Are corticothalamic 'up' states fragments of wakefulness?

Altered Neocortical Dynamics in a Mouse Model of Williams-Beuren Syndrome

Williams-Beuren syndrome (WBS) is a rare neurodevelopmental disorder characterized by moderate intellectual disability and learning difficulties alongside behavioral abnormalities such as hypersociability. Several structural and functional brain alterations are characteristic of this syndrome, as well as disturbed sleep and sleeping patterns. However, the detailed physiological mechanisms underlying WBS are mostly unknown. Here, we characterized the cortical dynamics in a mouse model of WBS previously reported to replicate most of the behavioral alterations described in humans. We recorded the laminar local field potential generated in the frontal cortex during deep anesthesia and characterized the properties of the emergent slow oscillation activity. Moreover, we performed micro-electrocorticogram recordings using multielectrode arrays covering the cortical surface of one hemisphere. We found significant differences between the cortical emergent activity and functional connectivity between wild-type mice and WBS model mice. Slow oscillations displayed Up states with diminished firing rate and lower high-frequency content in the gamma range. Lower firing rates were also recorded in the awake WBS animals while performing a marble burying task and could be associated with the decreased spine density and thus synaptic connectivity in this cortical area. We also found an overall increase in functional connectivity between brain areas, reflected in lower clustering and abnormally high integration, especially in the gamma range. These results expand previous findings in humans, suggesting that the cognitive deficits characterizing WBS might be associated with reduced excitability, plus an imbalance in the capacity to functionally integrate and segregate information.

Nightmares and the Cannabinoids

The cannabinoids, Δ9 tetrahydrocannabinol and its analogue, nabilone, have been found to reliably attenuate the intensity and frequency of post-traumatic nightmares. This essay examines how a traumatic event is captured in the mind, after just a single exposure, and repeatedly replicated during the nights that follow. The adaptive neurophysiological, endocrine and inflammatory changes that are triggered by the trauma and that alter personality and behavior are surveyed. These adaptive changes, once established, can be difficult to reverse. But cannabinoids, uniquely, have been shown to interfere with all of these post-traumatic somatic adaptations. While cannabinoids can suppress nightmares and other symptoms of post-traumatic stress disorder, they are not a cure. There may be no cure. The cannabinoids may best be employed, alone, but more likely in conjunction with other agents, in the immediate aftermath of a trauma to mitigate or even abort the metabolic changes which are set in motion by the trauma and which may permanently alter the reactivity of the nervous system. Steps in this direction have already been taken.

Bridging Single Neuron Dynamics to Global Brain States

Biological neural networks produce information backgrounds of multi-scale spontaneous activity that become more complex in brain states displaying higher capacities for cognition, for instance, attentive awake versus asleep or anesthetized states. Here, we review brain state-dependent mechanisms spanning ion channel currents (microscale) to the dynamics of brain-wide, distributed, transient functional assemblies (macroscale). Not unlike how microscopic interactions between molecules underlie structures formed in macroscopic states of matter, using statistical physics, the dynamics of microscopic neural phenomena can be linked to macroscopic brain dynamics through mesoscopic scales. Beyond spontaneous dynamics, it is observed that stimuli evoke collapses of complexity, most remarkable over high dimensional, asynchronous, irregular background dynamics during consciousness. In contrast, complexity may not be further collapsed beyond synchrony and regularity characteristic of unconscious spontaneous activity. We propose that increased dimensionality of spontaneous dynamics during conscious states supports responsiveness, enhancing neural networks' emergent capacity to robustly encode information over multiple scales.

Direct pathway neurons in mouse dorsolateral striatum in vivo receive stronger synaptic input than indirect pathway neurons

Striatal projection neurons, the medium spiny neurons (MSNs), play a crucial role in various motor and cognitive functions. MSNs express either D1- or D2-type dopamine receptors and initiate the direct-pathway (dMSNs) or indirect pathways (iMSNs) of the basal ganglia, respectively. dMSNs have been shown to receive more inhibition than iMSNs from intrastriatal sources. Based on these findings, computational modeling of the striatal network has predicted that under healthy conditions dMSNs should receive more total input than iMSNs. To test this prediction, we analyzed in vivo whole cell recordings from dMSNs and iMSNs in healthy and dopamine-depleted (6OHDA) anaesthetized mice. By comparing their membrane potential fluctuations, we found that dMSNs exhibited considerably larger membrane potential fluctuations over a wide frequency range. Furthermore, by comparing the spike-triggered average membrane potentials, we found that dMSNs depolarized toward the spike threshold significantly faster than iMSNs did. Together, these findings (in particular the STA analysis) corroborate the theoretical prediction that direct-pathway MSNs receive stronger total input than indirect-pathway neurons. Finally, we found that dopamine-depleted mice exhibited no difference between the membrane potential fluctuations of dMSNs and iMSNs. These data provide new insights into the question of how the lack of dopamine may lead to behavioral deficits associated with Parkinson’s disease.

NEW & NOTEWORTHY The direct and indirect pathways of the basal ganglia originate from the D1- and D2-type dopamine receptor expressing medium spiny neurons (dMSNs and iMSNs). Theoretical results have predicted that dMSNs should receive stronger synaptic input than iMSNs. Using in vivo intracellular membrane potential data, we provide evidence that dMSNs indeed receive stronger input than iMSNs, as has been predicted by the computational model.

Hippocampal Network Oscillations Rescue Memory Consolidation Deficits Caused by Sleep Loss

Oscillations in the hippocampal network during sleep are proposed to play a role in memory storage by patterning neuronal ensemble activity. Here we show that following single-trial fear learning, sleep deprivation (which impairs memory consolidation) disrupts coherent firing rhythms in hippocampal area CA1. State-targeted optogenetic inhibition of CA1 parvalbumin-expressing (PV+) interneurons during postlearning NREM sleep, but not REM sleep or wake, disrupts contextual fear memory (CFM) consolidation in a manner similar to sleep deprivation. NREM-targeted inhibition disrupts CA1 network oscillations which predict successful memory storage. Rhythmic optogenetic activation of PV+ interneurons following learning generates CA1 oscillations with coherent principal neuron firing. This patterning of CA1 activity rescues CFM consolidation in sleep-deprived mice. Critically, behavioral and optogenetic manipulations that disrupt CFM also disrupt learning-induced stabilization of CA1 ensembles’ communication patterns in the hours following learning. Conversely, manipulations that promote CFM also promote long-term stability of CA1 communication patterns. We conclude that sleep promotes memory consolidation by generating coherent rhythms of CA1 network activity, which provide consistent communication patterns within neuronal ensembles. Most importantly, we show that this rhythmic patterning of activity is sufficient to promote long-term memory storage in the absence of sleep.

