Different Simultaneous Sleep States in the Hippocampus and Neocortex

Adaptive modeling and inference of higher-order coordination in neuronal assemblies: A dynamic greedy estimation approach

Central in the study of population codes, coordinated ensemble spiking activity is widely observable in neural recordings with hypothesized roles in robust stimulus representation, interareal communication, and learning and memory formation. Model-free measures of synchrony characterize coherent pairwise activity but not higher-order interactions, a limitation transcended by statistical models of ensemble spiking activity. However, existing model-based analyses often impose assumptions about the relevance of higher-order interactions and require repeated trials to characterize dynamics in the correlational structure of ensemble activity. To address these shortcomings, we propose an adaptive greedy filtering algorithm based on a discretized mark point-process model of ensemble spiking and a corresponding statistical inference framework to identify significant higher-order coordination. In the course of developing a precise statistical test, we show that confidence intervals can be constructed for greedily estimated parameters. We demonstrate the utility of our proposed methods on simulated neuronal assemblies. Applied to multi-electrode recordings from human and rat cortical assemblies, our proposed methods provide new insights into the dynamics underlying localized population activity during transitions between brain states.

Somnotate: A probabilistic sleep stage classifier for studying vigilance state transitions

Electrophysiological recordings from freely behaving animals are a widespread and powerful mode of investigation in sleep research. These recordings generate large amounts of data that require sleep stage annotation (polysomnography), in which the data is parcellated according to three vigilance states: awake, rapid eye movement (REM) sleep, and non-REM (NREM) sleep. Manual and current computational annotation methods ignore intermediate states because the classification features become ambiguous, even though intermediate states contain important information regarding vigilance state dynamics. To address this problem, we have developed "Somnotate"-a probabilistic classifier based on a combination of linear discriminant analysis (LDA) with a hidden Markov model (HMM). First we demonstrate that Somnotate sets new standards in polysomnography, exhibiting annotation accuracies that exceed human experts on mouse electrophysiological data, remarkable robustness to errors in the training data, compatibility with different recording configurations, and an ability to maintain high accuracy during experimental interventions. However, the key feature of Somnotate is that it quantifies and reports the certainty of its annotations. We leverage this feature to reveal that many intermediate vigilance states cluster around state transitions, whereas others correspond to failed attempts to transition. This enables us to show for the first time that the success rates of different types of transition are differentially affected by experimental manipulations and can explain previously observed sleep patterns. Somnotate is open-source and has the potential to both facilitate the study of sleep stage transitions and offer new insights into the mechanisms underlying sleep-wake dynamics.

What is sleep exactly? Global and local modulations of sleep oscillations all around the clock

Wakefulness, non-rapid eye-movement (NREM) and rapid eye-movement (REM) sleep differ from each other along three dimensions: behavioral, phenomenological, physiological. Although these dimensions often fluctuate in step, they can also dissociate. The current paradigm that views sleep as made of global NREM and REM states fail to account for these dissociations. This conundrum can be dissolved by stressing the existence and significance of the local regulation of sleep. We will review the evidence in animals and humans, healthy and pathological brains, showing different forms of local sleep and the consequences on behavior, cognition, and subjective experience. Altogether, we argue that the notion of local sleep provides a unified account for a host of phenomena: dreaming in REM and NREM sleep, NREM and REM parasomnias, intrasleep responsiveness, inattention and mind wandering in wakefulness. Yet, the physiological origins of local sleep or its putative functions remain unclear. Exploring further local sleep could provide a unique and novel perspective on how and why we sleep.

