possible models of how sound stimulation during brain up‐states alters brain activity, we simultaneously recorded electro‐ and magnetoencephalography in human participants who received auditory stimulation across sleep stages. We conducted a series of analyses that test different models of pathways through which CLAS of slow oscillations may affect widespread neural activity that have been suggested in literature, using spatial information, timing and phase relationships in the source‐localized magnetoencephalography data. The results suggest that auditory information reaches ventral frontal lobe areas via non‐lemniscal pathways. From there, a slow oscillation is created and propagated. We demonstrate that while the state of excitability of tissue in auditory cortex and frontal ventral regions shows some synchrony with the electroencephalography (EEG)‐recorded up‐states that are commonly used for CLAS, it is the state of ventral frontal regions that is most critical for slow oscillation generation. Our findings advance models of how CLAS leads to enhancement of slow oscillations, sleep spindles and associated cognitive benefits and offer insight into how the effectiveness of brain stimulation techniques can be improved. [ABSTRACT FROM AUTHOR]

Study objectives: We aimed to build a tool which facilitates manual labeling of sleep slowoscillations (SOs) and evaluate the performance of traditional sleep SO detection algorithms on such a manually labeled data set. We sought to develop improved methods for SO detection. Method: SOs in polysomnographic recordings acquired during nap time from ten older adults were manually labeled using a custom built graphical user interface tool. Three automatic SO detection algorithms previously used in the literature were evaluated on this data set. Additional machine learning and deep learning algorithms were trained on the manually labeled data set. Results: Our custom built tool significantly decreased the time needed for manual labeling, allowing us tomanually inspect 96,277 potential SO events. The three automatic SO detection algorithms showed relatively low accuracy (max. 61.08%), but results were qualitatively similar, with SO density and amplitude increasing with sleep depth. The machine learning and deep learning algorithms showed higher accuracy (best: 99.20%) while maintaining a low prediction time. Conclusions: Accurate detection of SO events is important for investigating their role in memory consolidation. In this context, our tool and proposed methods can provide significant help in identifying these events. [ABSTRACT FROM AUTHOR]

Summary: Targeted memory reactivation (TMR) during sleep enhances memory consolidation in young adults by modulating electrophysiological markers of neuroplasticity. Interestingly, older adults exhibit deficits in motor memory consolidation, an impairment that has been linked to age‐related degradations in the same sleep features sensitive to TMR. We hypothesised that TMR would enhance consolidation in older adults via the modulation of these markers. A total of 17 older participants were trained on a motor task involving two auditory‐cued sequences. During a post‐learning nap, two auditory cues were played: one associated to a learned (i.e., reactivated) sequence and one control. Performance during two delayed re‐tests did not differ between reactivated and non‐reactivated sequences. Moreover, both associated and control sounds modulated brain responses, yet there were no consistent differences between the auditory cue types. Our results collectively demonstrate that older adults do not benefit from specific reactivation of a motor memory trace by an associated auditory cue during post‐learning sleep. Based on previous research, it is possible that auditory stimulation during post‐learning sleep could have boosted motor memory consolidation in a non‐specific manner. [ABSTRACT FROM AUTHOR]

Thanks to the availability of connectome data that map connectivity between multiple brain areas, it is now possible to build models of whole-brain activity. At the same time, advances in mean-field techniques have led to biologically based population models that integrate biophysical features such as membrane conductances or synaptic conductances. In this paper, we show that this approach can be used in brain-wide models of mice, macaques, and humans.We illustrate this approach by showing the transition from wakefulness to sleep, simulated using multi-scale models, in the three species. We compare the level of synchrony between the three species and find that the mouse brain displays a higher overall synchrony of slow waves compared to monkey and human brains. We show that these differences are due to the different delays in axonal signal propagation between regions associated with brain size differences between the species. We also make the program code—which provides a set of open-source tools for simulating large-scale activity in the cerebral cortex of mice, monkeys, and humans—publicly available. [ABSTRACT FROM AUTHOR]

Abstract Background Alzheimer’s disease (AD) patients exhibit memory disruptions and profound sleep disturbances, including disruption of deep non-rapid eye movement (NREM) sleep. Slow-wave activity (SWA) is a major restorative feature of NREM sleep and is important for memory consolidation. Methods We generated a mouse model where GABAergic interneurons could be targeted in the presence of APPswe/PS1dE9 (APP) amyloidosis, APP-GAD-Cre mice. An electroencephalography (EEG) / electromyography (EMG) telemetry system was used to monitor sleep disruptions in these animals. Optogenetic stimulation of GABAergic interneurons in the anterior cortex targeted with channelrhodopsin-2 (ChR2) allowed us to examine the role GABAergic interneurons play in sleep deficits. We also examined the effect of optogenetic stimulation on amyloid plaques, neuronal calcium as well as sleep-dependent memory consolidation. In addition, microglial morphological features and functions were assessed using confocal microscopy and flow cytometry. Finally, we performed sleep deprivation during optogenetic stimulation to investigate whether sleep restoration was necessary to slow AD progression. Results APP-GAD-Cre mice exhibited impairments in sleep architecture including decreased time spent in NREM sleep, decreased delta power, and increased sleep fragmentation compared to nontransgenic (NTG) NTG-GAD-Cre mice. Optogenetic stimulation of cortical GABAergic interneurons increased SWA and rescued sleep impairments in APP-GAD-Cre animals. Furthermore, it slowed AD progression by reducing amyloid deposition, normalizing neuronal calcium homeostasis, and improving memory function. These changes were accompanied by increased numbers and a morphological transformation of microglia, elevated phagocytic marker expression, and enhanced amyloid β (Aβ) phagocytic activity of microglia. Sleep was necessary for amelioration of pathophysiological phenotypes in APP-GAD-Cre mice. Conclusions In summary, our study shows that optogenetic targeting of GABAergic interneurons rescues sleep, which then ameliorates neuropathological as well as behavioral deficits by increasing clearance of Aβ by microglia in an AD mouse model.

