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The recurrent circuitry of the cerebral cortex generates an emergent pattern of activity that is organized into rhythmic periods of firing and silence referred to as slow oscillations (ca 1 Hz). Slow oscillations not only are dominant during slow wave sleep and deep anesthesia, but also can be generated by the isolated cortical network in vitro, being a sort of default activity of the cortical network. The cortex is densely and reciprocally connected with subcortical structures and, as a result, the slow oscillations in situ are the result of an interplay between cortex and thalamus. Due to this reciprocal connectivity and interplay, the mechanism responsible for the initiation of waves in the corticothalamocortical loop during slow oscillations is still a matter of debate. It was our objective to determine the directionality of the information flow between different layers of the cortex and the connected thalamus during spontaneous activity. With that purpose we obtained multilayer local field potentials from the rat visual cortex and from its connected thalamus, the lateral geniculate nucleus, during deep anaesthesia. We analyzed directionality of information flow between thalamus, cortical infragranular layers (5 and 6) and supragranular layers (2/3) by means of three information theoretical indicators: transfer entropy, symbolic transfer entropy and transcript mutual information. These three indicators coincided in finding that infragranular layers lead the information flow during slow oscillations both towards supragranular layers and towards the thalamus. [ABSTRACT FROM AUTHOR]

Background: Behavioral inhibition, which is a later-developing executive function (EF) and anatomically located in prefrontal areas, is impaired in attention-deficit and hyperactivity disorder (ADHD). While optimal EFs have been shown to depend on efficient sleep in healthy subjects, the impact of sleep problems, frequently reported in ADHD, remains elusive. Findings of macroscopic sleep changes in ADHD are inconsistent, but there is emerging evidence for distinct microscopic changes with a focus on prefrontal cortical regions and non-rapid eye movement (non-REM) slow-wave sleep. Recently, slow oscillations (SO) during non-REM sleep were found to be less functional and, as such, may be involved in sleep-dependent memory impairments in ADHD. Objective: By augmenting slow-wave power through bilateral, slow oscillating transcranial direct current stimulation (so-tDCS, frequency = 0.75 Hz) during non-REM sleep, we aimed to improve daytime behavioral inhibition in children with ADHD. Methods: Fourteen boys (10-14 years) diagnosed with ADHD were included. In a randomized, double-blind, cross-over design, patients received so-tDCS either in the first or in the second experimental sleep night. Inhibition control was assessed with a visuomotor go/no-go task. Intrinsic alertness was assessed with a simple stimulus response task. To control for visuomotor performance, motor memory was assessed with a finger sequence tapping task. Results: SO-power was enhanced during early non-REM sleep, accompanied by slowed reaction times and decreased standard deviations of reaction times, in the go/no-go task after so-tDCS. In contrast, intrinsic alertness, and motor memory performance were not improved by so-tDCS. Conclusion: Since behavioral inhibition but not intrinsic alertness or motor memory was improved by so-tDCS, our results suggest that lateral prefrontal slow oscillations during sleep might play a specific role for executive functioning in ADHD. [ABSTRACT FROM AUTHOR]

The < 1 Hz EEG slow oscillation (SO) is a hallmark of slow-wave sleep (SWS) and is critically involved in sleep-associated memory formation. Previous studies showed that SOs and associated memory function can be effectively enhanced by closed-loop auditory stimulation, when clicks are presented in synchrony with upcoming SO up states. However, increasing SOs and synchronized excitability also bear the risk of emerging seizure activity, suggesting the presence of mechanisms in the healthy brain that counter developing hypersynchronicity during SOs. Here, we aimed to test the limits of driving SOs through closed-loop auditory stimulation in healthy humans. Study I tested a "Driving stimulation" protocol (vs "Sham") in which trains of clicks were presented in synchrony with SO up states basically as long as an ongoing SO train was identified on-line. Study II compared Driving stimulation with a "2-Click" protocol where the maximum of stimuli delivered in a train was limited to two clicks. Stimulation was applied during SWS in the first 210 min of nocturnal sleep. Before and after sleep declarative word-pair memories were tested. Compared with the Sham control, Driving stimulation prolonged SO trains and enhanced SO amplitudes, phase-locked spindle activity, and overnight retention of word pairs (all ps < 0.05). Importantly, effects of Driving stimulation did not exceed those of 2-Click stimulation (p > 0.180), indicating the presence of a mechanism preventing the development of hypersynchronicity during SO activity. Assessment of temporal dynamics revealed a rapidly fading phase-locked spindle activity during repetitive click stimulation, suggesting that spindle refractoriness contributes to this protective mechanism. [ABSTRACT FROM AUTHOR]

