Linking Network Activity to Synaptic Plasticity during Sleep: Hypotheses and Recent Data

Sleep and synaptic down-selection

The synaptic homeostasis hypothesis (SHY) proposes that sleep is an essential process needed by the brain to maintain the total amount of synaptic strength under control. SHY predicts that by the end of a waking day the synaptic connections of many neural circuits undergo a net increase in synaptic strength due to ongoing learning, which is mainly mediated by synaptic potentiation. Stronger synapses require more energy and supplies and are prone to saturation, creating the need for synaptic renormalization. Such renormalization should mainly occur during sleep, when the brain is disconnected from the environment and neural circuits can be broadly reactivated off-line to undergo a systematic but specific synaptic down-selection. In short, according to SHY sleep is the price to pay for waking plasticity, to avoid runaway potentiation, decreased signal-to-noise ratio, and impaired learning due to saturation. In this review, we briefly discuss the rationale of the hypothesis and recent supportive ultrastructural evidence obtained in our laboratory. We then examine recent studies by other groups showing the causal role of cortical slow waves and hippocampal sharp waves/ripples in sleep-dependent down-selection of neural activity and synaptic strength. Finally, we discuss some of the molecular mechanisms that could mediate synaptic weakening during sleep.

Sleep as a translationally-relevant endpoint in studies of autism spectrum disorder (ASD)

Sleep has numerous advantages for aligning clinical and preclinical (basic neuroscience) studies of neuropsychiatric illness. Sleep has high translational relevance, because the same endpoints can be studied in humans and laboratory animals. In addition, sleep experiments are conducive to continuous data collection over long periods (hours/days/weeks) and can be based on highly objective neurophysiological measures. Here, we provide a translationally-oriented review on what is currently known about sleep in the context of autism spectrum disorder (ASD), including ASD-related conditions, thought to have genetic, environmental, or mixed etiologies. In humans, ASD is frequently associated with comorbid medical conditions including sleep disorders. Animal models used in the study of ASD frequently recapitulate dysregulation of sleep and biological (diurnal, circadian) rhythms, suggesting common pathophysiologies across species. As our understanding of the neurobiology of ASD and sleep each become more refined, it is conceivable that sleep-derived metrics may offer more powerful biomarkers of altered neurophysiology in ASD than the behavioral tests currently used in humans or lab animals. As such, the study of sleep in animal models for ASD may enable fundamentally new insights on the condition and represent a basis for strategies that enable the development of more effective therapeutics.

Infraslow coordination of slow wave activity through altered neuronal synchrony

Slow wave activity (SWA; the EEG power between 0.5 and 4 Hz during non-rapid eye movement sleep [NREM]) is the best electrophysiological marker of sleep need; SWA dissipates across the night and increases following sleep deprivation. In addition to these well-documented homeostatic SWA trends, SWA exhibits extensive variability across shorter timescales (seconds to minutes) and between local cortical regions. The physiological underpinnings of SWA variability, however, remain poorly characterized. In male Sprague-Dawley rats, we observed that SWA exhibits pronounced infraslow fluctuations (~40- to 120-s periods) that are coordinated across disparate cortical locations. Peaks in SWA across infraslow cycles were associated with increased slope, amplitude, and duration of individual slow waves and a reduction in the total number of waves and proportion of multipeak waves. Using a freely available data set comprised of extracellular unit recordings during consolidated NREM episodes in male Long-Evans rats, we further show that infraslow SWA does not appear to arise as a consequence of firing rate modulation of putative excitatory or inhibitory neurons. Instead, infraslow SWA was associated with alterations in neuronal synchrony surrounding "On"/"Off" periods and changes in the number and duration of "Off" periods. Collectively, these data provide a mechanism by which SWA can be coordinated across disparate cortical locations and thereby connect local and global expression of this patterned neuronal activity. In doing so, infraslow SWA may contribute to the regulation of cortical circuits during sleep and thereby play a critical role in sleep function.

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.