A prominent and robust finding in cognitive neuroscience is the strengthening of memories during nonrapid eye movement (NREM) sleep, with slow oscillations (SOs;

Background: Alzheimer's disease (AD) patients exhibit memory disruptions and profound sleep disturbances, including disruption of deep non-rapid eye movement (NREM) sleep. Slow-wave activity (SWA) is a major restorative feature of NREM sleep and is important for memory consolidation. Methods: We generated a mouse model where GABAergic interneurons could be targeted in the presence of APPswe/PS1dE9 (APP) amyloidosis, APP-GAD-Cre mice. An electroencephalography (EEG) / electromyography (EMG) telemetry system was used to monitor sleep disruptions in these animals. Optogenetic stimulation of GABAergic interneurons in the anterior cortex targeted with channelrhodopsin-2 (ChR2) allowed us to examine the role GABAergic interneurons play in sleep deficits. We also examined the effect of optogenetic stimulation on amyloid plaques, neuronal calcium as well as sleep-dependent memory consolidation. In addition, microglial morphological features and functions were assessed using confocal microscopy and flow cytometry. Finally, we performed sleep deprivation during optogenetic stimulation to investigate whether sleep restoration was necessary to slow AD progression. Results: APP-GAD-Cre mice exhibited impairments in sleep architecture including decreased time spent in NREM sleep, decreased delta power, and increased sleep fragmentation compared to nontransgenic (NTG) NTG-GAD-Cre mice. Optogenetic stimulation of cortical GABAergic interneurons increased SWA and rescued sleep impairments in APP-GAD-Cre animals. Furthermore, it slowed AD progression by reducing amyloid deposition, normalizing neuronal calcium homeostasis, and improving memory function. These changes were accompanied by increased numbers and a morphological transformation of microglia, elevated phagocytic marker expression, and enhanced amyloid β (Aβ) phagocytic activity of microglia. Sleep was necessary for amelioration of pathophysiological phenotypes in APP-GAD-Cre mice. Conclusions: In summary, our study shows that optogenetic targeting of GABAergic interneurons rescues sleep, which then ameliorates neuropathological as well as behavioral deficits by increasing clearance of Aβ by microglia in an AD mouse model. [ABSTRACT FROM AUTHOR]

Rocking devices are widely used across different age groups to facilitate sleep. This review discusses the current literature on rocking devices and how passive vestibular stimulation influences sleep architecture, sleep oscillations, and cognitive performance. We included eight studies that conducted research with rocking devices in humans (7) and mice (1) during daytime naps and/or nighttime sleep, respectively. Overall, vestibular stimulation during sleep induced faster sleep onset, coupled with more N2 in daytime naps or N3 in nighttime sleep. Vestibular stimulation also led to more sleep spindles and better memory consolidation. Optimal stimulation intensity was around 25 cm/s2, and lower intensities led to smaller effects. The findings suggest a sweet spot for vestibular stimulation intensity, promoting deeper sleep at the cost of wakefulness or N1 sleep without compromising REM sleep. While further studies are needed to thoroughly investigate the motion parameters that drive the impact on sleep and cognitive performance, rocking devices may present a promising therapeutic tool for people with disrupted sleep patterns. [ABSTRACT FROM AUTHOR]

Interictal epileptiform discharges (IEDs) often co-occur across spatially-separated cortical regions, forming IED networks. However, the factors prompting IED propagation remain unelucidated. We hypothesized that slow oscillations (SOs) might facilitate IED propagation. Here, the amplitude and phase synchronization of SOs preceding propagating and non-propagating IEDs were compared in 22 patients with focal epilepsy undergoing intracranial electroencephalography (EEG) evaluation. Intracranial channels were categorized into the irritative zone (IZ) and normal zone (NOZ) regarding the presence of IEDs. During wakefulness, we found that pre-IED SOs within the IZ exhibited higher amplitudes for propagating IEDs than non-propagating IEDs (delta band: p = 0.001, theta band: p

Study objectivesWe aimed to build a tool which facilitates manual labeling of sleep slow oscillations (SOs) and evaluate the performance of traditional sleep SO detection algorithms on such a manually labeled data set. We sought to develop improved methods for SO detection.MethodSOs in polysomnographic recordings acquired during nap time from ten older adults were manually labeled using a custom built graphical user interface tool. Three automatic SO detection algorithms previously used in the literature were evaluated on this data set. Additional machine learning and deep learning algorithms were trained on the manually labeled data set.ResultsOur custom built tool significantly decreased the time needed for manual labeling, allowing us to manually inspect 96,277 potential SO events. The three automatic SO detection algorithms showed relatively low accuracy (max. 61.08%), but results were qualitatively similar, with SO density and amplitude increasing with sleep depth. The machine learning and deep learning algorithms showed higher accuracy (best: 99.20%) while maintaining a low prediction time.ConclusionsAccurate detection of SO events is important for investigating their role in memory consolidation. In this context, our tool and proposed methods can provide significant help in identifying these events.