Adaptive modeling and inference of higher-order coordination in neuronal assemblies: a dynamic greedy estimation approach

Central in the study of population codes, coordinated ensemble spiking activity is widely observable in neural recordings with hypothesized roles in robust stimulus representation, interareal communication, and learning and memory formation. Model-free measures of synchrony characterize coherent pairwise activity but not higher-order interactions, a limitation transcended by statistical models of ensemble spiking activity. However, existing model-based analyses often impose assumptions about the relevance of higher-order interactions and require repeated trials to characterize dynamics in the correlational structure of ensemble activity. To address these shortcomings, we propose an adaptive greedy filtering algorithm based on a discretized mark point-process model of ensemble spiking and a corresponding statistical inference framework to identify significant higher-order coordination. In the course of developing a precise statistical test, we show that confidence intervals can be constructed for greedily estimated parameters. We demonstrate the utility of our proposed methods on simulated neuronal assemblies. Applied to multi-electrode recordings from human and rat cortical assemblies, our proposed methods provide new insights into the dynamics underlying localized population activity during transitions between brain states.

How we sleep: From brain states to processes

All our lives, we alternate between wakefulness and sleep with direct consequences on our ability to interact with our environment, the dynamics and contents of our subjective experience, and our brain activity. Consequently, sleep has been extensively characterised in terms of behavioural, phenomenological, and physiological changes, the latter constituting the gold standard of sleep research. The common view is thus that sleep represents a collection of discrete states with distinct neurophysiological signatures. However, recent findings challenge such a monolithic view of sleep. Indeed, there can be sharp discrepancies in time and space in the activity displayed by different brain regions or networks, making it difficult to assign a global vigilance state to such a mosaic of contrasted dynamics. Viewing sleep as a multidimensional continuum rather than a succession of non-overlapping and mutually exclusive states could account for these local aspects of sleep. Moving away from the focus on sleep states, sleep can also be investigated through the brain processes that are present in sleep, if not necessarily specific to sleep. This focus on processes rather than states allows to see sleep for what it does rather than what it is, avoiding some of the limitations of the state perspective and providing a powerful heuristic to understand sleep. Indeed, what is sleep if not a process itself that makes up wake up every morning with a brain cleaner, leaner and less cluttered.

Sleep and vigilance states: Embracing spatiotemporal dynamics

The classic view of sleep and vigilance states is a global stationary perspective driven by the interaction between neuromodulators and thalamocortical systems. However, recent data are challenging this view by demonstrating that vigilance states are highly dynamic and regionally complex. Spatially, sleep- and wake-like states often co-occur across distinct brain regions, as in unihemispheric sleep, local sleep in wakefulness, and during development. Temporally, dynamic switching prevails around state transitions, during extended wakefulness, and in fragmented sleep. This knowledge, together with methods monitoring brain activity across multiple regions simultaneously at millisecond resolution with cell-type specificity, is rapidly shifting how we consider vigilance states. A new perspective incorporating multiple spatial and temporal scales may have important implications for considering the governing neuromodulatory mechanisms, the functional roles of vigilance states, and their behavioral manifestations. A modular and dynamic view highlights novel avenues for finer spatiotemporal interventions to improve sleep function.

The Metabotropic Glutamate 5 Receptor in Sleep and Wakefulness: Focus on the Cortico-Thalamo-Cortical Oscillations

Sleep is an essential innate but complex behaviour which is ubiquitous in the animal kingdom. Our knowledge of the distinct neural circuit mechanisms that regulate sleep and wake states in the brain are, however, still limited. It is therefore important to understand how these circuits operate during health and disease. This review will highlight the function of mGlu5 receptors within the thalamocortical circuitry in physiological and pathological sleep states. We will also evaluate the potential of targeting mGlu5 receptors as a therapeutic strategy for sleep disorders that often co-occur with epileptic seizures.

A non-oscillatory, millisecond-scale embedding of brain state provides insight into behavior