Somatostatin+/nNOS+ neurons are involved in delta electroencephalogram activity and cortical-dependent recognition memory

Slow-wave activity (SWA) is an oscillatory neocortical activity occurring in the electroencephalogram delta (δ) frequency range (~0.5-4 Hz) during nonrapid eye movement sleep. SWA is a reliable indicator of sleep homeostasis after acute sleep loss and is involved in memory processes. Evidence suggests that cortical neuronal nitric oxide synthase (nNOS) expressing neurons that coexpress somatostatin (SST) play a key role in regulating SWA. However, previous studies lacked selectivity in targeting specific types of neurons that coexpress nNOS-cells which are activated in the cortex after sleep loss. We produced a mouse model that knocks out nNOS expression in neurons that coexpress SST throughout the cortex. Mice lacking nNOS expression in SST positive neurons exhibited significant impairments in both homeostatic low-δ frequency range SWA production and a recognition memory task that relies on cortical input. These results highlight that SST+/nNOS+ neurons are involved in the SWA homeostatic response and cortex-dependent recognition memory.

Mechanisms of systems memory consolidation during sleep

Long-term memory formation is a major function of sleep. Based on evidence from neurophysiological and behavioral studies mainly in humans and rodents, we consider the formation of long-term memory during sleep as an active systems consolidation process that is embedded in a process of global synaptic downscaling. Repeated neuronal replay of representations originating from the hippocampus during slow-wave sleep leads to a gradual transformation and integration of representations in neocortical networks. We highlight three features of this process: (i) hippocampal replay that, by capturing episodic memory aspects, drives consolidation of both hippocampus-dependent and non-hippocampus-dependent memory; (ii) brain oscillations hallmarking slow-wave and rapid-eye movement sleep that provide mechanisms for regulating both information flow across distant brain networks and local synaptic plasticity; and (iii) qualitative transformations of memories during systems consolidation resulting in abstracted, gist-like representations.

Phagocytic elimination of synapses by microglia during sleep

Synaptic strength reduces during sleep, but the underlying mechanisms of this process are unclear. This study showed reduction of synaptic proteins in rat prefrontal cortex (PFC) at AM7 or Zeitgeber Time (ZT0), when the light phase or sleeping period for rats started. At this time point, microglia were weakly activated, displaying larger and more granular somata with increased CD11b expression compared with those at ZT12, as revealed by flow cytometry. Expression of opsonins, such as complements or MFG-E8, matrix metalloproteinases, and microglial markers at ZT0 were increased compared with that at ZT12. Microglia at ZT0 phagocytosed synapses, as revealed by immunohistochemical staining. Immunoblotting detected more synapsin I in the isolated microglia at ZT0 than at ZT12. Complement C3- or MFG-E8-bound synapses were the most abundant at ZT0, some of which were phagocytosed by microglia. Systemic administration of synthetic glucocorticoid dexamethasone reduced microglial size, granularity and CD11b expression at ZT0, resembling microglia at ZT12, and increased synaptic proteins and decreased the sleeping period. Noradrenaline (NA) suppressed glutamate-induced phagocytosis in primary cultured microglia. Systemic administration of the brain monoamine-depleting agent reserpine decreased NA content and synapsin I expression in PFC, and increased expression of microglia markers, C3 and MFG-E8, while increasing the sleeping period. A NA precursor l-threo-dihydroxyphenylserine abolished the reserpine-induced changes. These results suggest that microglia may eliminate presumably weak synapses during every sleep phase. The circadian changes in concentrations of circulating glucocorticoids and brain NA might be correlated with the circadian changes of microglial phenotypes and synaptic strength.

How rhythms of the sleeping brain tune memory and synaptic plasticity

Decades of neurobehavioral research has linked sleep-associated rhythms in various brain areas to improvements in cognitive performance. However, it remains unclear what synaptic changes might underlie sleep-dependent declarative memory consolidation and procedural task improvement, and why these same changes appear not to occur across a similar interval of wake. Here we describe recent research on how one specific feature of sleep-network rhythms characteristic of rapid eye movement and non-rapid eye movement-could drive synaptic strengthening or weakening in specific brain circuits. We provide an overview of how these rhythms could affect synaptic plasticity individually and in concert. We also present an overarching hypothesis for how all network rhythms occurring across the sleeping brain could aid in encoding new information in neural circuits.

How Sleep Shapes Thalamocortical Circuit Function in the Visual System

Recent data have shown that sleep plays a beneficial role for cognitive functions such as declarative memory consolidation and perceptual learning. In this article, we review recent findings on the role of sleep in promoting adaptive visual response changes in the lateral geniculate nucleus and primary visual cortex following novel visual experiences. We discuss these findings in the context of what is currently known about how sleep affects the activity and function of thalamocortical circuits and current hypotheses regarding how sleep facilitates synaptic plasticity.