Efficient Implementation of Cerebellar Purkinje Cell With the CORDIC Algorithm on LaCSNN

Purkinje cell is an important neuron for the cerebellar information processing. In this work, we present an efficient implementation of a cerebellar Purkinje model using the Coordinate Rotation Digital Computer (CORDIC) algorithm and implement it on a Large-Scale Conductance-Based Spiking Neural Networks (LaCSNN) system with cost-efficient multiplier-less methods, which are more suitable for large-scale neural networks. The CORDIC-based Purkinje model has been compared with the original model in terms of the voltage activities, dynamic mechanisms, precision, and hardware resource utilization. The results show that the CORDIC-based Purkinje model can reproduce the same biological activities and dynamical mechanisms as the original model with slight deviation. In the aspect of the hardware implementation, it can use only logic resources, so it provides an efficient way for maximizing the FPGA resource utilization, thereby expanding the scale of neural networks that can be implemented on FPGAs.

Local Gamma Activity During Non-REM Sleep in the Context of Sensory Evoked K-Complexes

K-complexes (KCs) and Sleep Slow Oscillations (SSOs) are the EEG expression of neuronal bistability during deeper stages Non-REM sleep. They are characterized by a deep negative deflection lasting about half-a-second, sustained, at the cortical level, by a widespread and synchronized neuronal hyperpolarization (i.e., electrical silence). The phase of hyperpolarization is followed by a period of intense and synchronized neuronal firing (i.e., depolarization phase) resulting at the EEG level, in a large positive deflection (lasting about 0.5 s) and a concurrent high frequency activity (i.e., spindles). Both KCs and SSOs rather than being “local” phenomena, propagate over large sections of the cortex. These features suggest that bistability is a large-scale network phenomenon, possibly driven by a propagating excitatory activity and involving wide populations of synchronized neurons. We have recently shown that KCs and SSOs include a positive bump preceding the negative peak and that for sensory-evoked KCs this bump coincides with the P200 wave. We demonstrated that the P200 has a sensory-modality specific localization, as it is firstly elicited in the primary sensory areas related to the stimulus, which in turn receive projections from the thalamic core. We observed that the P200 acts as a propagating excitatory activity and hypothesized that it could play a key role in inducing the opening of K+ channels, and hence the cortical hyperpolarization. Here we demonstrate that the P200 is sustained by a high-frequency excitation bringing further support to its role in triggering bistability. We show that the P200 has a higher power density in gamma band as compared to the P900 coherently for all sensory modalities, and we confirm that the latter wave is crowned by higher activity in sigma-beta bands. Finally, we characterize the P200 gamma activity at the cortical level in terms of spatial localization and temporal dynamics, demonstrating that it emerges in sensory stimulus-specific primary areas and travels over the cortical mantle spreading toward fronto-central associative areas and fading concurrently with the N550 onset.

Computational Modeling of Genetic Contributions to Excitability and Neural Coding in Layer V Pyramidal Cells: Applications to Schizophrenia Pathology

Pyramidal cells in layer V of the neocortex are one of the most widely studied neuron types in the mammalian brain. Due to their role as integrators of feedforward and cortical feedback inputs, they are well-positioned to contribute to the symptoms and pathology in mental disorders-such as schizophrenia-that are characterized by a mismatch between the internal perception and external inputs. In this modeling study, we analyze the input/output properties of layer V pyramidal cells and their sensitivity to modeled genetic variants in schizophrenia-associated genes. We show that the excitability of layer V pyramidal cells and the way they integrate inputs in space and time are altered by many types of variants in ion-channel and Ca²⁺ transporter-encoding genes that have been identified as risk genes by recent genome-wide association studies. We also show that the variability in the output patterns of spiking and Ca²⁺ transients in layer V pyramidal cells is altered by these model variants. Importantly, we show that many of the predicted effects are robust to noise and qualitatively similar across different computational models of layer V pyramidal cells. Our modeling framework reveals several aspects of single-neuron excitability that can be linked to known schizophrenia-related phenotypes and existing hypotheses on disease mechanisms. In particular, our models predict that single-cell steady-state firing rate is positively correlated with the coding capacity of the neuron and negatively correlated with the amplitude of a prepulse-mediated adaptation and sensitivity to coincidence of stimuli in the apical dendrite and the perisomatic region of a layer V pyramidal cell. These results help to uncover the voltage-gated ion-channel and Ca²⁺ transporter-associated genetic underpinnings of schizophrenia phenotypes and biomarkers.

Cognition: Learning while Asleep

Is it really possible to learn new information during deep sleep? A new study suggests that implicit vocabulary binding can occur while we are snoozing. It also seems that the success of learning depends heavily upon the timing of such 'sleepy stimulation'.

PV+ Cells Enhance Temporal Population Codes but not Stimulus-Related Timing in Auditory Cortex

Spatio-temporal cortical activity patterns relative to both peripheral input and local network activity carry information about stimulus identity and context. GABAergic interneurons are reported to regulate spiking at millisecond precision in response to sensory stimulation and during gamma oscillations; their role in regulating spike timing during induced network bursts is unclear. We investigated this issue in murine auditory thalamo-cortical (TC) brain slices, in which TC afferents induced network bursts similar to previous reports in vivo. Spike timing relative to TC afferent stimulation during bursts was poor in pyramidal cells and SOM+ interneurons. It was more precise in PV+ interneurons, consistent with their reported contribution to spiking precision in pyramidal cells. Optogenetic suppression of PV+ cells unexpectedly improved afferent-locked spike timing in pyramidal cells. In contrast, our evidence suggests that PV+ cells do regulate the spatio-temporal spike pattern of pyramidal cells during network bursts, whose organization is suited to ensemble coding of stimulus information. Simulations showed that suppressing PV+ cells reduces the capacity of pyramidal cell networks to produce discriminable spike patterns. By dissociating temporal precision with respect to a stimulus versus internal cortical activity, we identified a novel role for GABAergic cells in regulating information processing in cortical networks.

Neuronal firing rates diverge during REM and homogenize during non-REM

Neurons fire at highly variable intrinsic rates and recent evidence suggests that low- and high-firing rate neurons display different plasticity and dynamics. Furthermore, recent publications imply possibly differing rate-dependent effects in hippocampus versus neocortex, but those analyses were carried out separately and with potentially important differences. To more effectively synthesize these questions, we analyzed the firing rate dynamics of populations of neurons in both hippocampal CA1 and frontal cortex under one framework that avoids the pitfalls of previous analyses and accounts for regression to the mean (RTM). We observed several consistent effects across these regions. While rapid eye movement (REM) sleep was marked by decreased hippocampal firing and increased neocortical firing, in both regions firing rate distributions widened during REM due to differential changes in high- versus low-firing rate cells in parallel with increased interneuron activity. In contrast, upon non-REM (NREM) sleep, firing rate distributions narrowed while interneuron firing decreased. Interestingly, hippocampal interneuron activity closely followed the patterns observed in neocortical principal cells rather than the hippocampal principal cells, suggestive of long-range interactions. Following these undulations in variance, the net effect of sleep was a decrease in firing rates. These decreases were greater in lower-firing hippocampal neurons but also higher-firing frontal cortical neurons, suggestive of greater plasticity in these cell groups. Our results across two different regions, and with statistical corrections, indicate that the hippocampus and neocortex show a mixture of differences and similarities as they cycle between sleep states with a unifying characteristic of homogenization of firing during NREM and diversification during REM.