Sleep and wake are understood to be slow, long-lasting processes that span the entire brain. Brain states correlate with many neurophysiological changes, yet the most robust and reliable signature of state is enriched in rhythms between 0.1 and 20 Hz. The possibility that the fundamental unit of brain state could be a reliable structure at the scale of milliseconds and microns has not been addressed due to the physical limits associated with oscillation-based definitions. Here, by analyzing high resolution neural activity recorded in 10 anatomically and functionally diverse regions of the murine brain over 24 h, we reveal a mechanistically distinct embedding of state in the brain. Sleep and wake states can be accurately classified from on the order of 100 to 101 ms of neuronal activity sampled from 100 μm of brain tissue. In contrast to canonical rhythms, this embedding persists above 1,000 Hz. This high frequency embedding is robust to substates and rapid events such as sharp wave ripples and cortical ON/OFF states. To ascertain whether such fast and local structure is meaningful, we leveraged our observation that individual circuits intermittently switch states independently of the rest of the brain. Brief state discontinuities in subsets of circuits correspond with brief behavioral discontinuities during both sleep and wake. Our results suggest that the fundamental unit of state in the brain is consistent with the spatial and temporal scale of neuronal computation, and that this resolution can contribute to an understanding of cognition and behavior.

Regional variability in intracerebral properties of NREM to REM sleep transitions in humans

Transitions between wake and sleep states show a progressive pattern underpinned by local sleep regulation. In contrast, little evidence is available on non-rapid eye movement (NREM) to rapid eye movement (REM) sleep boundaries, considered as mainly reflecting subcortical regulation. Using polysomnography (PSG) combined with stereoelectroencephalography (SEEG) in humans undergoing epilepsy presurgical evaluation, we explored the dynamics of NREM-to-REM transitions. PSG was used to visually score transitions and identify REM sleep features. SEEG-based local transitions were determined automatically with a machine learning algorithm using features validated for automatic intra-cranial sleep scoring (10.5281/zenodo.7410501). We analyzed 2988 channel-transitions from 29 patients. The average transition time from all intracerebral channels to the first visually marked REM sleep epoch was 8 s ± 1 min 58 s, with a great heterogeneity between brain areas. Transitions were observed first in the lateral occipital cortex, preceding scalp transition by 1 min 57 s ± 2 min 14 s (d = -0.83), and close to the first sawtooth wave marker. Regions with late transitions were the inferior frontal and orbital gyri (1 min 1 s ± 2 min 1 s, d = 0.43, and 1 min 1 s ± 2 min 5 s, d = 0.43, after scalp transition). Intracranial transitions were earlier than scalp transitions as the night advanced (last sleep cycle, d = -0.81). We show a reproducible gradual pattern of REM sleep initiation, suggesting the involvement of cortical mechanisms of regulation. This provides clues for understanding oneiric experiences occurring at the NREM/REM boundary.

Sleep-A brain-state serving systems memory consolidation

Although long-term memory consolidation is supported by sleep, it is unclear how it differs from that during wakefulness. Our review, focusing on recent advances in the field, identifies the repeated replay of neuronal firing patterns as a basic mechanism triggering consolidation during sleep and wakefulness. During sleep, memory replay occurs during slow-wave sleep (SWS) in hippocampal assemblies together with ripples, thalamic spindles, neocortical slow oscillations, and noradrenergic activity. Here, hippocampal replay likely favors the transformation of hippocampus-dependent episodic memory into schema-like neocortical memory. REM sleep following SWS might balance local synaptic rescaling accompanying memory transformation with a sleep-dependent homeostatic process of global synaptic renormalization. Sleep-dependent memory transformation is intensified during early development despite the immaturity of the hippocampus. Overall, beyond its greater efficacy, sleep consolidation differs from wake consolidation mainly in that it is supported, rather than impaired, by spontaneous hippocampal replay activity possibly gating memory formation in neocortex.

Recurrent Hippocampo-neocortical sleep-state divergence in humans

Sleep is assumed to be a unitary, global state in humans and most other animals that is coordinated by executive centers in the brain stem, hypothalamus, and basal forebrain. However, the common observation of unihemispheric sleep in birds and marine mammals, as well as the recently discovered nonpathological regional sleep in rodents, calls into question whether the whole human brain might also typically exhibit different states between brain areas at the same time. We analyzed sleep states independently from simultaneously recorded hippocampal depth electrodes and cortical scalp electrodes in eight human subjects who were implanted with depth electrodes for pharmacologically intractable epilepsy evaluation. We found that the neocortex and hippocampus could be in nonsimultaneous states, on average, one-third of the night and that the hippocampus often led in asynchronous state transitions. Nonsimultaneous bout lengths varied from 30 s to over 30 min. These results call into question the conclusions of studies, across phylogeny, that measure only surface cortical state but seek to assess the functions and drivers of sleep states throughout the brain.

