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Integrated information (Φ) is a measure of the cause-effect power of a physical system. This paper investigates the relationship between Φ as defined in Integrated Information Theory and state differentiation ([Formula: see text]), the number of, and difference between potential system states. Here we provide theoretical justification of the relationship between Φ and [Formula: see text], then validate the results using a simulation study. First, we show that a physical system in a state with high Φ necessarily has many elements and specifies many causal relationships. Furthermore, if the average value of integrated information across all states is high, the system must also have high differentiation. Next, we explore the use of [Formula: see text] as a proxy for Φ using artificial networks, evolved to have integrated structures. The results show a positive linear relationship between Φ and [Formula: see text] for multiple network sizes and connectivity patterns. Finally we investigate the differentiation evoked by sensory inputs and show that, under certain conditions, it is possible to estimate integrated information without a direct perturbation of its internal elements. In concluding, we discuss the need for further validation on larger networks and explore the potential applications of this work to the empirical study of consciousness, especially concerning the practical estimation of Φ from neuroimaging data.

Uncovering the neural basis of consciousness is a major challenge to neuroscience. In this Perspective, Tononi and colleagues describe the integrated information theory of consciousness and how it might be used to answer outstanding questions about the nature of consciousness. In this Opinion article, we discuss how integrated information theory accounts for several aspects of the relationship between consciousness and the brain. Integrated information theory starts from the essential properties of phenomenal experience, from which it derives the requirements for the physical substrate of consciousness. It argues that the physical substrate of consciousness must be a maximum of intrinsic cause–effect power and provides a means to determine, in principle, the quality and quantity of experience. The theory leads to some counterintuitive predictions and can be used to develop new tools for assessing consciousness in non-communicative patients.

Exploration of a novel environment leads to neuronal DNA double-strand breaks (DSBs). These DSBs are generated by type 2 topoisomerase to relieve topological constrains that limit transcription of plasticity-related immediate early genes. If not promptly repaired, however, DSBs may lead to cell death. Since the induction of plasticity-related genes is higher in wake than in sleep, we asked whether it is specifically wake associated with synaptic plasticity that leads to DSBs, and whether sleep provides any selective advantage over wake in their repair. In flies and mice, we find that enriched wake, more than simply time spent awake, induces DSBs, and their repair in mice is delayed or prevented by subsequent wake. In both species the repair of irradiation-induced neuronal DSBs is also quicker during sleep, and mouse genes mediating the response to DNA damage are upregulated in sleep. Thus, sleep facilitates the repair of neuronal DSBs.

Cortical circuits mature in stages, from early synaptogenesis and synaptic pruning to late synaptic refinement, resulting in the adult anatomical connection matrix. Because the mature matrix is largely fixed, genetic or environmental factors interfering with its establishment can have irreversible effects. Sleep disruption is rarely considered among those factors, and previous studies have focused on very young animals and the acute effects of sleep deprivation on neuronal morphology and cortical plasticity. Adolescence is a sensitive time for brain remodeling, yet whether chronic sleep restriction (CSR) during adolescence has long-term effects on brain connectivity remains unclear. We used viral-mediated axonal labeling and serial two-photon tomography to measure brain-wide projections from secondary motor cortex (MOs), a high-order area with diffuse projections. For each MOs target, we calculated the projection fraction, a combined measure of passing fibers and axonal terminals normalized for the size of each target. We found no homogeneous differences in MOs projection fraction between mice subjected to 5 days of CSR during early adolescence (P25–P30, ≥50% decrease in daily sleep,n=14) and siblings that slept undisturbed (n=14). Machine learning algorithms, however, classified animals at significantly above chance levels, indicating that differences between the two groups exist, but are subtle and heterogeneous. Thus, sleep disruption in early adolescence may affect adult brain connectivity. However, because our method relies on a global measure of projection density and was not previously used to measure connectivity changes due to behavioral manipulations, definitive conclusions on the long-term structural effects of early CSR require additional experiments.