Disruption of cortical network activity by the general anaesthetic isoflurane

Background

Actions of general anaesthetics on activity in the cortico-thalamic network likely contribute to loss of consciousness and disconnection from the environment. Previously, we showed that the general anaesthetic isoflurane preferentially suppresses cortically evoked synaptic responses compared with thalamically evoked synaptic responses, but how this differential sensitivity translates into changes in network activity is unclear.

Methods

We investigated isoflurane disruption of spontaneous and stimulus-induced cortical network activity using multichannel recordings in murine auditory thalamo-cortical brain slices.

Results

Under control conditions, afferent stimulation elicited short latency, presumably monosynaptically driven, spiking responses, as well as long latency network bursts that propagated horizontally through the cortex. Isoflurane (0.05-0.6 mM) suppressed spiking activity overall, but had a far greater effect on network bursts than on early spiking responses. At isoflurane concentrations >0.3 mM, network bursts were almost entirely blocked, even with increased stimulation intensity and in response to paired (thalamo-cortical + cortical layer 1) stimulation, while early spiking responses were <50% blocked. Isoflurane increased the threshold for eliciting bursts, decreased their propagation speed and prevented layer 1 afferents from facilitating burst induction by thalamo-cortical afferents.

Conclusions

Disruption of horizontal activity spread and of layer 1 facilitation of thalamo-cortical responses likely contribute to the mechanism by which suppression of cortical feedback connections disrupts sensory awareness under anaesthesia.

Sensory Processing Across Conscious and Nonconscious Brain States: From Single Neurons to Distributed Networks for Inferential Representation

Neuronal activity is markedly different across brain states: it varies from desynchronized activity during wakefulness to the synchronous alternation between active and silent states characteristic of deep sleep. Surprisingly, limited attention has been paid to investigating how brain states affect sensory processing. While it was long assumed that the brain was mostly disconnected from external stimuli during sleep, an increasing number of studies indicates that sensory stimuli continue to be processed across all brain states-albeit differently. In this review article, we first discuss what constitutes a brain state. We argue that-next to global, behavioral states such as wakefulness and sleep-there is a concomitant need to distinguish bouts of oscillatory dynamics with specific global/local activity patterns and lasting for a few hundreds of milliseconds, as these can lead to the same sensory stimulus being either perceived or not. We define these short-lasting bouts as micro-states. We proceed to characterize how sensory-evoked neural responses vary between conscious and nonconscious states. We focus on two complementary aspects: neuronal ensembles and inter-areal communication. First, we review which features of ensemble activity are conducive to perception, and how these features vary across brain states. Properties such as heterogeneity, sparsity and synchronicity in neuronal ensembles will especially be considered as essential correlates of conscious processing. Second, we discuss how inter-areal communication varies across brain states and how this may affect brain operations and sensory processing. Finally, we discuss predictive coding (PC) and the concept of multi-level representations as a key framework for understanding conscious sensory processing. In this framework the brain implements conscious representations as inferences about world states across multiple representational levels. In this representational hierarchy, low-level inference may be carried out nonconsciously, whereas high levels integrate across different sensory modalities and larger spatial scales, correlating with conscious processing. This inferential framework is used to interpret several cellular and population-level findings in the context of brain states, and we briefly compare its implications to two other theories of consciousness. In conclusion, this review article, provides foundations to guide future studies aiming to uncover the mechanisms of sensory processing and perception across brain states.

Memory corticalization triggered by REM sleep: mechanisms of cellular and systems consolidation

Once viewed as a passive physiological state, sleep is a heterogeneous and complex sequence of brain states with essential effects on synaptic plasticity and neuronal functioning. Rapid-eye-movement (REM) sleep has been shown to promote calcium-dependent plasticity in principal neurons of the cerebral cortex, both during memory consolidation in adults and during post-natal development. This article reviews the plasticity mechanisms triggered by REM sleep, with a focus on the emerging role of kinases and immediate-early genes for the progressive corticalization of hippocampus-dependent memories. The body of evidence suggests that memory corticalization triggered by REM sleep is a systemic phenomenon with cellular and molecular causes.

Absent sleep EEG spindle activity in GluA1 (Gria1) knockout mice: relevance to neuropsychiatric disorders

Sleep EEG spindles have been implicated in attention, sensory processing, synaptic plasticity and memory consolidation. In humans, deficits in sleep spindles have been reported in a wide range of neurological and psychiatric disorders, including schizophrenia. Genome-wide association studies have suggested a link between schizophrenia and genes associated with synaptic plasticity, including the Gria1 gene which codes for the GluA1 subunit of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor. Gria1^-/- mice exhibit a phenotype relevant for neuropsychiatric disorders, including reduced synaptic plasticity and, at the behavioural level, attentional deficits leading to aberrant salience. In this study we report a striking reduction of EEG power density including the spindle-frequency range (10-15 Hz) during sleep in Gria1^-/- mice. The reduction of spindle-activity in Gria1^-/- mice was accompanied by longer REM sleep episodes, increased EEG slow-wave activity in the occipital derivation during baseline sleep, and a reduced rate of decline of EEG slow wave activity (0.5-4 Hz) during NREM sleep after sleep deprivation. These data provide a novel link between glutamatergic dysfunction and sleep abnormalities in a schizophrenia-relevant mouse model.

Criteria on Balance, Stability, and Excitability in Cortical Networks for Constraining Computational Models

During ongoing and Up state activity, cortical circuits manifest a set of dynamical features that are conserved across these states. The present work systematizes these phenomena by three notions: excitability, the ability to sustain activity without external input; balance, precise coordination of excitatory and inhibitory neuronal inputs; and stability, maintenance of activity at a steady level. Slice preparations exhibiting Up states demonstrate that balanced activity can be maintained by small local circuits. While computational models of cortical circuits have included different combinations of excitability, balance, and stability, they have done so without a systematic quantitative comparison with experimental data. Our study provides quantitative criteria for this purpose, by analyzing in-vitro and in-vivo neuronal activity and characterizing the dynamics on the neuronal and population levels. The criteria are defined with a tolerance that allows for differences between experiments, yet are sufficient to capture commonalities between persistently depolarized cortical network states and to help validate computational models of cortex. As test cases for the derived set of criteria, we analyze three widely used models of cortical circuits and find that each model possesses some of the experimentally observed features, but none satisfies all criteria simultaneously, showing that the criteria are able to identify weak spots in computational models. The criteria described here form a starting point for the systematic validation of cortical neuronal network models, which will help improve the reliability of future models, and render them better building blocks for larger models of the brain.