Certainty about uncertainty in sleep staging: a theoretical framework

Sleep stage classification is an important tool for the diagnosis of sleep disorders. Because sleep staging has such a high impact on clinical outcome, it is important that it is done reliably. However, it is known that uncertainty exists in both expert scorers and automated models. On average, the agreement between human scorers is only 82.6%. In this study, we provide a theoretical framework to facilitate discussion and further analyses of uncertainty in sleep staging. To this end, we introduce two variants of uncertainty, known from statistics and the machine learning community: aleatoric and epistemic uncertainty. We discuss what these types of uncertainties are, why the distinction is useful, where they arise from in sleep staging, and provide recommendations on how this framework can improve sleep staging in the future.

Sex differences within sleep in gonadally intact rats

Sleep impacts diverse physiological and neural processes and is itself affected by the menstrual cycle; however, few studies have examined the effects of the estrous cycle on sleep in rodents. Studies of disease mechanisms in females therefore lack critical information regarding estrous cycle influences on relevant sleep characteristics. We recorded electroencephalographic (EEG) activity from multiple brain regions to assess sleep states as well as sleep traits such as spectral power and interregional spectral coherence in freely cycling females across the estrous cycle and compared with males. Our findings show that the high hormone phase of proestrus decreases the amount of nonrapid eye movement (NREM) sleep and rapid eye movement (REM) sleep and increases the amount of time spent awake compared with other estrous phases and to males. This spontaneous sleep deprivation of proestrus was followed by a sleep rebound in estrus which increased NREM and REM sleep. In proestrus, spectral power increased in the delta (0.5-4 Hz) and the gamma (30-60 Hz) ranges during NREM sleep, and increased in the theta range (5-9 Hz) during REM sleep during both proestrus and estrus. Slow-wave activity (SWA) and cortical sleep spindle density also increased in NREM sleep during proestrus. Finally, interregional NREM and REM spectral coherence increased during proestrus. This work demonstrates that the estrous cycle affects more facets of sleep than previously thought and reveals both sex differences in features of the sleep-wake cycle related to estrous phase that likely impact the myriad physiological processes influenced by sleep.

The why and how of sleep-dependent synaptic down-selection

Sleep requires that we disconnect from the environment, losing the ability to promptly respond to stimuli. There must be at least one essential function that justifies why we take this risk every day, and that function must depend on the brain being offline. We have proposed that this function is to renormalize synaptic weights after learning has led to a net increase in synaptic strength in many brain circuits. Without this renormalization, synaptic activity would become energetically too expensive and saturation would prevent new learning. There is converging evidence from molecular, electrophysiological, and ultrastructural experiments showing a net increase in synaptic strength after the major wake phase, and a net decline after sleep. The evidence also suggests that sleep-dependent renormalization is a smart process of synaptic down-selection, comprehensive and yet specific, which could explain the many beneficial effects of sleep on cognition. Recently, a key molecular mechanism that allows broad synaptic weakening during sleep was identified. Other mechanisms still being investigated should eventually explain how sleep can weaken most synapses but afford protection to some, including those directly activated by learning. That synaptic down-selection takes place during sleep is by now established; why it should take place during sleep has a plausible explanation; how it happens is still work in progress.