Peeking into the sleeping brain: Using in vivo imaging in rodents to understand the relationship between sleep and cognition

Sleep is well known to benefit cognitive function. In particular, sleep has been shown to enhance learning and memory in both humans and animals. While the underlying mechanisms are not fully understood, it has been suggested that brain activity during sleep modulates neuronal communication through synaptic plasticity. These insights were mostly gained using electrophysiology to monitor ongoing large scale and single cell activity. While these efforts were instrumental in the characterisation of important network and cellular activity during sleep, several aspects underlying cognition are beyond the reach of this technology. Neuronal circuit activity is dynamically regulated via the precise interaction of different neuronal and non-neuronal cell types and relies on subtle modifications of individual synapses. In contrast to established electrophysiological approaches, recent advances in imaging techniques, mainly applied in rodents, provide unprecedented access to these aspects of neuronal function in vivo. In this review, we describe various techniques currently available for in vivo brain imaging, from single synapse to large scale network activity. We discuss the advantages and limitations of these approaches in the context of sleep research and describe which particular aspects related to cognition lend themselves to this kind of investigation. Finally, we review the few studies that used in vivo imaging in rodents to investigate the sleeping brain and discuss how the results have already significantly contributed to a better understanding on the complex relation between sleep and plasticity across development and adulthood.

Primed to Sleep: The Dynamics of Synaptic Plasticity Across Brain States

It is commonly accepted that brain plasticity occurs in wakefulness and sleep. However, how these different brain states work in concert to create long-lasting changes in brain circuitry is unclear. Considering that wakefulness and sleep are profoundly different brain states on multiple levels (e.g., cellular, molecular and network activation), it is unlikely that they operate exactly the same way. Rather it is probable that they engage different, but coordinated, mechanisms. In this article we discuss how plasticity may be divided across the sleep-wake cycle, and how synaptic changes in each brain state are linked. Our working model proposes that waking experience triggers short-lived synaptic events that are necessary for transient plastic changes and mark (i.e., 'prime') circuits and synapses for further processing in sleep. During sleep, synaptic protein synthesis at primed synapses leads to structural changes necessary for long-term information storage.

The Function(s) of Sleep

Sleep is a highly conserved phenomenon in endotherms, and therefore it must serve at least one basic function across this wide range of species. What that function is remains one of the biggest mysteries in neurobiology. By using the word neurobiology, we do not mean to exclude possible non-neural functions of sleep, but it is difficult to imagine why the brain must be taken offline if the basic function of sleep did not involve the nervous system. In this chapter we discuss several current hypotheses about sleep function. We divide these hypotheses into two categories: ones that propose higher-order cognitive functions and ones that focus on housekeeping or restorative processes. We also pose four aspects of sleep that any successful functional hypothesis has to account for: why do the properties of sleep change across the life span? Why and how is sleep homeostatically regulated? Why must the brain be taken offline to accomplish the proposed function? And, why are there two radically different stages of sleep?The higher-order cognitive function hypotheses we discuss are essential mechanisms of learning and memory and synaptic plasticity. These are not mutually exclusive hypotheses. Each focuses on specific mechanistic aspects of sleep, and higher-order cognitive processes are likely to involve components of all of these mechanisms. The restorative hypotheses are maintenance of brain energy metabolism, macromolecular biosynthesis, and removal of metabolic waste. Although these three hypotheses seem more different than those related to higher cognitive function, they may each contribute important components to a basic sleep function. Any sleep function will involve specific gene expression and macromolecular biosynthesis, and as we explain there may be important connections between brain energy metabolism and the need to remove metabolic wastes.A deeper understanding of sleep functions in endotherms will enable us to answer whether or not rest behaviors in species other than endotherms are homologous with mammalian and avian sleep. Currently comparisons across the animal kingdom depend on superficial and phenomenological features of rest states and sleep, but investigations of sleep functions would provide more insight into the evolutionary relationships between EEG-defined sleep in endotherms and rest states in ectotherms.