Learning leads to rapid microstructural changes in gray (GM) and white (WM) matter. Do these changes continue to accumulate if task training continues, and can they be reverted by sleep? We addressed these questions by combining structural and diffusion weighted MRI and high-density EEG in 16 subjects studied during the physiological sleep/wake cycle, after 12 h and 24 h of intense practice in two different tasks, and after post-training sleep. Compared to baseline wake, 12 h of training led to a decline in cortical mean diffusivity. The decrease became even more significant after 24 h of task practice combined with sleep deprivation. Prolonged practice also resulted in decreased ventricular volume and increased GM and WM subcortical volumes. All changes reverted after recovery sleep. Moreover, these structural alterations predicted cognitive performance at the individual level, suggesting that sleep's ability to counteract performance deficits is linked to its effects on the brain microstructure. The cellular mechanisms that account for the structural effects of sleep are unknown, but they may be linked to its role in promoting the production of cerebrospinal fluid and the decrease in synapse size and strength, as well as to its recently discovered ability to enhance the extracellular space and the clearance of brain metabolites.

Pseudocode for the major algorithms of Integrated Information Theory

Sleep is traditionally constituted of two global behavioral states, non-rapid eye movement (NREM) and rapid eye movement (REM), characterized by quiescence and reduced responsiveness to sensory stimuli [1]. NREM sleep is distinguished by slow waves and spindles throughout the cerebral cortex and REM sleep by an "activated," low-voltage fast electroencephalogram (EEG) paradoxically similar to that of wake, accompanied by rapid eye movements and muscle atonia. However, recent evidence has shown that cortical activity patterns during wake and NREM sleep are not as global as previously thought. Local slow waves can appear in various cortical regions in both awake humans [2] and rodents [3-5]. Intracranial recordings in humans [6] and rodents [4, 7] have shown that NREM sleep slow waves most often involve only a subset of brain regions that varies from wave to wave rather than occurring near synchronously across all cortical areas. Moreover, some cortical areas can transiently "wake up" [8] in an otherwise sleeping brain. Yet until now, cortical activity during REM sleep was thought to be homogenously wake-like. We show here, using local laminar recordings in freely moving mice, that slow waves occur regularly during REM sleep, but only in primary sensory and motor areas and mostly in layer 4, the main target of relay thalamic inputs, and layer 3. This finding may help explain why, during REM sleep, we remain disconnected from the environment even though the bulk of the cortex shows wake-like, paradoxical activation.

Study Objectives We have recently shown higher parietal-occipital EEG gamma activity during sleep in long-term meditators compared to meditation-naive individuals. This gamma increase was specific for NREM sleep, was present throughout the entire night and correlated with meditation expertise, thus suggesting underlying long-lasting neuroplastic changes induced through prolonged training. The aim of this study was to explore the neuroplastic changes acutely induced by 2 intensive days of different meditation practices in the same group of practitioners. We also repeated baseline recordings in a meditation-naive cohort to account for time effects on sleep EEG activity. Design High-density EEG recordings of human brain activity were acquired over the course of whole sleep nights following intervention. Setting Sound-attenuated sleep research room. Patients or Participants Twenty-four long-term meditators and twenty-four meditation-naive controls. Interventions Two 8-h sessions of either a mindfulness-based meditation or a form of meditation designed to cultivate compassion and loving kindness, hereafter referred to as compassion meditation. Measurements and Results We found an increase in EEG low-frequency oscillatory activities (1–12 Hz, centered around 7–8 Hz) over prefrontal and left parietal electrodes across whole night NREM cycles. This power increase peaked early in the night and extended during the third cycle to high-frequencies up to the gamma range (25–40 Hz). There was no difference in sleep EEG activity between meditation styles in long-term meditators nor in the meditation naive group across different time points. Furthermore, the prefrontal-parietal changes were dependent on meditation life experience. Conclusions This low-frequency prefrontal-parietal activation likely reflects acute, meditation-related plastic changes occurring during wakefulness, and may underlie a top-down regulation from frontal and anterior parietal areas to the posterior parietal and occipital regions showing chronic, long-lasting plastic changes in long-term meditators.