Sleep is known to promote recovery after stroke. Yet it remains unclear how stroke affects neural processing during sleep. Using an experimental stroke model in rats along with electrophysiological monitoring of neural firing and sleep microarchitecture, here we show that sleep processing is altered by stroke. We find that the precise coupling of spindles to global slow oscillations (SOs), a phenomenon that is known to be important for memory consolidation, is disrupted by a pathological increase in "isolated" local delta waves. The transition from this pathological to a physiological state-with increased spindle coupling to SO-is associated with sustained performance gains during recovery. Interestingly, post-injury sleep could be pushed toward a physiological state via a pharmacological reduction of tonic γ-aminobutyric acid (GABA). Together, our results suggest that sleep processing after stroke is impaired due to an increase in delta waves and that its restoration can be important for recovery.

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.

Military veterans with posttraumatic stress disorder often complain of non-restful sleep, which further exacerbates their symptoms. Our previous study showed a deficit in Lo Deep sleep, or slow oscillations, in the PTSD population compared to healthy control sleepers. Because Lo Deep sleep is likely a stage when the brain eliminates protein debris, it is critical to find the cause and effective therapeutics to reverse Lo Deep deficiency. The current study aims to replicate and extend this finding by examining several physiological and medication factors that may be responsible for the Lo Deep deficiency. We recorded overnight sleep electroencephalogram (EEG) via a 2-channel headband device on 69 veterans in a residential treatment facility. Dried urine samples were collected at 4 time points during one day to measure melatonin, cortisol, norepinephrine and other factors. EEG data were transformed into frequency power and submitted to an automated sleep scoring algorithm. The scoring corresponds to clear spectral patterns in the overnight spectrogram but does not align exactly with traditional visual scoring stages. As expected, veterans showed decreased Lo Deep (activity

Thanks to the availability of connectome data that map connectivity between multiple brain areas, it is now possible to build models of whole-brain activity. At the same time, advances in mean-field techniques have led to biologically based population models that integrate biophysical features such as membrane conductances or synaptic conductances. In this paper, we show that this approach can be used in brain-wide models of mice, macaques, and humans.We illustrate this approach by showing the transition from wakefulness to sleep, simulated using multi-scale models, in the three species. We compare the level of synchrony between the three species and find that the mouse brain displays a higher overall synchrony of slow waves compared to monkey and human brains. We show that these differences are due to the different delays in axonal signal propagation between regions associated with brain size differences between the species. We also make the program code—which provides a set of open-source tools for simulating large-scale activity in the cerebral cortex of mice, monkeys, and humans—publicly available.

β-cells within the endocrine pancreas are fundamental for glucose, lipid and protein homeostasis. Gap junctions between cells constitute the primary coupling mechanism through which cells synchronize their electrical and metabolic activities. This evidence is still only partially investigated through models and numerical simulations. In this contribution, we explore the effect of combined electrical and metabolic coupling in β-cell clusters using a detailed biophysical model. We add heterogeneity and stochasticity to realistically reproduce β-cell dynamics and study networks mimicking arrangements of β-cells within human pancreatic islets. Model simulations are performed over different couplings and heterogeneities, analyzing emerging synchronization at the membrane potential, calcium, and metabolites levels. To describe network synchronization, we use the formalism of multiplex networks and investigate functional network properties and multiplex synchronization motifs over the structural, electrical, and metabolic layers. Our results show that metabolic coupling can support slow wave propagation in human islets, that combined electrical and metabolic synchronization is realized in small aggregates, and that metabolic long-range correlation is more pronounced with respect to the electrical one.

The impact of tau pathology on sleep microarchitecture features, including slow oscillations, spindles, and their coupling, has been understudied, despite the proposed importance of these electrophysiological features toward learning and memory. Dual orexin receptor antagonists (DORAs) are known to promote sleep, but whether and how they affect sleep microarchitecture in the setting of tauopathy is unknown. In the PS19 mouse model of tauopathy MAPT (microtubule-associated protein tau) P301S (both male and female), young PS19 mice 2–3 months old show a sleep electrophysiology signature with markedly reduced spindle duration and power and elevated slow oscillation (SO) density compared with littermate controls, although there is no significant tau hyperphosphorylation, tangle formation, or neurodegeneration at this age. With aging, there is evi)dence for sleep disruption in PS19 mice, characterized by reduced REM duration, increased non-REM and REM fragmenta)tion, and more frequent brief arousals at the macrolevel and reduced spindle density, SO density, and spindle-SO coupling at the microlevel. In ;33% of aged PS19 mice, we unexpectedly observed abnormal goal-directed behaviors in REM, including mastication, paw grasp, and forelimb/hindlimb extension, seemingly consistent with REM behavior disorder (RBD). Oral administration of DORA-12 in aged PS19 mice increased non-REM and REM duration, albeit with shorter bout lengths, and increased spindle density, spindle duration, and SO density without change to spindle–SO coupling, power in either the SO or spindle bands, or the arousal index. We observed a significant effect of DORA-12 on objective measures of RBD, thereby encouraging future exploration of DORA effects on sleep-mediated cognition and RBD treatment. [ABSTRACT FROM AUTHOR]