Neural memory of the genioglossus muscle during sleep is stage-dependent in healthy subjects and obstructive sleep apnoea patients

KEY POINTS

In most patients with obstructive sleep apnoea (OSA), there is a spontaneous resolution of the breathing disorders during slow wave sleep (SWS) for yet unknown reasons related to non-anatomical factors. Some recently identified forms of neural memory specific of upper airway muscles may play a role in this phenomenon. In the present study, we show for the first time that a form of memory of the genioglossus (tongue) muscle is greatly enhanced during SWS compared to non-rapid eye movement stage 2 sleep. The present study represents a step forward in understanding the mechanisms responsible for the spontaneous development of stable breathing during SWS in OSA patients and may help the discovery of novel therapeutic strategies for this disease.

ABSTRACT

Several studies have shown that obstructive sleep apnoea (OSA) improves during slow wave sleep (SWS) for reasons that remain unclear. Recent studies have identified forms of neural memory such as short-term potentiation or after-discharge that can occur in response to upper airway obstruction. Neural memory may play a role in the development of stable breathing during SWS by increasing upper airway muscles activity in this sleep stage. We hypothesize that the after-discharge of the genioglossus muscle following upper airway obstruction is enhanced during SWS compared to non-rapid eye movement stage 2 (N2). During sleep, we performed five-breath drops in continuous positive airway pressure (CPAP-drop) to simulate obstructive events and reflexively activate the genioglossus. Immediately afterwards, CPAP was returned to an optimal level. Once the post-drop ventilation returned to eupnoea, the genioglossus after-discharge was measured as the time it took for genioglossus activity to return to baseline levels. In total, 171 CPAP-drops were analysed from a group of 16 healthy subjects and 19 OSA patients. A mixed-model analysis showed that after-discharge duration during SWS was 208% (95% confidence interval = 112% to 387%, P = 0.022) greater than during N2 after adjusting for covariates (ventilatory drive, CPAP levels). There was also a non-significant trend for a -35% reduction in after-discharge duration following an arousal vs. no-arousal from sleep (95% confidence interval = -59.5% to 5%, P = 0.08). Genioglossus after-discharge is two-fold greater in SWS vs. N2, which could partly explain the breathing stabilization described in OSA patients during this sleep stage.

Computational models of memory consolidation and long-term synaptic plasticity during sleep

The brain stores memories by persistently changing the connectivity between neurons. Sleep is known to be critical for these changes to endure. Research on the neurobiology of sleep and the mechanisms of long-term synaptic plasticity has provided data in support of various theories of how brain activity during sleep affects long-term synaptic plasticity. The experimental findings - and therefore the theories - are apparently quite contradictory, with some evidence pointing to a role of sleep in the forgetting of irrelevant memories, whereas other results indicate that sleep supports the reinforcement of the most valuable recollections. A unified theoretical framework is in need. Computational modeling and simulation provide grounds for the quantitative testing and comparison of theoretical predictions and observed data, and might serve as a strategy to organize the rather complicated and diverse pool of data and methodologies used in sleep research. This review article outlines the emerging progress in the computational modeling and simulation of the main theories on the role of sleep in memory consolidation.

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.

Sleep Promotes, and Sleep Loss Inhibits, Selective Changes in Firing Rate, Response Properties and Functional Connectivity of Primary Visual Cortex Neurons

Recent studies suggest that sleep differentially alters the activity of cortical neurons based on firing rates during preceding wake—increasing the firing rates of sparsely firing neurons and decreasing those of faster firing neurons. Because sparsely firing cortical neurons may play a specialized role in sensory processing, sleep could facilitate sensory function via selective actions on sparsely firing neurons. To test this hypothesis, we analyzed longitudinal electrophysiological recordings of primary visual cortex (V1) neurons across a novel visual experience which induces V1 plasticity (or a control experience which does not), and a period of subsequent ad lib sleep or partial sleep deprivation. We find that across a day of ad lib sleep, spontaneous and visually-evoked firing rates are selectively augmented in sparsely firing V1 neurons. These sparsely firing neurons are more highly visually responsive, and show greater orientation selectivity than their high firing rate neighbors. They also tend to be “soloists” instead of “choristers”—showing relatively weak coupling of firing to V1 population activity. These population-specific changes in firing rate are blocked by sleep disruption either early or late in the day, and appear to be brought about by increases in neuronal firing rates across bouts of rapid eye movement (REM) sleep. Following a patterned visual experience that induces orientation-selective response potentiation (OSRP) in V1, sparsely firing and weakly population-coupled neurons show the highest level of sleep-dependent response plasticity. Across a day of ad lib sleep, population coupling strength increases selectively for sparsely firing neurons—this effect is also disrupted by sleep deprivation. Together, these data suggest that sleep may optimize sensory function by augmenting the functional connectivity and firing rate of highly responsive and stimulus-selective cortical neurons, while simultaneously reducing noise in the network by decreasing the activity of less selective, faster-firing neurons.