Introduction Sleep complaints are common after mild traumatic brain injury (mTBI). While recent findings suggest that sleep macro-architecture is preserved in mTBI, features of non-rapid eye movement (NREM) sleep micro-architecture including electroencephalography (EEG) spectral power, slow waves (SW), and sleep spindles could be affected. This study aimed to compare NREM sleep in mTBI and healthy controls, and explore whether NREM sleep characteristics correlate with sleep complaints in these groups. Methods Thirty-four mTBI participants (mean age: 34.2 ± 11.9 yrs; post-injury delay: 10.5 ± 10.4 weeks) and 29 age-matched controls (mean age: 32.4 ± 8.2 yrs) were recruited for two consecutive nights of polysomnographic (PSG) recording. Spectral power was computed and SW and spindles were automatically detected in three derivations (F3, C3, O1) for the first three sleep cycles. Subjective sleep quality was assessed with the Pittsburgh Sleep Quality Index (PSQI). Results mTBI participants reported significant poorer sleep quality than controls on the PSQI and showed significant increases in beta power during NREM sleep at the occipital derivation only. Conversely, no group differences were found in SW and spindle characteristics. Interestingly, changes in NREM sleep characteristics were not associated with mTBI estimation of sleep quality. Conclusions Compared to controls, mTBI were found to have enhanced NREM beta power. However, these changes were not found to be associated with the subjective evaluation of sleep. While increases in beta bands during NREM sleep may be attributable to the occurrence of a brain injury, they could also be related to the presence of pain and anxiety as suggested in one prior study. [ABSTRACT FROM AUTHOR]

In response to stimulus changes, the firing rates of many neurons adapt, such that stimulus change is emphasized. Previous work has emphasized that rate adaptation can span a wide range of time scales and produce time scale invariant power law adaptation. However, neuronal rate adaptation is typically modeled using single time scale dynamics, and constructing a conductance-based model with arbitrary adaptation dynamics is nontrivial. Here, a modeling approach is developed in which firing rate adaptation, or spike frequency adaptation, can be understood as a filtering of slow stimulus statistics. Adaptation dynamics are modeled by a stimulus filter, and quantified by measuring the phase leads of the firing rate in response to varying input frequencies. Arbitrary adaptation dynamics are approximated by a set of weighted exponentials with parameters obtained by fitting to a desired filter. With this approach it is straightforward to assess the effect of multiple time scale adaptation dynamics on neural networks. To demonstrate this, single time scale and power law adaptation were added to a network model of local field potentials. Rate adaptation enhanced the slow oscillations of the network and flattened the output power spectrum, dampening intrinsic network frequencies. Thus, rate adaptation may play an important role in network dynamics. [ABSTRACT FROM AUTHOR]

Background Cortical networks undergo large-scale switching between states of increased or decreased activity in normal sleep and cognition as well as in pathological conditions such as epilepsy. We previously found that focal hippocampal seizures in rats induce increased neuronal firing and cerebral blood flow in subcortical structures including the lateral septal area, along with frontal cortical slow oscillations resembling slow wave sleep. In addition, stimulation of the lateral septum in the absence of a seizure resulted in cortical deactivation with slow oscillations. Hypothesis We hypothesized that lateral septal activation might cause neocortical deactivation indirectly, possibly through impaired subcortical arousal. But how does subcortical stimulation cause slow wave activity in frontal cortex? How do arousal neurotransmitter levels (e.g. acetylcholine) change in cortex during the excitation of inhibitory projection nuclei? Methods and results In the current study, we used simultaneous electrophysiology and enzyme-based amperometry in a rat model, and found a decrease in choline, along with slow wave activity in orbital frontal cortex during lateral septal stimulation in the absence of seizures. In contrast, the choline signal and local field potential in frontal cortex had no significant changes when stimulating the hippocampus, but showed increased choline and decreased slow wave activity with an arousal stimulus produced by toe pinch. Conclusions These findings indicate that the activation of subcortical inhibitory structures (such as lateral septum) can depress subcortical cholinergic arousal. This mechanism may play an important role in large-scale transitions of cortical activity in focal seizures, as well as in normal cortical function. [ABSTRACT FROM AUTHOR]