The dynamics of an unbalanced rotor with a vibrating suspension axis and driven by an asynchronous electric motor of limited power is considered. Stationary (near stationary) modes of rotation of the rotor with a frequency equal to the vibration frequency of the axis are investigated. An explanation of the phenomenon of vibrational capture of rotation of an unbalanced rotor is given. The proposed mechanical interpretation of the effect allows deeper understanding of the classical results and conclusions. The obtained condition for the existence of a stationary mode allows us to estimate the frequency capture interval of the rotor. The case when the mode of vibration capture of rotation is not set is considered. For such a case, an expression for the vibrational moment is obtained, as well as an equation for slow motions. Attention is drawn to the possibility of occurrence in the considered modes of motion of slow (relative to the rotation frequency) rotor oscillations with sufficiently large amplitudes. It is demonstrated that the vibrational capture mode has the property of self-regulation; allows to stabilize the rotation frequency of an unbalanced rotor during load oscillations. Attention is drawn to the fact that in this mode of motion, there is certainly a transfer of energy either from the source of vibration to the rotor, or vice versa. The Sommerfeld effect in an oscillatory system with an inertial vibration exciter is represented by vibration capture of rotation of the vibration exciter by resonant oscillations of the carrier body. The theoretical results are confirmed by numerical modelling. [ABSTRACT FROM AUTHOR]

Spindles and slow oscillations (SOs) both appear to play an important role in memory consolidation. Spindle and SO "nesting," or the temporal overlap between the two events, is believed to modulate consolidation. However, the neurophysiological processes modified by nesting remain poorly understood. We thus recorded activity from the primary motor cortex of 4 male sleeping rats to investigate how SO and spindles interact to modulate the correlation structure of neural firing. During spindles, primary motor cortex neurons fired at a preferred phase, with neural pairs demonstrating greater neural synchrony, or correlated firing, during spindle peaks. We found a direct relationship between the temporal proximity between SO and spindles, and changes to the distribution of neural correlations; nesting was associated with narrowing of the distribution, with a reduction in low- and high-correlation pairs. Such narrowing may be consistent with greater exploration of neural states. Interestingly, after animals practiced a novel motor task, pairwise correlations increased during nested spindles, consistent with targeted strengthening of functional interactions. These findings may be key mechanisms through which spindle nesting supports memory consolidation.SIGNIFICANCE STATEMENT Our analysis revealed changes in cortical spiking structure that followed the waxing and waning of spindles; firing rates increased, spikes were more phase-locked to spindle-band local field potential, and synchrony across units peaked during spindles. Moreover, we showed that the degree of nesting between spindles and slow oscillations modified the correlation structure across units by narrowing the distribution of pairwise correlations. Finally, we demonstrated that engaging in a novel motor task increased pairwise correlations during nested spindles. These phenomena suggest key mechanisms through which the interaction of spindles and slow oscillations may support sensorimotor learning. More broadly, this work helps link large-scale measures of population activity to changes in spiking structure, a critical step in understanding neuroplasticity across multiple scales.

The architecture of sleep undergoes distinct changes during childhood and early adolescence. Slow wave sleep is involved in memory processing and may support active consolidation of newly encoded representations to support the formation of abstracted "gist" memories. Here, we examined sleep and overnight memory formation in German school children (n = 33) between 7 and 15 years of age, after the encoding phase of a verbal Deese-Roediger-McDermott (DRM) task. Effects of age were analyzed on sleep electroencephalogram (EEG) signatures of memory processing during nonrapid eye movement (NonREM) sleep, and the overnight formation of veridical and gist-based memories. Increasing age decreases time spent in slow wave sleep, and slow wave activity, whereas density and amplitude of fast sleep spindles in NonREM sleep were increased. Moreover, fast spindles were more consistently and more closely coupled to the upstate of the slow oscillation in the older children. Also, veridical and gist-based recall of words after sleep increased with age. Notably, a closer slow oscillation upstate-fast spindle coupling predicted veridical recall of words, and this relationship was found independent of age. Memory performance in the sleeping children did not differ from that of an age-matched control group (n = 32) tested over a daytime wake retention interval, with adolescents even showing superior veridical recall after wake. Our findings suggest that slow oscillation-spindle coupling as a mechanism of sleep-dependent memory formation becomes increasingly relevant during childhood and early adolescence. However, wake-associated mechanisms similarly effective in forming medium-term memory exist in this age as well. Public Significance Statement: The recall of veridical and gist-based memories increases with age in children between 7 to 15 years of age. Whereas sleep compared with wakefulness did not generally alter memory performance, consolidation of veridical memory was connected to a more precise coupling of spindles to slow oscillations, two cardinal nonrapid eye movement sleep oscillations. [ABSTRACT FROM AUTHOR]

Electrophysiological sleep rhythms have been shown to impact human waking cognition, but their spatio-temporal dynamics are not understood. We investigated how slow oscillations (SOs; 0.5-4 Hz) are organized during a night of polysomnographically-recorded sleep, focusing on the scalp electrode manifold. We detected troughs of SOs at all electrodes independently and analyzed the concurrent SO troughs found in every other electrode within ±400 ms. We used a k-clustering algorithm to categorize the spatial patterns of SO trough co-occurrence into three types (Global, Local or Frontal) depending on their footprint on the electrode manifold during the considered time window. When comparing the clusters across non-rapid eye movement (NREM) sleep stages, we found a relatively larger fraction of Local SOs in slow wave sleep (SWS) compared to stage 2, and larger fraction of Global SOs in stage 2 compared to SWS. The probability of SO detection in time between two electrodes showed that SO troughs of all types co-occurred at some nearby electrodes, but only Global troughs had traveling wave profiles, moving anteriorly to posteriorly. Global SOs also had larger amplitudes at frontal electrodes and stronger coupling with fast spindles (12.5-16 Hz). Indeed, SO-spindle complexes were more likely to be detected following a Global SO trough compared to SOs in other clusters. Also, the phase-amplitude modulation of SOs over spindles (modulation vector) was higher for Global SOs across the electrode manifold. Given the recent evidence of a link between thalamocortical coupling and cognition, our findings suggest stronger cognitive relevance of Global SOs as compared to other SO types in sleep memory processing. No clinical trial is related to this study.