Beta and Gamma Oscillations in Prefrontal Cortex During NMDA Hypofunction: An In Vitro Model of Schizophrenia Features

NMDA receptor (NMDAr) hypofunction has been widely used as a schizophrenia model. Decreased activation of NMDAr is associated with a disrupted excitation/inhibition balance in the prefrontal cortex and with alterations in gamma synchronization. Our aim was to investigate whether this phenomenon could be reproduced in the spontaneous oscillatory activity generated by the local prefrontal network in vitro and, if so, to explore the effects of antipsychotics on the resulting activity. Extracellular recordings were obtained from prefrontal cortex slices bathed in in vivo-like ACSF solution. Slow (<1 Hz) oscillations consisting of interspersed Up (active) and Down (silent) states spontaneously emerged. Fast-frequency oscillations (15-90 Hz) occurred during Up states. We explored the effects of the NMDAr antagonist MK-801 on the spontaneously generated activity. Bath-applied MK-801 induced a dose-dependent decrease in Up-state duration and in the frequency of Up states. However, the beta/gamma power during Up states significantly increased; this increase was in turn prevented by the antipsychotic drug clozapine. The increased beta/gamma power with NMDAr blockade implies that NMDAr activation in physiological conditions prevents hypersynchronization in this frequency range. High-frequency hypersynchronization following NMDAr blockade occurring in cortical slices suggests that-at least part of-the underlying mechanisms of this schizophrenia feature persist in the local cortical circuit, even in the absence of long-range cortical or subcortical inputs. The observed action of clozapine decreasing hypersynchronization in the local circuit may be one of the mechanisms of action of clozapine in preventing schizophrenia symptoms derived from NMDA hypofunction.

Effects of Aging on Cortical Neural Dynamics and Local Sleep Homeostasis in Mice

Healthy aging is associated with marked effects on sleep, including its daily amount and architecture, as well as the specific EEG oscillations. Neither the neurophysiological underpinnings nor the biological significance of these changes are understood, and crucially the question remains whether aging is associated with reduced sleep need or a diminished capacity to generate sufficient sleep. Here we tested the hypothesis that aging may affect local cortical networks, disrupting the capacity to generate and sustain sleep oscillations, and with it the local homeostatic response to sleep loss. We performed chronic recordings of cortical neural activity and local field potentials from the motor cortex in young and older male C57BL/6J mice, during spontaneous waking and sleep, as well as during sleep after sleep deprivation. In older animals, we observed an increase in the incidence of non-rapid eye movement sleep local field potential slow waves and their associated neuronal silent (OFF) periods, whereas the overall pattern of state-dependent cortical neuronal firing was generally similar between ages. Furthermore, we observed that the response to sleep deprivation at the level of local cortical network activity was not affected by aging. Our data thus suggest that the local cortical neural dynamics and local sleep homeostatic mechanisms, at least in the motor cortex, are not impaired during healthy senescence in mice. This indicates that powerful protective or compensatory mechanisms may exist to maintain neuronal function stable across the life span, counteracting global changes in sleep amount and architecture.

SIGNIFICANCE STATEMENT The biological significance of age-dependent changes in sleep is unknown but may reflect either a diminished sleep need or a reduced capacity to generate deep sleep stages. As aging has been linked to profound disruptions in cortical sleep oscillations and because sleep need is reflected in specific patterns of cortical activity, we performed chronic electrophysiological recordings of cortical neural activity during waking, sleep, and after sleep deprivation from young and older mice. We found that all main hallmarks of cortical activity during spontaneous sleep and recovery sleep after sleep deprivation were largely intact in older mice, suggesting that the well-described age-related changes in global sleep are unlikely to arise from a disruption of local network dynamics within the neocortex.

Distinct Functional Groups Emerge from the Intrinsic Properties of Molecularly Identified Entorhinal Interneurons and Principal Cells

Inhibitory interneurons are an important source of synaptic inputs that may contribute to network mechanisms for coding of spatial location by entorhinal cortex (EC). The intrinsic properties of inhibitory interneurons in the EC of the mouse are mostly undescribed. Intrinsic properties were recorded from known cell types, such as, stellate and pyramidal cells and 6 classes of molecularly identified interneurons (regulator of calcineurin 2, somatostatin, serotonin receptor 3a, neuropeptide Y neurogliaform (NGF), neuropeptide Y non-NGF, and vasoactive intestinal protein) in acute brain slices. We report a broad physiological diversity between and within cell classes. We also found differences in the ability to produce postinhibitory rebound spikes and in the frequency and amplitude of incoming EPSPs. To understand the source of this intrinsic variability we applied hierarchical cluster analysis to functionally classify neurons. These analyses revealed physiologically derived cell types in EC that mostly corresponded to the lines identified by biomarkers with a few unexpected and important differences. Finally, we reduced the complex multidimensional space of intrinsic properties to the most salient five that predicted the cellular biomolecular identity with 81.4% accuracy. These results provide a framework for the classification of functional subtypes of cortical neurons by their intrinsic membrane properties.

Low- and high-frequency cortical brain oscillations reflect dissociable mechanisms of concurrent speech segregation in noise

Parsing simultaneous speech requires listeners use pitch-guided segregation which can be affected by the signal-to-noise ratio (SNR) in the auditory scene. The interaction of these two cues may occur at multiple levels within the cortex. The aims of the current study were to assess the correspondence between oscillatory brain rhythms and determine how listeners exploit pitch and SNR cues to successfully segregate concurrent speech. We recorded electrical brain activity while participants heard double-vowel stimuli whose fundamental frequencies (F0s) differed by zero or four semitones (STs) presented in either clean or noise-degraded (+5 dB SNR) conditions. We found that behavioral identification was more accurate for vowel mixtures with larger pitch separations but F0 benefit interacted with noise. Time-frequency analysis decomposed the EEG into different spectrotemporal frequency bands. Low-frequency (θ, β) responses were elevated when speech did not contain pitch cues (0ST > 4ST) or was noisy, suggesting a correlate of increased listening effort and/or memory demands. Contrastively, γ power increments were observed for changes in both pitch (0ST > 4ST) and SNR (clean > noise), suggesting high-frequency bands carry information related to acoustic features and the quality of speech representations. Brain-behavior associations corroborated these effects; modulations in low-frequency rhythms predicted the speed of listeners' perceptual decisions with higher bands predicting identification accuracy. Results are consistent with the notion that neural oscillations reflect both automatic (pre-perceptual) and controlled (post-perceptual) mechanisms of speech processing that are largely divisible into high- and low-frequency bands of human brain rhythms.