Our previous experiments showed that sleep in rats enhances consolidation of hippocampus dependent episodic-like memory, i.e. the ability to remember an event bound into specific spatio-temporal context. Here we tested the hypothesis that this enhancing effect of sleep is linked to the occurrence of slow oscillatory and spindle activity during slow wave sleep (SWS). Rats were tested on an episodic-like memory task and on three additional tasks covering separately the where (object place recognition), when (temporal memory), and what (novel object recognition) components of episodic memory. In each task, the sample phase (encoding) was followed by an 80-min retention interval that covered either a period of regular morning sleep or sleep deprivation. Memory during retrieval was tested using preferential exploration of novelty vs. familiarity. Consistent with previous findings, the rats which had slept during the retention interval showed significantly stronger episodic-like memory and spatial memory, and a trend of improved temporal memory (although not significant). Object recognition memory was similarly retained across sleep and sleep deprivation retention intervals. Recall of episodic-like memory was associated with increased slow oscillatory activity (0.85–2.0 Hz) during SWS in the retention interval. Spatial memory was associated with increased proportions of SWS. Against our hypothesis, a relationship between spindle activity and episodic-like memory performance was not detected, but spindle activity was associated with object recognition memory. The results provide support for the role of SWS and slow oscillatory activity in consolidating hippocampus-dependent memory, the role of spindles in this process needs to be further examined. [ABSTRACT FROM AUTHOR]

Large-amplitude sleep slow oscillations group faster neuronal oscillations and are of functional relevance for memory performance. However, relatively little is known about the impact of slow oscillations on functionally coupled networks. Here, we provide a comprehensive view on how human slow oscillatory dynamics influence various measures of brain processing. We demonstrate that slow oscillations coordinate interregional cortical communication, as assessed by phase synchrony in the sleep spindle frequency range and cross-frequency coupling between spindle and beta activity. Furthermore, we show that the organizing role of slow oscillations is restricted to circumscribed topographical areas. These findings add importantly to our basic understanding of the orchestrating role of slow oscillations. In addition, they are of considerable relevance for accounts of sleep-dependent memory reprocessing and consolidation. [ABSTRACT FROM AUTHOR]

We observed an unusual modulatory phenomenon in the electroencephalogram (EEG) of pediatric patients with acquired brain injury. The modulation is orders of magnitude slower than the fast EEG background activity, necessitating new analysis procedures to systematically detect and quantify the phenomenon. We propose a method for analyzing spatial and temporal relationships associated with slow, narrowband modulation of EEG. We extract envelope signals from physiological frequency bands of EEG. Then, we construct a sparse representation of the spectral content of the envelope signal across sliding windows. For the latter, we use an augmented LASSO regression to incorporate spatial and temporal filtering into the solution. The method can be applied to windows of variable length, depending on the desired frequency resolution. The sparse estimates of the envelope power spectra enable the detection of narrowband modulation in the millihertz frequency range. Subsequently, we are able to assess non-stationarity in the frequency and spatial relationships across channels. The method can be paired with unsupervised anomaly detection to identify windows with significant modulation. We validated such findings by applying our method to a control set of EEGs. To our knowledge, no methods have been previously proposed to quantify second order modulation at such disparate time-scales. We provide a general EEG analysis framework capable of detecting signal content below 0.1 Hz, which is especially germane to clinical recordings that may contain multiple hours worth of continuous data. • We propose a novel method to quantify very slow (< 0.01 Hz) EEG modulation as a function of time. • Our method enables tracking of the dominant modulation frequency and spatial distribution. • Paired with unsupervised anomaly detection, the method allows for detection of significant modulation. • We successfully applied our methods to EEG from neurocritical care patients and controls. [ABSTRACT FROM AUTHOR]