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. [ABSTRACT FROM AUTHOR]

Summary: Recent studies have shown that slow oscillations (SOs) can be driven by rhythmic auditory stimulation, which deepens slow‐wave sleep (SWS) and improves memory and the immune‐supportive hormonal milieu related to this sleep stage. While different attempts have been made to optimise the driving of the SOs by changing the number of click stimulations, no study has yet investigated the impact of applying more than five clicks in a row. Likewise, the importance of the type of sounds in eliciting brain responses is presently unclear. In a study of 12 healthy young participants (10 females; aged 18–26 years), we applied an established closed‐loop stimulation method, which delivered sequences of 10 pink noises, 10 pure sounds (B note of 247 Hz), 10 pronounced "a" vowels, 10 sham, 10 variable sounds, and 10 "oddball" sounds on the up phase of the endogenous SOs. By analysing area under the curve, amplitude, and event related potentials, we explored whether the nature of the sound had a differential effect on driving SOs. We showed that every stimulus in a 10‐click sequence, induces a SO response. Interestingly, all three types of sounds that we tested triggered SOs. However, pink noise elicited a more pronounced response compared to the other sounds, which was explained by a broader topographical recruitment of brain areas. Our data further suggest that varying the sounds may partially counteract habituation. [ABSTRACT FROM AUTHOR]

After experiences are encoded, post‐encoding reactivations during sleep have been proposed to mediate long‐term memory consolidation. Spindle–slow oscillation coupling during NREM sleep is a candidate mechanism through which a hippocampal‐cortical dialogue may strengthen a newly formed memory engram. Here, we investigated the role of fast spindle‐ and slow spindle–slow oscillation coupling in the consolidation of spatial memory in humans with a virtual watermaze task involving allocentric and egocentric learning strategies. Furthermore, we analyzed how resting‐state functional connectivity evolved across learning, consolidation, and retrieval of this task using a data‐driven approach. Our results show task‐related connectivity changes in the executive control network, the default mode network, and the hippocampal network at post‐task rest. The hippocampal network could further be divided into two subnetworks of which only one showed modulation by sleep. Decreased functional connectivity in this subnetwork was associated with higher spindle–slow oscillation coupling power, which was also related to better memory performance at test. Overall, this study contributes to a more holistic understanding of the functional resting‐state networks and the mechanisms during sleep associated to spatial memory consolidation. [ABSTRACT FROM AUTHOR]

I investigate the issue of the appearance of false slow oscillations of magnetization in quasi-two-dimensional metals, which arise from a careless approach to the calculation of magnetic quantum oscillations of magnetization. The absence of slow magnetization oscillations is shown numerically. The Coulomb interaction of electrons is neglected. [ABSTRACT FROM AUTHOR]

Slow oscillations, the hallmark of non‐REM sleep, and their cellular counterpart, Up and Down states (UDSs), are considered a signature of cortical dynamics that reflect the intrinsic network organization. Although previous studies have explored the role of inhibition in regulating UDSs, little is known about whether this role changes with maturation. This is surprising since both slow oscillations and UDSs exhibit significant age‐dependent alterations. To elucidate the developmental impact of GABAB and GABAA receptors on UDS activity, we conducted simultaneous local field potentials and intracellular recordings ex vivo, in brain slices of young and adult male mice, using selective blockers, CGP55845 and a non‐saturating concentration of gabazine, respectively. Blockade of both GABAB and GABAA signalling showed age‐differentiated functions. CGP55845 caused an increase in Down state duration in young animals, but a decrease in adults. Gabazine evoked spike and wave discharges in both ages; however, while young networks became completely epileptic, adults maintained the ability to generate UDSs. Furthermore, voltage clamp recordings of miniature inhibitory postsynaptic currents revealed that gabazine selectively blocks phasic currents, particularly involving postsynaptic mechanisms. The latter exhibit clear maturational changes, suggesting a different subunit composition of GABAA receptors in young vs. adult animals. Indeed, subsequent local field potential recordings under diazepam (nanomolar or micromolar concentrations) revealed that mechanisms engaging the drug's classical binding site, mediated by α1‐subunit‐containing GABAA receptors, make a bigger contribution to Up state initiation in young networks compared to adults. Taken together, these findings help clarify the mechanisms that underlie the maturation of cortical network activity and enhance our understanding regarding the emergence of neurodevelopmental disorders. Key points: Slow oscillations, the EEG hallmark of non‐REM sleep, and their cellular counterpart, Up and Down states (UDSs), are considered the default activity of the cerebral cortex and reflect the underlying neural connectivity.GABAB‐ and GABAA‐receptor‐mediated inhibition play a major role in regulating UDS activity.Although slow oscillations and UDSs exhibit significant alterations as a function of age, it is unknown how developmental changes in inhibition contribute to the developmental profile of this activity.In this study, we reveal for the first time age‐dependent effects of GABAB and GABAA signalling on UDSs. We also document the differential subunit composition of postsynaptic GABAA receptors in young and adult animals, highlighting the α1‐subunit as a major component of the age‐differentiated regulation of UDSs.These findings help clarify the mechanisms that underlie the maturation of cortical network activity, and enhance our understanding regarding the emergence of neurodevelopmental disorders. [ABSTRACT FROM AUTHOR]