The activity of the noradrenergic system of the locus coeruleus (LC) is high in wake and low in sleep. LC promotes arousal and EEG activation, as well as attention, working memory, and cognitive flexibility. These functions rely on prefrontal cortex and are impaired by sleep deprivation, but the extent to which LC activity changes during wake remains unclear. Moreover, it is unknown whether noradrenergic neurons can sustain elevated firing during extended wake. Recent studies show that relative to LC neurons targeting primary motor cortex (M1), those projecting to medial prefrontal cortex (mPFC) have higher spontaneous firing rates and are more excitable. These results suggest that noradrenaline (NA) levels should be higher in mPFC than M1, and that during prolonged wake LC cells targeting mPFC may fatigue more, but direct evidence is lacking.We performed in vivo microdialysis experiments in adult (9-10 weeks old) C57BL/6 mice implanted for chronic electroencephalographic recordings. Cortical NA levels were measured during spontaneous sleep and wake (n = 8 mice), and in the course of sleep deprivation (n = 6).We found that absolute NA levels are higher in mPFC than in M1. Moreover, in both areas they decline during sleep and increase during wake, but these changes are faster in M1 than mPFC. Finally, by the end of sleep deprivation NA levels decline only in mPFC.Locus coeruleus (LC) neurons targeting prefrontal cortex may fatigue more markedly, or earlier, than other LC cells, suggesting one of the mechanisms underlying the cognitive impairment and the increased sleep presure associated with sleep deprivation.A commentary on this article appears in this issue on page 11.

Sleep brings about the most dramatic change in consciousness we are all familiar with. Consciousness nearly fades during deep sleep early in the night, and returns later on in the form of dreams despite our virtual disconnection from the outside world. Meanwhile, the brain goes through an orderly progression of changes in neural activity, epitomized by the occurrence of slow oscillations and spindles. There are also local changes in the activation of many brain regions, as indicated by imaging studies. This chapter considers sleep stages and cycles, brain centers regulating wakefulness and sleep, the neural correlates of wakefulness and sleep including changes in spontaneous neural activity and in metabolism, as well as changes in responsiveness to stimuli. Next, it reviews changes in the level of consciousness during sleep, and considers recent findings concerning the underlying mechanisms. Finally, this chapter examines how consciousness changes during dreaming and discusses the underlying neuropsychology, possible neurocognitive models, as well as the development of dreams. This overview ends with a consideration of dissociated states such as daydreaming, lucid dreaming, sleepwalking, rapid eye movement sleep behavioral disorders, and narcolepsy.

This final chapter provides the reader with an overview of the neurological and neurobiological literature on the neural substrate of consciousness. First, the chapter reviews the evidence suggesting that consciousness can be dissociated from other brain functions, such as responsiveness to sensory inputs, motor control, attention, language, memory, reflection, spatial frames of reference, the body, and perhaps even the self. The chapter then summarizes what has been learned by studying global changes in the level of consciousness, such as sleep, anesthesia, seizures, and post-comatose states. Next, it asks what can be said at this point about the neuroanatomy and the neurophysiology of consciousness. The chapter ends by briefly considering how a theoretical analysis of the fundamental properties of consciousness can complement neurobiological studies.

Study Objective: It is well established that brain metabolism is higher during wake and rapid eye movement (REM) sleep than in nonrapid eye movement (NREM) sleep. Most of the brain’s energy is used to maintain neuronal firing and glutamatergic transmission. Recent evidence shows that cortical firing rates, extracellular glutamate levels, and markers of excitatory synaptic strength increase with time spent awake and decline throughout NREM sleep. These data imply that the metabolic cost of each behavioral state is not fixed but may reflect sleep-wake history, a possibility that is investigated in the current report. Design: Chronic (4d) electroencephalographic (EEG) recordings in the rat cerebral cortex were coupled with fixed-potential amperometry to monitor the extracellular concentration of oxygen ([oxy]) and lactate ([lac]) on a second-by-second basis across the spontaneous sleep-wake cycle and in response to sleep deprivation. Setting: Basic sleep research laboratory. Patients or Participants: Wistar Kyoto (WKY) adult male rats. Interventions: N/A. Measurements and Results: Within 30-60 sec [lac] and [oxy] progressively increased during wake and REM sleep and declined during NREM sleep (n = 10 rats/metabolite), but with several differences. [Oxy], but not [lac], increased more during wake with high motor activity and/or elevated EEG high-frequency power. Meanwhile, only the NREM decline of [lac] reflected sleep pressure as measured by slow-wave activity, mirroring previous results for cortical glutamate. Conclusions: The observed state-dependent changes in cortical [lac] and [oxy] are consistent with higher brain metabolism during waking and REM sleep in comparison with NREM sleep. Moreover, these data suggest that glycolytic activity, most likely through its link with glutamatergic transmission, reflects sleep homeostasis.