Memory consolidation—the transformation of labile memory traces into stable long-term representations—is facilitated by post-learning sleep. Computational and biophysical models suggest that sleep spindles may play a key mechanistic role for consolidation, igniting structural changes at cortical sites involved in prior learning. Here, we tested the resulting prediction that spindles are most pronounced over learning-related cortical areas and that the extent of this learning-spindle overlap predicts behavioral measures of memory consolidation. Using high-density scalp electroencephalography (EEG) and polysomnography (PSG) in healthy volunteers, we first identified cortical areas engaged during a temporospatial associative memory task (power decreases in the alpha/beta frequency range, 6–20 Hz). Critically, we found that participant-specific topographies (i.e., spatial distributions) of post-learning sleep spindle amplitude correlated with participant-specific learning topographies. Importantly, the extent to which spindles tracked learning patterns further predicted memory consolidation across participants. Our results provide empirical evidence for a role of post-learning sleep spindles in tracking learning networks, thereby facilitating memory consolidation. • Participant-specific topographies of sleep spindles overlap with learning topographies • This overlap is driven by sleep spindle amplitudes • The overlap of learning/spindle topographies predicts memory consolidation Petzka et al. provide evidence that post-learning sleep spindles track learning-related cortical networks and thereby facilitate memory consolidation. [ABSTRACT FROM AUTHOR]

Background Slow oscillations (<1 Hz) during slow wave sleep (SWS) promote the consolidation of declarative memory. Children with attention-deficit/hyperactivity disorder (ADHD) have been shown to display deficits in sleep-dependent consolidation of declarative memory supposedly due to dysfunctional slow brain rhythms during SWS. Objective Using transcranial oscillating direct current stimulation (toDCS) at 0.75 Hz, we investigated whether an externally triggered increase in slow oscillations during early SWS elevates memory performance in children with ADHD. Methods: 12 children with ADHD underwent a toDCS and a sham condition in a double-blind crossover study design conducted in a sleep laboratory. Memory was tested using a 2D object-location task. In addition, 12 healthy children performed the same memory task in their home environment. Results Stimulation enhanced slow oscillation power in children with ADHD and boosted memory performance to the same level as in healthy children. Conclusion These data indicate that increasing slow oscillation power during sleep by toDCS can alleviate declarative memory deficits in children with ADHD. [ABSTRACT FROM AUTHOR]

The local field potential (LFP) captures different neural processes, including integrative synaptic dynamics that cannot be observed by measuring only the spiking activity of small populations. Therefore, investigating how LFP power is modulated by external stimuli can offer important insights into sensory neural representations. However, gaining such insight requires developing data-driven computational models that can identify and disambiguate the neural contributions to the LFP. Here, we investigated how networks of excitatory and inhibitory integrate-and-fire neurons responding to time-dependent inputs can be used to interpret sensory modulations of LFP spectra. We computed analytically from such models the LFP spectra and the information that they convey about input and used these analytical expressions to fit the model to LFPs recorded in V1 of anesthetized macaques (Macaca mulatta) during the presentation of color movies. Our expressions explain60%-98%of the variance of the LFP spectrum shape and its dependency upon movie scenes and we achieved this with realistic values for the best-fit parameters. In particular, synaptic best-fit parameters were compatible with experimental measurements and the predictions of firing rates, based only on the fit of LFP data, correlated with the multiunit spike rate recorded from the same location. Moreover, the parameters characterizing the input to the network across different movie scenes correlated with cross-scene changes of several image features. Our findings suggest that analytical descriptions of spiking neuron networks may become a crucial tool for the interpretation of field recordings. [ABSTRACT FROM AUTHOR]

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Seok, McDevitt, Mednick and Malerba.)

The slow wave state is a general state of quiescence interrupted by sudden bursts of activity or so-called slow wave events (SWEs). Recently, the relationship between SWEs and blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) signals was assessed in rodent models which revealed cortex-wide BOLD activation. However, it remains unclear which macroscopic signature corresponds to these specific neurophysiological events in the human brain. Therefore, we analyzed simultaneous electroencephalographic (EEG)-fMRI data during human non-REM sleep. SWEs individually detected in the EEG data were used as predictors in event-related fMRI analyses to examine the relationship between SWEs and fMRI signals. For all 10 subjects we identified significant changes in BOLD activity associated with SWEs covering substantial parts of the gray matter. As demonstrated in rodents, we observed a direct relation of a neurophysiological event to specific BOLD activation patterns. We found a correlation between the number of SWEs and the spatial extent of these BOLD activation patterns and discovered that the amplitude of the BOLD response strongly depends on the SWE amplitude. As altered SWE propagation has recently been found in neuropsychiatric diseases, it is critical to reveal the brain's physiological slow wave state networks to potentially establish early imaging biomarkers for various diseases long before disease onset., (© The Author(s) 2022. Published by Oxford University Press.)