Transient and Persistent UP States during Slow-wave Oscillation and their Implications for Cell-Assembly Dynamics

The membrane potentials of cortical neurons in vivo exhibit spontaneous fluctuations between a depolarized UP state and a resting DOWN state during the slow-wave sleeps or in the resting states. This oscillatory activity is believed to engage in memory consolidation although the underlying mechanisms remain unknown. Recently, it has been shown that UP-DOWN state transitions exhibit significantly different temporal profiles in different cortical regions, presumably reflecting differences in the underlying network structure. Here, we studied in computational models whether and how the connection configurations of cortical circuits determine the macroscopic network behavior during the slow-wave oscillation. Inspired by cortical neurobiology, we modeled three types of synaptic weight distributions, namely, log-normal, sparse log-normal and sparse Gaussian. Both analytic and numerical results suggest that a larger variance of weight distribution results in a larger chance of having significantly prolonged UP states. However, the different weight distributions only produce similar macroscopic behavior. We further confirmed that prolonged UP states enrich the variety of cell assemblies activated during these states. Our results suggest the role of persistent UP states for the prolonged repetition of a selected set of cell assemblies during memory consolidation.

Circadian rhythm and sleep-wake systems share the dynamic extracellular synaptic milieu

The mammalian circadian and sleep-wake systems are closely aligned through their coordinated regulation of daily activity patterns. Although they differ in their anatomical organization and physiological processes, they utilize overlapping regulatory mechanisms that include an assortment of proteins and molecules interacting within the extracellular space. These extracellular factors include proteases that interact with soluble proteins, membrane-attached receptors and the extracellular matrix; and cell adhesion molecules that can form complex scaffolds connecting adjacent neurons, astrocytes and their respective intracellular cytoskeletal elements. Astrocytes also participate in the dynamic regulation of both systems through modulating neuronal appositions, the extracellular space and/or through release of gliotransmitters that can further contribute to the extracellular signaling processes. Together, these extracellular elements create a system that integrates rapid neurotransmitter signaling across longer time scales and thereby adjust neuronal signaling to reflect the daily fluctuations fundamental to both systems. Here we review what is known about these extracellular processes, focusing specifically on areas of overlap between the two systems. We also highlight questions that still need to be addressed. Although we know many of the extracellular players, far more research is needed to understand the mechanisms through which they modulate the circadian and sleep-wake systems.

Sleep loss disrupts Arc expression in dentate gyrus neurons

Sleep loss affects many aspects of cognition, and memory consolidation processes occurring in the hippocampus seem particularly vulnerable to sleep loss. The immediate-early gene Arc plays an essential role in both synaptic plasticity and memory formation, and its expression is altered by sleep. Here, using a variety of techniques, we have characterized the effects of brief (3-h) periods of sleep vs. sleep deprivation (SD) on the expression of Arc mRNA and Arc protein in the mouse hippocampus and cortex. By comparing the relative abundance of mature Arc mRNA with unspliced pre-mRNA, we see evidence that during SD, increases in Arc across the cortex, but not hippocampus, reflect de novo transcription. Arc increases in the hippocampus during SD are not accompanied by changes in pre-mRNA levels, suggesting that increases in mRNA stability, not transcription, drives this change. Using in situ hybridization (together with behavioral observation to quantify sleep amounts), we find that in the dorsal hippocampus, SD minimally affects Arc mRNA expression, and decreases the number of dentate gyrus (DG) granule cells expressing Arc. This is in contrast to neighboring cortical areas, which show large increases in neuronal Arc expression after SD. Using immunohistochemistry, we find that Arc protein expression is also differentially affected in the cortex and DG with SD - while larger numbers of cortical neurons are Arc+, fewer DG granule cells are Arc+, relative to the same regions in sleeping mice. These data suggest that with regard to expression of plasticity-regulating genes, sleep (and SD) can have differential effects in hippocampal and cortical areas. This may provide a clue regarding the susceptibility of performance on hippocampus-dependent tasks to deficits following even brief periods of sleep loss.