Short-term sleep deprivation immediately after contextual conditioning inhibits BDNF signaling and disrupts memory consolidation in predator odor trauma mice model of PTSD

Post-traumatic stress disorder (PTSD) is a debilitating neuropsychiatric illness affecting > 7 million people every year in the US. Recently, we have shown that the mouse model of predator odor trauma (POT) displayed contextual conditioning and core features of PTSD including sleep disturbances (hyperarousal) and retrieval of traumatic memories following exposure to objective reminders (re-experiencing). PTSD is a disorder of memory function. Since memory consolidation requires the expression of BDNF along with an activation of MAPK/pERK signaling pathway in limbic brain structures (hippocampus and amygdala) and sleep favors memory consolidation, we hypothesized that short-term sleep deprivation (SD, 3 h), immediately after contextual conditioning will attenuate molecular correlates of memory consolidation, sleep disturbances, and memory consolidation. We performed two experiments in adult male C57BL/6J mice to test our hypothesis. Experiment 1 determined the effects of SD on contextual conditioning and changes in sleep wakefulness. Experiment 2 determined the effects of SD on contextual conditioning-induced changes in the expression of BDNF and pERK in hippocampus and amygdala. SD immediately after contextual conditioning (POT + SD group) significantly attenuated sleep disturbances, memory retrieval, and expression of pERK and BDNF in the hippocampus and amygdala as compared to POT-SD group (no SD after contextual conditioning). No significant differences were observed between POT + SD, NOC-SD (no contextual conditioning + no SD), and NOC + SD (no contextual conditioning + SD) groups. Memory consolidation requires sleep and the expression of pERK and BDNF in hippocampus and amygdala immediately after contextual conditioning in POT model of PTSD in mice.

Diversity of simultaneous sleep in the motor cortex and hippocampus in rats

We investigated the homogeneity/heterogeneity of spontaneous sleep, simultaneously recorded in the motor cortex and the hippocampus of control rats, and particularly analysed simultaneous and non-simultaneous motor cortical and hippocampal non-rapid eye movement (NREM)/rapid eye movement (REM) sleep. We demonstrate that the sleep architectures of the motor cortex and hippocampus are different in control rats. There was an increase of NREM duration and a decrease of REM duration in the hippocampus versus the motor cortex. In terms of duration, NREM state is the most heterogeneous in the hippocampus, whereas the REM state is the most heterogeneous in the motor cortex. Whereas the hippocampal NREM duration was increased due to the prolongation of NREM episodes, the hippocampal REM duration decreased due to the decreased number of REM episodes. The heterogeneity of sleep in the motor cortex and hippocampus in control rats was particularly expressed through the inverse alteration of sigma amplitude during NREM sleep and beta/gamma amplitudes during REM sleep in the hippocampus, along with the delta, sigma, beta and gamma amplitudes only during non-simultaneous NREM/REM sleep in the hippocampus. We demonstrated the brain structure-related and NREM/REM state-related heterogeneity of the motor cortical and hippocampal local sleep in control rats. The distinctly altered local NREM/REM states, alongside their episode dynamics and electroencephalographic (EEG) microstructures, suggest the importance of both the local neuronal network substrate and the NREM/REM neurochemical substrate in the control mechanisms of sleep.

What Is REM Sleep?

For many decades, sleep researchers have sought to determine which species 'have' rapid eye movement (REM) sleep. In doing so, they relied predominantly on a template derived from the expression of REM sleep in the adults of a small number of mammalian species. Here, we argue for a different approach that focuses less on a binary decision about haves and have nots, and more on the diverse expression of REM sleep components over development and across species. By focusing on the components of REM sleep and discouraging continued reliance on a restricted template, we aim to promote a richer and more biologically grounded developmental-comparative approach that spans behavioral, physiological, neural, and ecological domains.

Heterogeneous profiles of coupled sleep oscillations in human hippocampus

Cross-frequency coupling of sleep oscillations is thought to mediate memory consolidation. While the hippocampus is deemed central to this process, detailed knowledge of which oscillatory rhythms interact in the sleeping human hippocampus is lacking. Combining intracranial hippocampal and non-invasive electroencephalography from twelve neurosurgical patients, we characterized spectral power and coupling during non-rapid eye movement (NREM) and rapid eye movement (REM) sleep. Hippocampal coupling was extensive, with the majority of channels expressing spectral interactions. NREM consistently showed delta–ripple coupling, but ripples were also modulated by slow oscillations (SOs) and sleep spindles. SO–delta and SO–theta coupling, as well as interactions between delta/theta and spindle/beta frequencies also occurred. During REM, limited interactions between delta/theta and beta frequencies emerged. Moreover, oscillatory organization differed substantially between i) hippocampus and scalp, ii) sites along the anterior-posterior hippocampal axis, and iii) individuals. Overall, these results extend and refine our understanding of hippocampal sleep oscillations.