Cerebral cortical brain networks possess a number of conspicuous features of structure and dynamics. First, these networks have an intricate, non-random organization. They are structured in a hierarchical modular fashion, from large-scale regions of the whole brain, via cortical areas and area subcompartments organized as structural and functional maps to cortical columns, and finally circuits made up of individual neurons. Second, the networks display self-organized sustained activity, which is persistent in the absence of external stimuli. At the systems level, such activity is characterized by complex rhythmical oscillations over a broadband background, while at the cellular level, neuronal discharges have been observed to display avalanches, indicating that cortical networks are at the state of self-organized criticality. We explored the relationship between hierarchical neural network organization and sustained dynamics using large-scale network modeling. It was shown that sparse random networks with balanced excitation and inhibition can sustain neural activity without external stimulation. We find that a hierarchical modular architecture can generate sustained activity better than random networks. Moreover, the system can simultaneously support rhythmical oscillations and self-organized criticality, which are not present in the respective random networks. The underlying mechanism is that each dense module cannot sustain activity on its own, but displays self-organized criticality in the presence of weak perturbations. The hierarchical modular networks provide the coupling among subsystems with self-organized criticality. These results imply that the hierarchical modular architecture of cortical networks plays an important role in shaping the ongoing spontaneous activity of the brain, potentially allowing the system to take advantage of both the sensitivityof critical state and predictability and timing of oscillations for efficient information processing.

Sleepwalkers have been shown to have an unusually high number of arousals from slow wave sleep and lower slow wave activity ( SWA) power during the night than controls. Because sleep deprivation increases the frequency of slow wave sleep ( SWS) arousals in sleepwalkers, it may also affect the expression of the homeostatic process to a greater extent than shown previously. We thus investigated SWA power as well as slow wave oscillation ( SWO) density in 10 sleepwalkers and nine controls at baseline and following 38 h of sleep deprivation. There was a significant increase in SWA during participants' recovery sleep, especially during their second non-rapid eye movement ( NREM) period. SWO density was similarly increased during recovery sleep's first two NREM periods. A fronto-central gradient in SWA and SWO was also present on both nights. However, no group differences were noted on any of the 2 nights on SWA or SWO. This unexpected result may be related to the heterogeneity of sleepwalkers as a population, as well as our small sample size. SWA pressure after extended sleep deprivation may also result in a ceiling effect in both sleepwalkers and controls. [ABSTRACT FROM AUTHOR]

Slow oscillations are electrical potential oscillations with a spectral peak frequency of ∼0.8 Hz, and hallmark the electroencephalogram during slow-wave sleep. Recent studies have indicated a causal contribution of slow oscillations to the consolidation of memories during slow-wave sleep, raising the question to what extent such oscillations can be induced by external stimulation. Here, we examined whether slow oscillations can be effectively induced by rhythmic acoustic stimulation. Human subjects were examined in three conditions: (i) with tones presented at a rate of 0.8 Hz ('0.8-Hz stimulation'); (ii) with tones presented at a random sequence ('random stimulation'); and (iii) with no tones presented in a control condition ('sham'). Stimulation started during wakefulness before sleep and continued for the first ∼90 min of sleep. Compared with the other two conditions, 0.8-Hz stimulation significantly delayed sleep onset. However, once sleep was established, 0.8-Hz stimulation significantly increased and entrained endogenous slow oscillation activity. Sleep after the 90-min period of stimulation did not differ between the conditions. Our data show that rhythmic acoustic stimulation can be used to effectively enhance slow oscillation activity. However, the effect depends on the brain state, requiring the presence of stable non-rapid eye movement sleep. [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., Competing Interests: JN, BK, DL, LL, EC, SS, JD, JC, GA No competing interests declared, (© 2022, Nicolas et al.)