Sleep manifests itself by the spontaneous emergence of characteristic oscillatory rhythms, which often time-lock and are implicated in memory formation. Here, we analyze a neural mass model of the thalamocortical loop in which the cortical node can generate slow oscillations (approximately 1 Hz) while its thalamic component can generate fast sleep spindles of σ-band activity (12–15 Hz). We study the dynamics for different coupling strengths between the thalamic and cortical nodes, for different conductance values of the thalamic node's potassium leak and hyperpolarization-activated cation-nonselective currents, and for different parameter regimes of the cortical node. The latter are listed as follows: (1) a low activity (DOWN) state with noise-induced, transient excursions into a high activity (UP) state, (2) an adaptation induced slow oscillation limit cycle with alternating UP and DOWN states, and (3) a high activity (UP) state with noise-induced, transient excursions into the low activity (DOWN) state. During UP states, thalamic spindling is abolished or reduced. During DOWN states, the thalamic node generates sleep spindles, which in turn can cause DOWN to UP transitions in the cortical node. Consequently, this leads to spindle-induced UP state transitions in parameter regime (1), thalamic spindles induced in some but not all DOWN states in regime (2), and thalamic spindles following UP to DOWN transitions in regime (3). The spindle-induced σ-band activity in the cortical node, however, is typically the strongest during the UP state, which follows a DOWN state "window of opportunity" for spindling. When the cortical node is parametrized in regime (3), the model well explains the interactions between slow oscillations and sleep spindles observed experimentally during Non-Rapid Eye Movement sleep. The model is computationally efficient and can be integrated into large-scale modeling frameworks to study spatial aspects like sleep wave propagation. [ABSTRACT FROM AUTHOR]

Stationary (near stationary) rotation modes of an unbalanced rotor with the oscillation frequency of its axis are considered. From the standpoint of I. I. Blekhman vibrational mechanics, explanations of the mechanism of vibrational capture of the rotation of an unbalanced rotor are given. It is demonstrated that the vibration moment arising as a result of oscillations tends to synchronize vibrational and rotational movements. In addition its effect in the stationary mode becomes unambiguous -- either braking or accelerating. It is shown that when a stationary rotation mode is established, large, relatively slow oscillations of rotor's speed can occur. Theoretical results are confirmed by computer simulation. [ABSTRACT FROM AUTHOR]

Targeted memory reactivation (TMR) during post-learning sleep is known to enhance motor memory consolidation but the underlying neurophysiological processes remain unclear. Here, we confirm the beneficial effect of auditory TMR on motor performance. At the neural level, TMR enhanced slow wave (SW) characteristics. Additionally, greater TMR-related phase-amplitude coupling between slow (0.5–2 Hz) and sigma (12–16 Hz) oscillations after the SW peak was related to higher TMR effect on performance. Importantly, sounds that were not associated to learning strengthened SW-sigma coupling at the SW trough. Moreover, the increase in sigma power nested in the trough of the potential evoked by the unassociated sounds was related to the TMR benefit. Altogether, our data suggest that, depending on their precise temporal coordination during post learning sleep, slow and sigma oscillations play a crucial role in either memory reinstatement or protection against irrelevant information; two processes that critically contribute to motor memory consolidation.

Sleep manifests itself by the spontaneous emergence of characteristic oscillatory rhythms, which often time-lock and are implicated in memory formation. Here, we analyze a neural mass model of the thalamocortical loop in which the cortical node can generate slow oscillations (approximately 1 Hz) while its thalamic component can generate fast sleep spindles of σ-band activity (12–15 Hz). We study the dynamics for different coupling strengths between the thalamic and cortical nodes, for different conductance values of the thalamic node's potassium leak and hyperpolarization-activated cation-nonselective currents, and for different parameter regimes of the cortical node. The latter are listed as follows: (1) a low activity (DOWN) state with noise-induced, transient excursions into a high activity (UP) state, (2) an adaptation induced slow oscillation limit cycle with alternating UP and DOWN states, and (3) a high activity (UP) state with noise-induced, transient excursions into the low activity (DOWN) state. During UP states, thalamic spindling is abolished or reduced. During DOWN states, the thalamic node generates sleep spindles, which in turn can cause DOWN to UP transitions in the cortical node. Consequently, this leads to spindle-induced UP state transitions in parameter regime (1), thalamic spindles induced in some but not all DOWN states in regime (2), and thalamic spindles following UP to DOWN transitions in regime (3). The spindle-induced σ-band activity in the cortical node, however, is typically the strongest during the UP state, which follows a DOWN state “window of opportunity” for spindling. When the cortical node is parametrized in regime (3), the model well explains the interactions between slow oscillations and sleep spindles observed experimentally during Non-Rapid Eye Movement sleep. The model is computationally efficient and can be integrated into large-scale modeling frameworks to study spatial aspects like sleep wave propagation.