Anti-correlated cortical networks arise from spontaneous neuronal dynamics at slow timescales

In the highly interconnected architectures of the cerebral cortex, recurrent intracortical loops disproportionately outnumber thalamo-cortical inputs. These networks are also capable of generating neuronal activity without feedforward sensory drive. It is unknown, however, what spatiotemporal patterns may be solely attributed to intrinsic connections of the local cortical network. Using high-density microelectrode arrays, here we show that in the isolated, primary somatosensory cortex of mice, neuronal firing fluctuates on timescales from milliseconds to tens of seconds. Slower firing fluctuations reveal two spatially distinct neuronal ensembles, which correspond to superficial and deeper layers. These ensembles are anti-correlated: when one fires more, the other fires less and vice versa. This interplay is clearest at timescales of several seconds and is therefore consistent with shifts between active sensing and anticipatory behavioral states in mice.

Back to Pupillometry: How Cortical Network State Fluctuations Tracked by Pupil Dynamics Could Explain Neural Signal Variability in Human Cognitive Neuroscience

The mammalian thalamocortical system generates intrinsic activity reflecting different states of excitability, arising from changes in the membrane potentials of underlying neuronal networks. Fluctuations between these states occur spontaneously, regularly, and frequently throughout awake periods and influence stimulus encoding, information processing, and neuronal and behavioral responses. Changes of pupil size have recently been identified as a reliable marker of underlying neuronal membrane potential and thus can encode associated network state changes in rodent cortex. This suggests that pupillometry, a ubiquitous measure of pupil dilation in cognitive neuroscience, could be used as an index for network state fluctuations also for human brain signals. Considering this variable may explain task-independent variance in neuronal and behavioral signals that were previously disregarded as noise.

Cortex-wide BOLD fMRI activity reflects locally-recorded slow oscillation-associated calcium waves

Spontaneous slow oscillation-associated slow wave activity represents an internally generated state which is characterized by alternations of network quiescence and stereotypical episodes of neuronal activity - slow wave events. However, it remains unclear which macroscopic signal is related to these active periods of the slow wave rhythm. We used optic fiber-based calcium recordings of local neural populations in cortex and thalamus to detect neurophysiologically defined slow calcium waves in isoflurane anesthetized rats. The individual slow wave events were used for an event-related analysis of simultaneously acquired whole-brain BOLD fMRI. We identified BOLD responses directly related to onsets of slow calcium waves, revealing a cortex-wide BOLD correlate: the entire cortex was engaged in this specific type of slow wave activity. These findings demonstrate a direct relation of defined neurophysiological events to a specific BOLD activity pattern and were confirmed for ongoing slow wave activity by independent component and seed-based analyses.

When a person is in a deep non-dreaming sleep, neurons in their brain alternate slowly between periods of silence and periods of activity. This gives rise to low-frequency brain rhythms called slow waves, which are thought to help stabilize memories. Slow wave activity can be detected on multiple scales, from the pattern of electrical impulses sent by an individual neuron to the collective activity of the brain’s entire outer layer, the cortex. But does slow wave activity in an individual group of neurons in the cortex affect the activity of the rest of the brain?

To find out, Schwalm, Schmid, Wachsmuth et al. took advantage of the fact that slow waves also occur under general anesthesia, and placed anesthetized rats inside miniature whole-brain scanners. A small region of cortex in each rat had been injected with a dye that fluoresces whenever the neurons in that region are active. An optical fiber was lowered into the rat’s brain to transmit the fluorescence signals to a computer. Monitoring these signals while the animals lay inside the scanner revealed that slow-wave activity in any one group of cortical neurons was accompanied by slow-wave activity across the cortex as a whole. This relationship was seen only for slow waves, and not for other brain rhythms.

Slow waves seem to occur in all species of animal with a backbone, and in both healthy and diseased brains. While it is not possible to inject fluorescent dyes into the human brain, it is possible to monitor neuronal activity using electrodes. Comparing local electrode recordings with measures of whole-brain activity from scanners could thus allow similar experiments to be performed in people. There is growing evidence – from animal models and from studies of patients – that slow waves may be altered in Alzheimer’s disease. Further work is required to determine whether detecting these changes could help diagnose disease at earlier stages, and whether reversing them may have therapeutic potential.

Shaping the Default Activity Pattern of the Cortical Network

Slow oscillations have been suggested as the default emergent activity of the cortical network. This is a low complexity state that integrates neuronal, synaptic, and connectivity properties of the cortex. Shaped by variations of physiological parameters, slow oscillations provide information about the underlying healthy or pathological network. We review how this default activity is shaped, how it acts as a powerful attractor, and how getting out of it is necessary for the brain to recover the levels of complexity associated with conscious states. We propose that slow oscillations provide a robust unifying paradigm for the study of cortical function.

Spike-Based Functional Connectivity in Cerebral Cortex and Hippocampus: Loss of Global Connectivity Is Coupled to Preservation of Local Connectivity During Non-REM Sleep

Behavioral states are commonly considered global phenomena with homogeneous neural determinants. However, recent studies indicate that behavioral states modulate spiking activity with neuron-level specificity as a function of brain area, neuronal subtype, and preceding history. Although functional connectivity also strongly depends on behavioral state at a mesoscopic level and is globally weaker in non-REM (NREM) sleep and anesthesia than wakefulness, it is unknown how neuronal communication is modulated at the cellular level. We hypothesize that, as for neuronal activity, the influence of behavioral states on neuronal coupling strongly depends on type, location, and preceding history of involved neurons. Here, we applied nonlinear, information-theoretical measures of functional connectivity to ensemble recordings with single-cell resolution to quantify neuronal communication in the neocortex and hippocampus of rats during wakefulness and sleep. Although functional connectivity (measured in terms of coordination between firing rate fluctuations) was globally stronger in wakefulness than in NREM sleep (with distinct traits for cortical and hippocampal areas), the drop observed during NREM sleep was mainly determined by a loss of inter-areal connectivity between excitatory neurons. Conversely, local (intra-area) connectivity and long-range (inter-areal) coupling between interneurons were preserved during NREM sleep. Furthermore, neuronal networks that were either modulated or not by a behavioral task remained segregated during quiet wakefulness and NREM sleep. These results show that the drop in functional connectivity during wake-sleep transitions globally holds true at the cellular level, but confine this change mainly to long-range coupling between excitatory neurons.

SIGNIFICANCE STATEMENT

Studies performed at a mesoscopic level of analysis have shown that communication between cortical areas is disrupted in non-REM sleep and anesthesia. However, the neuronal determinants of this phenomenon are not known. Here, we applied nonlinear, information-theoretical measures of functional coupling to multi-area tetrode recordings from freely moving rats to investigate whether and how brain state modulates coordination between individual neurons. We found that the previously observed drop in functional connectivity during non-REM (NREM) sleep can be explained by a decrease in coupling between excitatory neurons located in distinct brain areas. Conversely, intra-area communication and coupling between interneurons are preserved. Our results provide significant new insights into the neuron-level mechanisms responsible for the loss of consciousness occurring in NREM sleep.