Abstract: Objective: In this study we investigated the properties of infraslow oscillations (ISOs) ⩽0.05Hz and delta frequency band in the patients during the epileptic discharges and in healthy persons who performed a variety of mental and physical tasks. Methods: The EEG signals were recorded on the subjects who were divided into three groups. In the first group were patients with global epileptic seizures, while in the second group were patients with focal epileptic seizures. In the third group were healthy volunteers who performed different mental and physical tasks. Results: By analyzing EEG data of epileptic patients, we showed that frequency peak exists at 1.5Hz which is typical for general and focal epileptic seizures. In the type of general epileptic discharges the role of the slow rhythms at 0.05Hz, was particularly pronounced and their origin was in fluctuations of cortical neural network. The properties of parameter spectral coherency of ISOs in healthy persons point to their important role in brain functioning. Conclusions: Our results suggested that the role of ISOs could be extended, because they were detected in healthy human subjects during carrying out different tasks. Investigating the properties of parameter spectral coherency, we showed that in this case correlation between different regions of the brain was significantly modulated with ISOs. [Copyright &y& Elsevier]

Abstract: Memory representations are reactivated during slow-wave sleep (SWS) after learning, and these reactivations cause a beneficial effect of sleep for memory consolidation. Memory reactivations can also be externally triggered during sleep by associated cues which enhance the sleep-dependent memory consolidation process. Here, we compared in humans the influence of sleep periods (i) of 40min and (ii) of 90min without externally triggered reactivations and (iii) of externally triggered reactivations by an associated odor cue during a 40-min sleep period on the consolidation of previously learned hippocampus-dependent visuo-spatial memories. We show that external reactivation by an odor cue during the 40-min sleep period enhanced memory stability to the same extent as 90min of sleep without odor reactivation. In contrast, 40min of sleep without external reactivations were not sufficient to benefit memory. In the 90-min sleep condition, memory enhancements were associated with time spent in SWS and were independent of the presence or absence of REM sleep. These results suggest that the efficacy of hippocampus-dependent memory consolidation depends on the duration of sleep and particularly SWS. External reactivation cues can accelerate the consolidation process even during shorter sleep episodes. [Copyright &y& Elsevier]

Abstract: Various studies suggest that non-rapid eye movement (NREM) sleep, especially slow-wave sleep (SWS), is vital to the consolidation of declarative memories. However, sleep stage 2 (S2), which is the other NREM sleep stage besides SWS, has gained only little attention. The current study investigated whether S2 during an afternoon nap contributes to the consolidation of declarative memories. Participants learned associations between faces and cities prior to a brief nap. A cued recall test was administered before and following the nap. Spindle, delta and slow oscillation activity was recorded during S2 in the nap following learning and in a control nap. Increases in spindle activity, delta activity, and slow oscillation activity in S2 in the nap following learning compared to the control nap were associated with enhanced retention of face-city associations. Furthermore, spindles tended to occur more frequently during up-states than down-states within slow oscillations during S2 following learning versus S2 of the control nap. These findings suggest that spindles, delta waves, and slow oscillations might promote memory consolidation not only during SWS, as shown earlier, but also during S2. [Copyright &y& Elsevier]

Integrated within neural circuits, astrocytes have recently been shown to modulate brain rhythms thought to mediate sleep function. Experimental evidence suggests that local impact of astrocytes on single synapses translates into global modulation of neuronal networks and behavior. We discuss these findings in the context of current conceptual models of sleep generation and function, each of which have historically focused on neural mechanisms. We highlight the implications and the challenges introduced by these results from a conceptual and computational perspective. We further provide modeling directions on how these data might extend our knowledge of astrocytic properties and sleep function. Given our evolving understanding of how local cellular activities during sleep lead to functional outcomes for the brain, further mechanistic and theoretical understanding of astrocytic contribution to these dynamics will undoubtedly be of great basic and translational benefit. [ABSTRACT FROM AUTHOR]

Rhythms at slow (<1 Hz) frequency of alternating Up and Down states occur during slow-wave sleep states, under deep anaesthesia and in cortical slices of mammals maintained in vitro. Such spontaneous oscillations result from the interplay between network reverberations nonlinearly sustained by a strong synaptic coupling and a fatigue mechanism inhibiting the neurons firing in an activity-dependent manner. Varying pharmacologically the excitability level of brain slices we exploit the network dynamics underlying slow rhythms, uncovering an intrinsic anticorrelation between Up and Down state durations. Besides, a non-monotonic change of Down state duration is also observed, which shrinks the distribution of the accessible frequencies of the slow rhythms. Attractor dynamics with activity-dependent self-inhibition predicts a similar trend even when the system excitability is reduced, because of a stability loss of Up and Down states. Hence, such cortical rhythms tend to display a maximal size of the distribution of Up/Down frequencies, envisaging the location of the system dynamics on a critical boundary of the parameter space. This would be an optimal solution for the system in order to display a wide spectrum of dynamical regimes and timescales. [ABSTRACT FROM AUTHOR]