Some people commit violent acts while asleep. In seeking to understand their brain states, scientists and physicians are investigating the murky borders of consciousness

Patients surviving severe brain injury may regain consciousness without recovering their ability to understand, move and communicate. Recently, electrophysiological and neuroimaging approaches, employing simple sensory stimulations or verbal commands, have proven useful in detecting higher order processing and, in some cases, in establishing some degree of communication in brain-injured subjects with severe impairment of motor function. To complement these approaches, it would be useful to develop methods to detect recovery of consciousness in ways that do not depend on the integrity of sensory pathways or on the subject's ability to comprehend or carry out instructions. As suggested by theoretical and experimental work, a key requirement for consciousness is that multiple, specialized cortical areas can engage in rapid causal interactions (effective connectivity). Here, we employ transcranial magnetic stimulation together with high-density electroencephalography to evaluate effective connectivity at the bedside of severely brain injured, non-communicating subjects. In patients in a vegetative state, who were open-eyed, behaviourally awake but unresponsive, transcranial magnetic stimulation triggered a simple, local response indicating a breakdown of effective connectivity, similar to the one previously observed in unconscious sleeping or anaesthetized subjects. In contrast, in minimally conscious patients, who showed fluctuating signs of non-reflexive behaviour, transcranial magnetic stimulation invariably triggered complex activations that sequentially involved distant cortical areas ipsi- and contralateral to the site of stimulation, similar to activations we recorded in locked-in, conscious patients. Longitudinal measurements performed in patients who gradually recovered consciousness revealed that this clear-cut change in effective connectivity could occur at an early stage, before reliable communication was established with the subject and before the spontaneous electroencephalogram showed significant modifications. Measurements of effective connectivity by means of transcranial magnetic stimulation combined with electroencephalography can be performed at the bedside while by-passing subcortical afferent and efferent pathways, and without requiring active participation of subjects or language comprehension; hence, they offer an effective way to detect and track recovery of consciousness in brain-injured patients who are unable to exchange information with the external environment.

Converging lines of evidence strongly support a role for sleep in brain plasticity. An elegant idea that may explain how sleep accomplishes this role is the "synaptic homeostasis hypothesis (SHY)." According to SHY, sleep promotes net synaptic weakening which offsets net synaptic strengthening that occurs during wakefulness. SHY is intuitively appealing because it relates the homeostatic regulation of sleep to an important function (synaptic plasticity). SHY has also received important experimental support from recent studies in Drosophila melanogaster. There remain, however, a number of unanswered questions about SHY. What is the cellular mechanism governing SHY? How does it fit with what we know about plasticity mechanisms in the brain? In this review, I discuss the evidence and theory of SHY in the context of what is known about Hebbian and non-Hebbian synaptic plasticity. I conclude that while SHY remains an elegant idea, the underlying mechanisms are mysterious and its functional significance unknown.