Neural Markers of Responsiveness to the Environment in Human Sleep

Sleep is characterized by a loss of behavioral responsiveness. However, recent research has shown that the sleeping brain is not completely disconnected from its environment. How neural activity constrains the ability to process sensory information while asleep is yet unclear. Here, we instructed human volunteers to classify words with lateralized hand responses while falling asleep. Using an electroencephalographic (EEG) marker of motor preparation, we show how responsiveness is modulated across sleep. These modulations are tracked using classic event-related potential analyses complemented by Lempel-Ziv complexity (LZc), a measure shown to track arousal in sleep and anesthesia. Neural activity related to the semantic content of stimuli was conserved in light non-rapid eye movement (NREM) sleep. However, these processes were suppressed in deep NREM sleep and, importantly, also in REM sleep, despite the recovery of wake-like neural activity in the latter. In NREM sleep, sensory activations were counterbalanced by evoked down states, which, when present, blocked further processing of external information. In addition, responsiveness markers correlated positively with baseline complexity, which could be related to modulation in sleep depth. In REM sleep, however, this relationship was reversed. We therefore propose that, in REM sleep, endogenously generated processes compete with the processing of external input. Sleep can thus be seen as a self-regulated process in which external information can be processed in lighter stages but suppressed in deeper stages. Last, our results suggest drastically different gating mechanisms in NREM and REM sleep.

SIGNIFICANCE STATEMENT

Previous research has tempered the notion that sleepers are isolated from their environment. Here, we pushed this idea forward and examined, across all sleep stages, the brain's ability to flexibly process sensory information, up to the decision level. We extracted an EEG marker of motor preparation to determine the completion of the sensory processing chain and explored how it is constrained by baseline and evoked neural activity. In NREM sleep, slow waves elicited by stimuli appeared to block response preparation. We also used a novel analytic approach (Lempel-Ziv complexity) and showed that the ability to process external information correlates with neural complexity. A reversal of the correlation between complexity and motor indices in REM sleep suggests drastically different gating mechanisms across sleep stages.

Disconnecting Consciousness: Is There a Common Anesthetic End Point?

A quest for a systems-level neuroscientific basis of anesthetic-induced loss and return of consciousness has been in the forefront of research for the past 2 decades. Recent advances toward the discovery of underlying mechanisms have been achieved using experimental electrophysiology, multichannel electroencephalography, magnetoencephalography, and functional magnetic resonance imaging. By the careful dosing of various volatile and IV anesthetic agents to the level of behavioral unresponsiveness, both specific and common changes in functional and effective connectivity across large-scale brain networks have been discovered and interpreted in the context of how the synthesis of neural information might be affected during anesthesia. The results of most investigations to date converge toward the conclusion that a common neural correlate of anesthetic-induced unresponsiveness is a consistent depression or functional disconnection of lateral frontoparietal networks, which are thought to be critical for consciousness of the environment. A reduction in the repertoire of brain states may contribute to the anesthetic disruption of large-scale information integration leading to unconsciousness. In future investigations, a systematic delineation of connectivity changes with multiple anesthetics using the same experimental design, and the same analytical method will be desirable. The critical neural events that account for the transition between responsive and unresponsive states should be assessed at similar anesthetic doses just below and above the loss or return of responsiveness. There will also be a need to identify a robust, sensitive, and reliable measure of information transfer. Ultimately, finding a behavior-independent measure of subjective experience that can track covert cognition in unresponsive subjects and a delineation of causal factors versus correlated events will be essential to understand the neuronal basis of human consciousness and unconsciousness.

The adenosine-mediated, neuronal-glial, homeostatic sleep response

Slow wave activity (SWA) during slow wave sleep (SWS) is the best indicator of the sleep homeostasis. The intensity of the SWA observed during SWS that follows prolonged waking is directly correlated with the duration of prior waking and its intensity decays during SWS suggesting a buildup and a resolution of sleep need. This sleep-homeostasis related SWA results from a buildup and decay of extracellular adenosine that acts at neuronal adenosine A1 receptors to facilitate SWA and is metabolized by adenosine kinase found in glia. This local neuronal-glial circuit for homeostatic SWA is primarily under the requisite control of two genes, the Adora1 and Adk, encoding the responsible adenosine receptor and adenosine's highest affinity metabolizing enzyme.

Slow and Fast Neocortical Oscillations in the Senescence-Accelerated Mouse Model SAMP8

The senescence-accelerated mouse prone 8 (SAMP8) model is characterized by accelerated, progressive cognitive decline as well as Alzheimer’s disease (AD)-like neurodegenerative changes, and resembles the etiology of multicausal, sporadic late-onset/age-related AD in humans. Our aim was to find whether these AD-like pathological features, together with the cognitive deficits present in the SAMP8 strain, are accompanied by disturbances in cortical network activity with respect to control mice (SAM resistance 1, SAMR1) and, if so, how the alterations in cortical activity progress with age. For this purpose, we characterized the extracellular spontaneous oscillatory activity in different regions of the cerebral cortex of SAMP8 and SAMR1 mice under ketamine anesthesia at 5 and 7 months of age. Under these conditions, slow oscillations and fast rhythms generated in the cortical network were recorded and different parameters of these oscillations were quantified and compared between SAMP8 and their control, SAMR1 mice. The average frequency of slow oscillations in SAMP8 mice was decreased with respect to the control mice at both studied ages. An elongation of the silent periods or Down states was behind the decreased slow oscillatory frequency while the duration of active or Up states remained stable. SAMP8 mice also presented increased cycle variability and reduced high frequency components during Down states. During Up states, the power peak in the gamma range was displaced towards lower frequencies in all the cortical areas of SAMP8 with respect to control mice suggesting that the spectral profile of SAMP8 animals is shifted towards lower frequencies. This shift is reminiscent to one of the principal hallmarks of electroencephalography (EEG) abnormalities in patients with Alzheimer’s disease, and adds evidence in support of the suitability of the SAMP8 mouse as a model of this disease. Although some of the differences between SAMP8 and control mice were emphasized with age, the evolution of the studied parameters as SAMR1 mice got older indicates that the SAMR1 phenotype tends to converge with that of SAMP8 animals. To our knowledge, this is the first systematic characterization of the cortical slow and fast rhythms in the SAMP8 strain and it provides useful insights about the cellular and synaptic mechanisms underlying the reported alterations.