Background: Oseltamivir phosphate (OP, Tamiflu®) is a widely used drug in the treatment of influenza with fever. However, case reports have associated OP intake with sudden abnormal behaviors. In rats infected by the influenza A virus (IAV), the electroencephalogram (EEG) displayed abnormal high-voltage amplitudes with spikes and theta oscillations at a core temperature of 39.9°C to 41°C. Until now, there has been no information describing the effect of OP on intact brain hippocampal activity of IAV-infected animals during hyperthermia. Objective: The aim of the present study was to examine the effect of OP on abnormal EEG activities in the hippocampus using the rat model of influenza-associated encephalopathy. Methods: Male Wistar rats aged 3 to 4 weeks were used for the study. Influenza A/WSN/33 strain (1 × 105 plaque forming unit in PBS, 60 μL) was applied intranasally to the rats. To characterize OP effects on the IAV-infected rats, EEG activity was studied more particularly in isoflurane-anesthetized IAV-infected rats during hyperthermia. Results: We found that the hippocampal EEG of the OP-administered (10 mg/kg) IAV-infected rats showed significant reduction of the high-voltage amplitudes and spikes, but the theta oscillations, which had been observed only at .40°C in OP non-administered rats, appeared at 38°C core temperature. Atropine (30 mg/kg) blocked the theta oscillations. Conclusion: Our data suggest that OP efficiently reduces the abnormal EEG activities after IAV infection during hyperthermia. However, OP administration may stimulate ACh release in rats at normal core temperature [ABSTRACT FROM AUTHOR]

Abstract: Objective: Sleep supports the consolidation of declarative memory. Patients with attention-deficit/hyperactivity disorder (ADHD) are not only characterized by sleep problems but also by declarative memory deficits. Given that the consolidation of declarative memory during sleep is supported by slow oscillations, which are predominantly generated by the prefrontal cortex, and that ADHD patients display low prefrontal brain activity, we assumed that ADHD patients show reduced sleep-associated consolidation of declarative memory. Methods: The impact of sleep on the consolidation of declarative memory was examined with a picture recognition task. Twelve ADHD patients (10–16years) and 12 healthy controls participated in two experimental conditions: in the sleep condition, learning was performed in the evening and picture recognition was tested after nocturnal sleep; in the wake condition, learning was conducted in the morning while retrieval took place after a day of wakefulness. Results: Analyses of recognition accuracy revealed reduced sleep-associated enhancement of recognition accuracy in ADHD. While sleep-associated enhancement of recognition accuracy was correlated with slow oscillation power during non-REM sleep in healthy controls, no such correlations were observed in ADHD. Conclusions: These data indicate a deficit in sleep-associated consolidation of declarative memory in ADHD. Moreover, our results suggest reduced functionality of slow oscillations in sleep-associated consolidation of declarative memory in ADHD. [Copyright &y& Elsevier]

Abstract: The agency facet of extraversion has been hypothesized to be based on individual differences in dopamine activity. Recent work suggests that resting posterior minus frontal electroencephalographic (EEG) slow oscillations (delta, theta) is both consistently associated with extraversion and sensitive to dopamine D2 receptor antagonist-induced changes in dopaminergic activity. Here we examine for the first time the interrelations between polymorphisms of the dopamine D2 receptor (DRD2) gene (rs1800497 [previously termed TAQ1A], rs1076560, rs1799732 [−141C Ins/Del]), extraversion and resting posterior minus frontal (Pz–Fz) slow oscillations. As predicted, we found an association between DRD2 and resting Pz–Fz slow oscillations in a sample of 141 individuals participating in an eyes-closed resting EEG session. Moreover, we replicated the association between extraversion and Pz–Fz slow oscillations. Our findings strongly suggest that the posterior–frontal distribution of slow oscillations constitutes a useful brain-based intermediate phenotype for investigating the dopaminergic basis of extraversion. [Copyright &y& Elsevier]