A brief period of sleep deprivation causes spine loss in the dentate gyrus of mice

Sleep and sleep loss have a profound impact on hippocampal function, leading to memory impairments. Modifications in the strength of synaptic connections directly influences neuronal communication, which is vital for normal brain function, as well as the processing and storage of information. In a recently published study, we found that as little as five hours of sleep deprivation impaired hippocampus-dependent memory consolidation, which was accompanied by a reduction in dendritic spine numbers in hippocampal area CA1. Surprisingly, loss of sleep did not alter the spine density of CA3 neurons. Although sleep deprivation has been reported to affect the function of the dentate gyrus, it is unclear whether a brief period of sleep deprivation impacts spine density in this region. Here, we investigated the impact of a brief period of sleep deprivation on dendritic structure in the dentate gyrus of the dorsal hippocampus. We found that five hours of sleep loss reduces spine density in the dentate gyrus with a prominent effect on branched spines. Interestingly, the inferior blade of the dentate gyrus seems to be more vulnerable in terms of spine loss than the superior blade. This decrease in spine density predominantly in the inferior blade of the dentate gyrus may contribute to the memory deficits observed after sleep loss, as structural reorganization of synaptic networks in this subregion is fundamental for cognitive processes.

Resonance with subthreshold oscillatory drive organizes activity and optimizes learning in neural networks

Networks of neurons need to reliably encode and replay patterns and sequences of activity. In the brain, sequences of spatially coding neurons are replayed in both the forward and reverse direction in time with respect to their order in recent experience. As of yet there is no network-level or biophysical mechanism known that can produce both modes of replay within the same network. Here we propose that resonance, a property of neurons, paired with subthreshold oscillations in neural input facilitate network-level learning of fixed and sequential activity patterns and lead to both forward and reverse replay.

Network oscillations across and within brain areas are critical for learning and performance of memory tasks. While a large amount of work has focused on the generation of neural oscillations, their effect on neuronal populations’ spiking activity and information encoding is less known. Here, we use computational modeling to demonstrate that a shift in resonance responses can interact with oscillating input to ensure that networks of neurons properly encode new information represented in external inputs to the weights of recurrent synaptic connections. Using a neuronal network model, we find that due to an input current-dependent shift in their resonance response, individual neurons in a network will arrange their phases of firing to represent varying strengths of their respective inputs. As networks encode information, neurons fire more synchronously, and this effect limits the extent to which further “learning” (in the form of changes in synaptic strength) can occur. We also demonstrate that sequential patterns of neuronal firing can be accurately stored in the network; these sequences are later reproduced without external input (in the context of subthreshold oscillations) in both the forward and reverse directions (as has been observed following learning in vivo). To test whether a similar mechanism could act in vivo, we show that periodic stimulation of hippocampal neurons coordinates network activity and functional connectivity in a frequency-dependent manner. We conclude that resonance with subthreshold oscillations provides a plausible network-level mechanism to accurately encode and retrieve information without overstrengthening connections between neurons.

Sleep State Dependence of Optogenetically evoked Responses in Neuronal Nitric Oxide Synthase-positive Cells of the Cerebral Cortex

Slow-wave activity (SWA) in the electroencephalogram during slow-wave sleep (SWS) varies as a function of sleep-wake history. A putative sleep-active population of neuronal nitric oxide synthase (nNOS)-containing interneurons in the cerebral cortex, defined as such by the expression of Fos in animals euthanized after protracted deep sleep, may be a local regulator of SWA. We investigated whether electrophysiological responses to activation of these cells are consistent with their role of a local regulator of SWA. Using a Cre/loxP strategy, we targeted the population of nNOS interneurons to express the light-activated cation channel Channelrhodopsin2 and the histological marker tdTomato in mice. We then performed histochemical and optogenetic studies in these transgenic mice. Our studies provided histochemical evidence of transgene expression and electrophysiological evidence that the cerebral cortex was responsive to optogenetic manipulation of these cells in both anesthetized and behaving mice. Optogenetic stimulation of the cerebral cortex of animals expressing Channelrhodopsin2 in nNOS interneurons triggered an acute positive deflection of the local field potential that was followed by protracted oscillatory events only during quiet wake and slow wave sleep. The response during wake was maximal when the electroencephalogram (EEG) was in a negative polarization state and abolished when the EEG was in a positive polarization state. Since the polarization state of the EEG is a manifestation of slow-wave oscillations in the activity of underlying pyramidal neurons between the depolarized (LFP negative) and hyperpolarized (LFP positive) states, these data indicate that sleep-active cortical neurons expressing nNOS function in sleep slow-wave physiology.