Spontaneous cortical activity is transiently poised close to criticality

Brain activity displays a large repertoire of dynamics across the sleep-wake cycle and even during anesthesia. It was suggested that criticality could serve as a unifying principle underlying the diversity of dynamics. This view has been supported by the observation of spontaneous bursts of cortical activity with scale-invariant sizes and durations, known as neuronal avalanches, in recordings of mesoscopic cortical signals. However, the existence of neuronal avalanches in spiking activity has been equivocal with studies reporting both its presence and absence. Here, we show that signs of criticality in spiking activity can change between synchronized and desynchronized cortical states. We analyzed the spontaneous activity in the primary visual cortex of the anesthetized cat and the awake monkey, and found that neuronal avalanches and thermodynamic indicators of criticality strongly depend on collective synchrony among neurons, LFP fluctuations, and behavioral state. We found that synchronized states are associated to criticality, large dynamical repertoire and prolonged epochs of eye closure, while desynchronized states are associated to sub-criticality, reduced dynamical repertoire, and eyes open conditions. Our results show that criticality in cortical dynamics is not stationary, but fluctuates during anesthesia and between different vigilance states.

Desynchronization of the Rat Cortical Network and Excitation of White Matter Neurons by Neurotensin

Cortical network activity correlates with vigilance state: Deep sleep is characterized by slow, synchronized oscillations, whereas desynchronized, stochastic discharge is typical of the waking state. Neuropeptides, such as orexin and substance P but also neurotensin (NT), promote arousal. Relatively little is known about if NT can directly affect the cortical network, and if so, through which mechanisms and cellular targets. Here, we addressed these issues using rat in vitro cortex preparations. Following NT application specifically to deeper layers, slow oscillation activity was attenuated with a significant reduction in UP state frequency. The cortical response to thalamic stimulation exhibited enhanced temporal precision in the presence of NT, consistent with the transition in vivo from sleep to wakefulness. These changes were associated with a relative shift toward inhibition in the excitation/inhibition balance. Whole-cell recordings from layer 6 revealed presynaptically driven NT-induced inhibition of pyramidal neurons and excitation of fast-spiking interneurons. Deeper in the cortex, neurons within the white matter (WM) were strongly depolarized by NT application. The colocalization of NT and tyrosine hydroxylase immunoreactivities in deep layer fibers throughout the cortical mantle indicates mediation via dopaminergic systems. These data suggest a cortical mechanism for NT-induced wakefulness and support a role for WM neurons in state control.

The beneficial role of memory reactivation for language learning during sleep: A review

Sleep is essential for diverse aspects of language learning. According to a prominent concept these beneficial effects of sleep rely on spontaneous reactivation processes. A series of recent studies demonstrated that inducing such reactivation processes by re-exposure to memory cues during sleep enhances foreign vocabulary learning. Building upon these findings, the present article reviews recent models and empirical findings concerning the beneficial effects of sleep on language learning. Consequently, the memory function of sleep, its neural underpinnings and the role of the sleeping brain in language learning will be summarized. Finally, we will propose a working model concerning the oscillatory requirements for successful reactivation processes and future research questions to advance our understanding of the role of sleep on language learning and memory processes in general.

Chaos and Correlated Avalanches in Excitatory Neural Networks with Synaptic Plasticity

A collective chaotic phase with power law scaling of activity events is observed in a disordered mean field network of purely excitatory leaky integrate-and-fire neurons with short-term synaptic plasticity. The dynamical phase diagram exhibits two transitions from quasisynchronous and asynchronous regimes to the nontrivial, collective, bursty regime with avalanches. In the homogeneous case without disorder, the system synchronizes and the bursty behavior is reflected into a period doubling transition to chaos for a two dimensional discrete map. Numerical simulations show that the bursty chaotic phase with avalanches exhibits a spontaneous emergence of persistent time correlations and enhanced Kolmogorov complexity. Our analysis reveals a mechanism for the generation of irregular avalanches that emerges from the combination of disorder and deterministic underlying chaotic dynamics.

Common oscillatory mechanisms across multiple memory systems

The cortex, hippocampus, and striatum support dissociable forms of memory. While each of these regions contains specialized circuitry supporting their respective functions, all structure their activities across time with delta, theta, and gamma rhythms. We review how these oscillations are generated and how they coordinate distinct memory systems during encoding, consolidation, and retrieval. First, gamma oscillations occur in all regions and coordinate local spiking, compressing it into short population bursts. Second, gamma oscillations are modulated by delta and theta oscillations. Third, oscillatory dynamics in these memory systems can operate in either a 'slow' or 'fast' mode. The slow mode happens during slow-wave sleep (SWS) and is characterized by large irregular activity in the hippocampus and delta oscillations in cortical and striatal circuits. The fast mode occurs during active waking and REM and is characterized by theta oscillations in the hippocampus and its targets, along with gamma oscillations in the rest of cortex. In waking, the fast mode is associated with the efficacious encoding and retrieval of declarative and procedural memories. Theta and gamma oscillations have the similar relationships with encoding and retrieval across multiple forms of memory and brain regions, despite regional differences in microcircuitry and information content. Differences in the oscillatory coordination of memory systems during sleep might explain why the consolidation of some forms of memory is sensitive to SWS, while others depend on REM. In particular, theta oscillations appear to support the consolidation of certain types of procedural memories during REM, while delta oscillations during SWS seem to promote declarative and procedural memories.

Repertoire of mesoscopic cortical activity is not reduced during anesthesia

Consciousness has been linked to the repertoire of brain states at various spatiotemporal scales. Anesthesia is thought to modify consciousness by altering information integration in cortical and thalamocortical circuits. At a mesoscopic scale, neuronal populations in the cortex form synchronized ensembles whose characteristics are presumably state-dependent but this has not been rigorously tested. In this study, spontaneous neuronal activity was recorded with 64-contact microelectrode arrays in primary visual cortex of chronically instrumented, unrestrained rats under stepwise decreasing levels of desflurane anesthesia (8%, 6%, 4%, and 2% inhaled concentrations) and wakefulness (0% concentration). Negative phases of the local field potentials formed compact, spatially contiguous activity patterns (CAPs) that were not due to chance. The number of CAPs was 120% higher in wakefulness and deep anesthesia associated with burst-suppression than at intermediate levels of consciousness. The frequency distribution of CAP sizes followed a power-law with slope -1.5 in relatively deep anesthesia (8-6%) but deviated from that at the lighter levels. Temporal variance and entropy of CAP sizes were lowest in wakefulness (76% and 24% lower at 0% than at 8% desflurane, respectively) but changed little during recovery of consciousness. CAPs categorized by K-means clustering were conserved at all anesthesia levels and wakefulness, although their proportion changed in a state-dependent manner. These observations yield new knowledge about the dynamic landscape of ongoing population activity in sensory cortex at graded levels of anesthesia. The repertoire of population activity and self-organized criticality at the mesoscopic scale do not appear to contribute to anesthetic suppression of consciousness, which may instead depend on large-scale effects, more subtle dynamic properties, or changes outside of primary sensory cortex.