During slow-wave sleep, the brain is in a self-organized regime in which slow oscillations (SOs) between up- and down-states travel across the cortex. While an isolated piece of cortex can produce SOs, the brain-wide propagation of these oscillations are thought to be mediated by the long-range axonal connections. We address the mechanism of how SOs emerge and recruit large parts of the brain using a whole-brain model constructed from empirical connectivity data in which SOs are induced independently in each brain area by a local adaptation mechanism. Using an evolutionary optimization approach, good fits to human resting-state fMRI data and sleep EEG data are found at values of the adaptation strength close to a bifurcation where the model produces a balance between local and global SOs with realistic spatiotemporal statistics. Local oscillations are more frequent, last shorter, and have a lower amplitude. Global oscillations spread as waves of silence across the undirected brain graph, traveling from anterior to posterior regions. These traveling waves are caused by heterogeneities in the brain network in which the connection strengths between brain areas determine which areas transition to a down-state first, and thus initiate traveling waves across the cortex. Our results demonstrate the utility of whole-brain models for explaining the origin of large-scale cortical oscillations and how they are shaped by the connectome. [ABSTRACT FROM AUTHOR]

During slow-wave sleep, the brain is in a self-organized regime in which slow oscillations (SOs) between up- and down-states travel across the cortex. While an isolated piece of cortex can produce SOs, the brain-wide propagation of these oscillations are thought to be mediated by the long-range axonal connections. We address the mechanism of how SOs emerge and recruit large parts of the brain using a whole-brain model constructed from empirical connectivity data in which SOs are induced independently in each brain area by a local adaptation mechanism. Using an evolutionary optimization approach, good fits to human resting-state fMRI data and sleep EEG data are found at values of the adaptation strength close to a bifurcation where the model produces a balance between local and global SOs with realistic spatiotemporal statistics. Local oscillations are more frequent, last shorter, and have a lower amplitude. Global oscillations spread as waves of silence across the undirected brain graph, traveling from anterior to posterior regions. These traveling waves are caused by heterogeneities in the brain network in which the connection strengths between brain areas determine which areas transition to a down-state first, and thus initiate traveling waves across the cortex. Our results demonstrate the utility of whole-brain models for explaining the origin of large-scale cortical oscillations and how they are shaped by the connectome.

Objective: In patients with epilepsy, interictal epileptic discharges are a diagnostic hallmark of epilepsy and represent abnormal, so‐called "irritative" activity that disrupts normal cognitive functions. Despite their clinical relevance, their mechanisms of generation remain poorly understood. It is assumed that brain activity switches abruptly, unpredictably, and supposedly randomly to these epileptic transients. We aim to study the period preceding these epileptic discharges, to extract potential proepileptogenic mechanisms supporting their expression. Methods: We used multisite intracortical recordings from patients who underwent intracranial monitoring for refractory epilepsy, the majority of whom had a mesial temporal lobe seizure onset zone. Our objective was to evaluate the existence of proepileptogenic windows before interictal epileptic discharges. We tested whether the amplitude and phase synchronization of slow oscillations (.5–4 Hz and 4–7 Hz) increase before epileptic discharges and whether the latter are phase‐locked to slow oscillations. Then, we tested whether the phase‐locking of neuronal activity (assessed by high‐gamma activity, 60–160 Hz) to slow oscillations increases before epileptic discharges to provide a potential mechanism linking slow oscillations to interictal activities. Results: Changes in widespread slow oscillations anticipate upcoming epileptic discharges. The network extends beyond the irritative zone, but the increase in amplitude and phase synchronization is rather specific to the irritative zone. In contrast, epileptic discharges are phase‐locked to widespread slow oscillations and the degree of phase‐locking tends to be higher outside the irritative zone. Then, within the irritative zone only, we observe an increased coupling between slow oscillations and neuronal discharges before epileptic discharges. Significance: Our results show that epileptic discharges occur during vulnerable time windows set up by a specific phase of slow oscillations. The specificity of these permissive windows is further reinforced by the increased coupling of neuronal activity to slow oscillations. These findings contribute to our understanding of epilepsy as a distributed oscillopathy and open avenues for future neuromodulation strategies aiming at disrupting proepileptic mechanisms. [ABSTRACT FROM AUTHOR]

Military veterans with posttraumatic stress disorder often complain of non-restful sleep, which further exacerbates their symptoms. Our previous study showed a deficit in Lo Deep sleep, or slow oscillations, in the PTSD population compared to healthy control sleepers. Because Lo Deep sleep is likely a stage when the brain eliminates protein debris, it is critical to find the cause and effective therapeutics to reverse Lo Deep deficiency. The current study aims to replicate and extend this finding by examining several physiological and medication factors that may be responsible for the Lo Deep deficiency. We recorded overnight sleep electroencephalogram (EEG) via a 2-channel headband device on 69 veterans in a residential treatment facility. Dried urine samples were collected at 4 time points during one day to measure melatonin, cortisol, norepinephrine and other factors. EEG data were transformed into frequency power and submitted to an automated sleep scoring algorithm. The scoring corresponds to clear spectral patterns in the overnight spectrogram but does not align exactly with traditional visual scoring stages. As expected, veterans showed decreased Lo Deep (activity

Background: Initial observations with the human electroencephalogram (EEG) have interpreted slow oscillations (SOs) of the EEG during deep sleep (N3) as reflecting widespread surface-negative traveling waves that originate in frontal regions and propagate across the neocortex. However, mapping SOs with a high-density array shows the simultaneous appearance of posterior positive voltage fields in the EEG at the time of the frontal-negative fields, with the typical inversion point (apparent source) around the temporal lobe.Methods: Overnight 256-channel EEG recordings were gathered from 10 healthy young adults. Individual head conductivity models were created using each participant's own structural MRI. Source localization of SOs during N3 was then performed.Results: Electrical source localization models confirmed that these large waves were created by focal discharges within the ventral limbic cortex, including medial temporal and caudal orbitofrontal cortex.Conclusions: Although the functional neurophysiology of deep sleep involves interactions between limbic and neocortical networks, the large EEG deflections of deep sleep are not created by distributed traveling waves in lateral neocortex but instead by relatively focal limbic discharges. [ABSTRACT FROM AUTHOR]