Homeostatic Changes in GABA and Acetylcholine Muscarinic Receptors on GABAergic Neurons in the Mesencephalic Reticular Formation following Sleep Deprivation

We have examined whether GABAergic neurons in the mesencephalic reticular formation (RFMes), which are believed to inhibit the neurons in the pons that generate paradoxical sleep (PS or REMS), are submitted to homeostatic regulation under conditions of sleep deprivation (SD) by enforced waking during the day in mice. Using immunofluorescence, we investigated first, by staining for c-Fos, whether GABAergic RFMes neurons are active during SD and then, by staining for receptors, whether their activity is associated with homeostatic changes in GABA_A or acetylcholine muscarinic type 2 (AChM2) receptors (Rs), which evoke inhibition. We found that a significantly greater proportion of the GABAergic neurons were positively stained for c-Fos after SD (∼27%) as compared to sleep control (SC; ∼1%) and sleep recovery (SR; ∼6%), suggesting that they were more active during waking with SD and less active or inactive during sleep with SC and SR. The density of GABA_ARs and AChM2Rs on the plasma membrane of the GABAergic neurons was significantly increased after SD and restored to control levels after SR. We conclude that the density of these receptors is increased on RFMes GABAergic neurons during presumed enhanced activity with SD and is restored to control levels during presumed lesser or inactivity with SR. Such increases in GABA_AR and AChM2R with sleep deficits would be associated with increased susceptibility of the wake-active GABAergic neurons to inhibition from GABAergic and cholinergic sleep-active neurons and to thus permitting the onset of sleep and PS with muscle atonia.

Rapid Eye Movement Sleep, Sleep Continuity and Slow Wave Sleep as Predictors of Cognition, Mood, and Subjective Sleep Quality in Healthy Men and Women, Aged 20-84 Years

Sleep and its sub-states are assumed to be important for brain function across the lifespan but which aspects of sleep associate with various aspects of cognition, mood and self-reported sleep quality has not yet been established in detail. Sleep was quantified by polysomnography, quantitative Electroencephalogram (EEG) analysis and self-report in 206 healthy men and women, aged 20-84 years, without sleep complaints. Waking brain function was quantified by five assessments scheduled across the day covering objectively assessed performance across cognitive domains including sustained attention and arousal, decision and response time, motor and sequence control, working memory, and executive function as well as self-reports of alertness, mood and affect. Controlled for age and sex, self-reported sleep quality was negatively associated with number of awakenings and positively associated with the duration of Rapid Eye Movement (REM) sleep, but no significant associations with Slow Wave Sleep (SWS) measures were observed. Controlling only for age showed that associations between objective and subjective sleep quality were much stronger in women than in men. Analysis of 51 performance measures demonstrated that, after controlling for age and sex, fewer awakenings and more REM sleep were associated significantly with better performance on the Goal Neglect task, which is a test of executive function. Factor analysis of the individual performance measures identified four latent variables labeled Mood/Arousal, Response Time, Accuracy, and Visual Perceptual Sensitivity. Whereas Mood/Arousal improved with age, Response Times became slower, while Accuracy and Visual perceptual sensitivity showed little change with age. After controlling for sex and age, nominally significant association between sleep and factor scores were observed such that Response Times were faster with more SWS, and Accuracy was reduced where individuals woke more often or had less REM sleep. These data identify a positive contribution of SWS to processing speed and in particular highlight the importance of sleep continuity and REM sleep for subjective sleep quality and performance accuracy across the adult lifespan. These findings warrant further investigation of the contribution of sleep continuity and REM sleep to brain function.