What Is the Link Between Hallucinations, Dreams, and Hypnagogic-Hypnopompic Experiences?

By definition, hallucinations occur only in the full waking state. Yet similarities to sleep-related experiences such as hypnagogic and hypnopompic hallucinations, dreams and parasomnias, have been noted since antiquity. These observations have prompted researchers to suggest a common aetiology for these phenomena based on the neurobiology of rapid eye movement (REM) sleep. With our recent understanding of hallucinations in different population groups and at the neurobiological, cognitive and interpersonal levels, it is now possible to draw comparisons between the 2 sets of experiences as never before. In the current article, we make detailed comparisons between sleep-related experiences and hallucinations in Parkinson's disease, schizophrenia and eye disease, at the levels of phenomenology (content, sensory modalities involved, perceptual attributes) and of brain function (brain activations, resting-state networks, neurotransmitter action). Findings show that sleep-related experiences share considerable overlap with hallucinations at the level of subjective descriptions and underlying brain mechanisms. Key differences remain however: (1) Sleep-related perceptions are immersive and largely cut off from reality, whereas hallucinations are discrete and overlaid on veridical perceptions; and (2) Sleep-related perceptions involve only a subset of neural networks implicated in hallucinations, reflecting perceptual signals processed in a functionally and cognitively closed-loop circuit. In summary, both phenomena are non-veridical perceptions that share some phenomenological and neural similarities, but insufficient evidence exists to fully support the notion that the majority of hallucinations depend on REM processes or REM intrusions into waking consciousness.

An Adenosine-Mediated Glial-Neuronal Circuit for Homeostatic Sleep

Sleep homeostasis reflects a centrally mediated drive for sleep, which increases during waking and resolves during subsequent sleep. Here we demonstrate that mice deficient for glial adenosine kinase (AdK), the primary metabolizing enzyme for adenosine (Ado), exhibit enhanced expression of this homeostatic drive by three independent measures: (1) increased rebound of slow-wave activity; (2) increased consolidation of slow-wave sleep; and (3) increased time constant of slow-wave activity decay during an average slow-wave sleep episode, proposed and validated here as a new index for homeostatic sleep drive. Conversely, mice deficient for the neuronal adenosine A1 receptor exhibit significantly decreased sleep drive as judged by these same indices. Neuronal knock-out of AdK did not influence homeostatic sleep need. Together, these findings implicate a glial-neuronal circuit mediated by intercellular Ado, controlling expression of homeostatic sleep drive. Because AdK is tightly regulated by glial metabolic state, our findings suggest a functional link between cellular metabolism and sleep homeostasis.

SIGNIFICANCE STATEMENT

The work presented here provides evidence for an adenosine-mediated regulation of sleep in response to waking (i.e., homeostatic sleep need), requiring activation of neuronal adenosine A1 receptors and controlled by glial adenosine kinase. Adenosine kinase acts as a highly sensitive and important metabolic sensor of the glial ATP/ADP and AMP ratio directly controlling intracellular adenosine concentration. Glial equilibrative adenosine transporters reflect the intracellular concentration to the extracellular milieu to activate neuronal adenosine receptors. Thus, adenosine mediates a glial-neuronal circuit linking glial metabolic state to neural-expressed sleep homeostasis. This indicates a metabolically related function(s) for this glial-neuronal circuit in the buildup and resolution of our need to sleep and suggests potential therapeutic targets more directly related to sleep function.

High-frequency oscillations in human and monkey neocortex during the wake-sleep cycle

Beta (β)- and gamma (γ)-oscillations are present in different cortical areas and are thought to be inhibition-driven, but it is not known if these properties also apply to γ-oscillations in humans. Here, we analyze such oscillations in high-density microelectrode array recordings in human and monkey during the wake-sleep cycle. In these recordings, units were classified as excitatory and inhibitory cells. We find that γ-oscillations in human and β-oscillations in monkey are characterized by a strong implication of inhibitory neurons, both in terms of their firing rate and their phasic firing with the oscillation cycle. The β- and γ-waves systematically propagate across the array, with similar velocities, during both wake and sleep. However, only in slow-wave sleep (SWS) β- and γ-oscillations are associated with highly coherent and functional interactions across several millimeters of the neocortex. This interaction is specifically pronounced between inhibitory cells. These results suggest that inhibitory cells are dominantly involved in the genesis of β- and γ-oscillations, as well as in the organization of their large-scale coherence in the awake and sleeping brain. The highest oscillation coherence found during SWS suggests that fast oscillations implement a highly coherent reactivation of wake patterns that may support memory consolidation during SWS.

Distinct Roles of SOM and VIP Interneurons during Cortical Up States

During cortical network activity, recurrent synaptic excitation among pyramidal neurons is approximately balanced by synaptic inhibition, which is provided by a vast diversity of inhibitory interneurons. The relative contributions of different interneuron subtypes to inhibitory tone during cortical network activity is not well-understood. We previously showed that many of the major interneuron subtypes in mouse barrel cortex are highly active during Up states (Neske et al., 2015); while fast-spiking (FS), parvalbumin (PV)-positive cells were the most active interneuron subtype, many non-fast-spiking (NFS), PV-negative interneurons were as active or more active than neighboring pyramidal cells. This suggests that the NFS cells could play a role in maintaining or modulating Up states. Here, using optogenetic techniques, we further dissected the functional roles during Up states of two major NFS, PV-negative interneuron subtypes: somatostatin (SOM)-positive cells and vasoactive intestinal peptide (VIP)-positive cells. We found that while pyramidal cell excitability during Up states significantly increased when SOM cells were optogenetically silenced, VIP cells did not influence pyramidal cell excitability either upon optogenetic silencing or activation. VIP cells failed to contribute to Up states despite their ability to inhibit SOM cells strongly. We suggest that the contribution of VIP cells to the excitability of pyramidal cells may vary with cortical state.

Spindle Activity Orchestrates Plasticity during Development and Sleep

Spindle oscillations have been described during early brain development and in the adult brain. Besides similarities in temporal patterns and involved brain areas, neonatal spindle bursts (NSBs) and adult sleep spindles (ASSs) show differences in their occurrence, spatial distribution, and underlying mechanisms. While NSBs have been proposed to coordinate the refinement of the maturating neuronal network, ASSs are associated with the implementation of acquired information within existing networks. Along with these functional differences, separate synaptic plasticity mechanisms seem to be recruited. Here, we review the generation of spindle oscillations in the developing and adult brain and discuss possible implications of their differences for synaptic plasticity. The first part of the review is dedicated to the generation and function of ASSs with a particular focus on their role in healthy and impaired neuronal networks. The second part overviews the present knowledge of spindle activity during development and the ability of NSBs to organize immature circuits. Studies linking abnormal maturation of brain wiring with neurological and neuropsychiatric disorders highlight the importance to better elucidate neonatal plasticity rules in future research.