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Sleep oscillations in human hippocampus exhibit cross-frequency coupling during non-rapid eye movement sleep

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Coupling occurs between various frequency pairs, including slow oscillation, delta, theta, spindle, beta, and ripple bands

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Oscillatory organization varies between hippocampus and scalp, sites along the hippocampal axis, and individuals

Local Aspects of Avian Non-REM and REM Sleep

Birds exhibit two types of sleep that are in many respects similar to mammalian rapid eye movement (REM) and non-REM (NREM) sleep. As in mammals, several aspects of avian sleep can occur in a local manner within the brain. Electrophysiological evidence of NREM sleep occurring more deeply in one hemisphere, or only in one hemisphere – the latter being a phenomenon most pronounced in dolphins – was actually first described in birds. Such asymmetric or unihemispheric NREM sleep occurs with one eye open, enabling birds to visually monitor their environment for predators. Frigatebirds primarily engage in this form of sleep in flight, perhaps to avoid collisions with other birds. In addition to interhemispheric differences in NREM sleep intensity, the intensity of NREM sleep is homeostatically regulated in a local, use-depended manner within each hemisphere. Furthermore, the intensity and temporo-spatial distribution of NREM sleep-related slow waves varies across layers of the avian hyperpallium – a primary visual area – with the slow waves occurring first in, and propagating through and outward from, thalamic input layers. Slow waves also have the greatest amplitude in these layers. Although most research has focused on NREM sleep, there are also local aspects to avian REM sleep. REM sleep-related reductions in skeletal muscle tone appear largely restricted to muscles involved in maintaining head posture. Other local aspects of sleep manifest as a mixture of features of NREM and REM sleep occurring simultaneously in different parts of the neuroaxis. Like monotreme mammals, ostriches often exhibit brainstem-mediated features of REM sleep (muscle atonia and REMs) while the hyperpallium shows EEG slow waves typical of NREM sleep. Finally, although mice show slow waves in thalamic input layers of primary sensory cortices during REM sleep, this is not the case in the hyperpallium of pigeons, suggesting that this phenomenon is not a universal feature of REM sleep. Collectively, the local aspects of sleep described in birds and mammals reveal that wakefulness, NREM sleep, and REM sleep are not always discrete states.

Abnormal Locus Coeruleus Sleep Activity Alters Sleep Signatures of Memory Consolidation and Impairs Place Cell Stability and Spatial Memory

Sleep is critical for proper memory consolidation. The locus coeruleus (LC) releases norepinephrine throughout the brain except when the LC falls silent throughout rapid eye movement (REM) sleep and prior to each non-REM (NREM) sleep spindle. We hypothesize that these transient LC silences allow the synaptic plasticity that is necessary to incorporate new information into pre-existing memory circuits. We found that spontaneous LC activity within sleep spindles triggers a decrease in spindle power. By optogenetically stimulating norepinephrine-containing LC neurons at 2 Hz during sleep, we reduced sleep spindle occurrence, as well as NREM delta power and REM theta power, without causing arousals or changing sleep amounts. Stimulating the LC during sleep following a hippocampus-dependent food location learning task interfered with consolidation of newly learned locations and reconsolidation of previous locations, disrupting next-day place cell activity. The LC stimulation-induced reduction in NREM sleep spindles, delta, and REM theta and reduced ripple-spindle coupling all correlated with decreased hippocampus-dependent performance on the task. Thus, periods of LC silence during sleep following learning are essential for normal spindle generation, delta and theta power, and consolidation of spatial memories.