Studies have reported that exposure to radiofrequency electromagnetic fields (RF‐EMF) emitted by mobile telephony might affect specific sleep features. Possible effects of RF‐EMF emitted by Wi‐Fi networks on sleep‐dependent memory consolidation processes have not been investigated so far. The present study explored the impact of an all‐night Wi‐Fi (2.45 GHz) exposure on sleep‐dependent memory consolidation and its associated physiological correlates. Thirty young males (mean ± standard deviation [SD]: 24.1 ± 2.9 years) participated in this double‐blind, randomized, sham‐controlled crossover study. Participants spent five nights in the laboratory. The first night was an adaptation/screening night. The second and fourth nights were baseline nights, each followed consecutively by an experimental night with either Wi‐Fi (maximum: psSAR10g = <25 mW/kg; 6 min average: <6.4 mW/kg) or sham exposure. Declarative, emotional and procedural memory performances were measured using a word pair, a sequential finger tapping and a face recognition task, respectively. Furthermore, learning‐associated brain activity parameters (power spectra for slow oscillations and in the spindle frequency range) were analysed. Although emotional and procedural memory were not affected by RF‐EMF exposure, overnight improvement in the declarative task was significantly better in the Wi‐Fi condition. However, none of the post‐learning sleep‐specific parameters was affected by exposure. Thus, the significant effect of Wi‐Fi exposure on declarative memory observed at the behavioural level was not supported by results at the physiological level. Due to these inconsistencies, this result could also be a random finding. [ABSTRACT FROM AUTHOR]

Quantitative estimations of spatiotemporal complexity of cortical activity patterns are used in the clinic as a measure of consciousness levels, but the cortical mechanisms involved are not fully understood. We used a version of the perturbational complexity index (PCI) adapted to multisite recordings from the ferret (either sex) cerebral cortex in vitro (sPCI) to investigate the role of GABAergic inhibition in cortical complexity. We studied two dynamical states: slow-wave activity (synchronous state) and desynchronized activity, that express low and high causal complexity respectively. Progressive blockade of GABAergic inhibition during both regimes revealed its impact on the emergent cortical activity and on sPCI. Gradual GABAA receptor blockade resulted in higher synchronization, being able to drive the network from a desynchronized to a synchronous state, with a progressive decrease of complexity (sPCI). Blocking GABAB receptors also resulted in a reduced sPCI, in particular when in a synchronous, slow wave state. Our findings demonstrate that physiological levels of inhibition contribute to the generation of dynamical richness and spatiotemporal complexity. However, if inhibition is diminished or enhanced, cortical complexity decreases. Using a computational model, we explored a larger parameter space in this relationship and demonstrate a link between excitatory/inhibitory balance and the complexity expressed by the cortical network. [ABSTRACT FROM AUTHOR]

Sleep has been shown to be critical for memory consolidation, with some research suggesting that certain memories are prioritized for consolidation. Initial strength of a memory appears to be an important boundary condition in determining which memories are consolidated during sleep. However, the role of consolidation-mediating oscillations, such as sleep spindles and slow oscillations, in this preferential consolidation has not been explored. Here, 54 human participants (76% female) studied pairs of words to three distinct encoding strengths, with recall being tested immediately following learning and again 6 h later. Thirty-six had a 2 h nap opportunity following learning, while the remaining 18 remained awake throughout. Results showed that, across 6 h awake, weakly encoded memories deteriorated the fastest. In the nap group, however, this effect was attenuated, with forgetting rates equivalent across encoding strengths. Within the nap group, consolidation of weakly encoded items was associated with fast sleep spindle density during non-rapid eye movement sleep. Moreover, sleep spindles that were coupled to slow oscillations predicted the consolidation of weak memories independently of uncoupled sleep spindles. These relationships were unique to weakly encoded items, with spindles not correlating with memory for intermediate or strong items. This suggests that sleep spindles facilitate memory consolidation, guided in part by memory strength. [ABSTRACT FROM AUTHOR]

Background: Researchers have proposed that impaired sleep may be a causal link in the progression from Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD). Several recent findings suggest that enhancing deep sleep (N3) may improve neurological health in persons with MCI, and buffer the risk for AD. Specifically, Transcranial Electrical Stimulation (TES) of frontal brain areas, the inferred source of the Slow Oscillations (SOs) of N3 sleep, can extend N3 sleep duration and improve declarative memory for recently learned information. Recent work in our laboratory using dense array Electroencephalography (dEEG) localized the sources of SOs to anterior limbic sites - suggesting that targeting these sites with TES may be more effective for enhancing N3.Methods: For the present study, we recruited 13 healthy adults (M = 42 years) to participate in three all-night sleep EEG recordings where they received low level (0.5 mA) TES designed to target anterior limbic areas and a sham stimulation (placebo). We used a convolutional neural network, trained and tested on professionally scored EEG sleep staging, to predict sleep stages for each recording.Results: When compared to the sham session, limbic-targeted TES significantly increased the duration of N3 sleep. TES also significantly increased spectral power in the 0.5-1 Hz frequency band (relative to pre-TES epochs) in left temporoparietal and left occipital scalp regions compared to sham.Conclusion: These results suggest that even low-level TES, when specifically targeting anterior limbic sites, can increase deep (N3) sleep and thereby contribute to healthy sleep quality. [ABSTRACT FROM AUTHOR]