Specific Basal Forebrain-Cortical Cholinergic Circuits Coordinate Cognitive Operations

Based on recent molecular genetics, as well as functional and quantitative anatomical studies, the basal forebrain (BF) cholinergic projections, once viewed as a diffuse system, are emerging as being remarkably specific in connectivity. Acetylcholine (ACh) can rapidly and selectively modulate activity of specific circuits and ACh release can be coordinated in multiple areas that are related to particular aspects of cognitive processing. This review discusses how a combination of multiple new approaches with more established techniques are being used to finally reveal how cholinergic neurons, together with other BF neurons, provide temporal structure for behavior, contribute to local cortical state regulation, and coordinate activity between different functionally related cortical circuits. ACh selectively modulates dynamics for encoding and attention within individual cortical circuits, allows for important transitions during sleep, and shapes the fidelity of sensory processing by changing the correlation structure of neural firing. The importance of this system for integrated and fluid behavioral function is underscored by its disease-modifying role; the demise of BF cholinergic neurons has long been established in Alzheimer's disease and recent studies have revealed the involvement of the cholinergic system in modulation of anxiety-related circuits. Therefore, the BF cholinergic system plays a pivotal role in modulating the dynamics of the brain during sleep and behavior, as foretold by the intricacies of its anatomical map.

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.

Integrative analysis of genome-wide association study and brain region related enhancer maps identifies biological pathways for insomnia

Insomnia is a common sleep disorder whose genetic mechanism remains unknown. The aim of this study is to identify novel genes, gene enrichment sets and enriched tissue/cell types for insomnia considering the differences across different brain regions. We conducted an integrative analysis of genome-wide association study (GWAS) and brain region related enhancer maps. Summary data was derived from a large-scale GWAS of insomnia, involving 113,006 unrelated individuals. The chromosomal enhancer maps of 6 brain regions were then aligned with the GWAS summary data to obtain the association testing results of enhancer regions for insomnia. Gene prioritization, tissue/cell and pathway enrichment analysis were implemented by Data-driven Expression Prioritized Integration for Complex Traits (DEPICT) tool. We identified multiple cross-brain regions or brain-region specific prioritized genes for insomnia, such as MADD (P = .0013 in angular gyrus), PPP2R3C (P = .0319 in cingulate gyrus), CASP9 (P = .0066 in angular gyrus and P = .0278 in hippocampus middle), PLEKHM2 (P = .0032 in angular gyrus, P = .0052 in anterior caudate, P = .0385 in cingulate gyrus and P = .0011 in inferior temporal lobe). This study also detected a group of insomnia associated biological pathways within multiple or specific brain regions, such as REACTOME_SIGNALING_BY_NOTCH and KEGG_GLYCEROPHOSPHOLIPID_METABOLISM. Our results showed that insomnia associated genes were significantly enriched in neural stem cells. Our results highlight a set of potential points, particularly neural stem cells, for subsequent biological studies for insomnia.

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.

Consolidative mechanisms of emotional processing in REM sleep and PTSD

Research suggests sleep plays a role in the consolidation of recently acquired memories for long-term storage. rapid eye movement (REM) sleep has been shown to play a complex role in emotional-memory processing, and may be involved in subsequent waking-day emotional reactivity and amygdala responsivity. Interaction of the hippocampus and basolateral amygdala with the medial-prefrontal cortex is associated with sleep-dependent learning and emotional memory processing. REM is also implicated in post-traumatic stress disorder (PTSD), which is characterized by sleep disturbance, heightened reactivity to fearful stimuli, and nightmares. Many suffers of PTSD also exhibit dampened medial-prefrontal cortex activity. However, the effects of PTSD-related brain changes on REM-dependent consolidation or the notion of 'over-consolidation' (strengthening of memory traces to such a degree that they become resistant to extinction) have been minimally explored. Here, we posit that (in addition to sleep architecture changes) the memory functions of REM must also be altered in PTSD. We propose a model of REM-dependent consolidation of learned fear in PTSD and examine how PTSD-related brain changes might interact with fear learning. We argue that reduced efficacy of inhibitory medial-prefrontal pathways may lead to maladaptive processing of traumatic memories in the early stages of consolidation after trauma.