We recently showed that adaptive logic-gate networks ('animats') increase their capacity to integrate information as they adapt to environments of increasing complexity [1]. The animats' task environments consisted of falling blocks of different sizes, which had to be either caught or avoided. When the animats were exposed to more difficult task environments that required more internal memory, they developed more complex networks ('brains'), as indicated by a larger number of irreducible internal mechanisms ('concepts') and higher integrated conceptual information (Φ) [2,3]. Animats with brains of high Φ outperformed animats with modular or feedforward brains because they could pack a larger number of mechanisms for the same number of nodes and connections. Here we investigate whether this key feature of animats of high Φ leads to greater flexibility in adapting to environmental changes. We selected animats with integrated (Φ > 0) or modular (Φ = 0) structures that had adapted perfectly, within 30,000 generations, to a particular task environment. We then enhanced the difficulty of the task environment by adding blocks of various sizes that were to be caught or avoided in a way that was either: i) congruent with the old task, ii) neutral to the old task, or iii) incongruent with the old task, reversing the previous rules. For congruent changes in the task environment, the fitness of animats with integrated brains and many irreducible mechanisms, as compared to modular animats, (1) dropped less right after the change, (2) recovered faster, and (3) maintained higher values even after 30,000 generations in the new environment. These results corroborate the hypothesis that brains with high capacity for information integration may provide an evolutionary advantage in complex, changing environments.

Adapting movements to a visual rotation involves the activation of right posterior parietal areas. Further performance improvement requires an increase of slow wave activity in subsequent sleep in the same areas. Here we ascertained whether a post-learning trace is present in wake EEG and whether such a trace is influenced by sleep slow waves.In two separate sessions, we recorded high-density EEG in 17 healthy subjects before and after a visuomotor rotation task, which was performed both before and after sleep. High-density EEG was recorded also during sleep. One session aimed to suppress sleep slow waves, while the other session served as a control.After learning, we found a trace in the eyes-open wake EEG as a local, parietal decrease in alpha power. After the control night, this trace returned to baseline levels, but it failed to do so after slow wave deprivation. The overnight change of the trace correlated with the dissipation of low frequency (8 Hz) NREM sleep activity only in the control session.Visuomotor learning leaves a trace in the wake EEG alpha power that appears to be renormalized by sleep slow waves.These findings link visuomotor learning to regional changes in wake EEG and sleep homeostasis.

Objective: Sleep homeostasis is altered in major depressive disorder (MDD). Pre- to postsleep decline in waking auditory evoked potential (AEP) amplitude has been correlated with sleep slow wave activity (SWA), suggesting that overnight changes in waking AEP amplitude are homeostatically regulated in healthy individuals. This study investigated whether the overnight change in waking AEP amplitude and its relation to SWA is altered in MDD. Method: Using 256-channel high-density electroencephalography, all-night sleep polysomnography and single-tone waking AEPs pre- and postsleep were collected in 15 healthy controls (HC) and 15 non-medicated individuals with MDD. Results: N1 and P2 amplitudes of the waking AEP declined after sleep in the HC group, but not in MDD. The reduction in N1 amplitude also correlated with fronto-central SWA in the HC group, but a comparable relationship was not found in MDD, despite equivalent SWA between groups. No pre- to postsleep differences were found for N1 or P2 latencies in either group. These findings were not confounded by varying levels of alertness or differences in sleep variables between groups. Conclusion: MDD involves altered sleep homeostasis as measured by the overnight change in waking AEP amplitude. Future research is required to determine the clinical implications of these findings.

Intelligent behavior of computers continues to improve, but these machines are still far removed from being conscious of the world around them. Computer scientists and neurobiologists like to ponder a related question with both a technical and metaphysical bent: Will we even be able to tell when a machine is truly conscious? A simple test, which can be performed at home with this magazine and a pair of scissors, may ascertain whether such a machine has finally arrived.

Sleep is a behavioral state ideal for studying functional connectivity because it minimizes many sources of between-subject variability that confound waking analyses. This is particularly important for potential connectivity studies in mental illness where cognitive ability, internal milieu and active psychotic symptoms can vary widely across subjects. We, therefore, sought to adapt techniques applied to magnetoencephalography for use in high-density electroencephalography (EEG), the gold-standard in brain-recording methods during sleep. Autoregressive integrative moving average modeling was used to reduce spurious correlations between recording sites (electrodes) in order to identify functional networks. We hypothesized that identified network characteristics would be similar to those found with magnetoencephalography, and would demonstrate sleep stage-related differences in a control population. We analysed 60-s segments of low-artifact data from seven healthy human subjects during wakefulness and sleep. EEG analysis of eyes-closed wakefulness revealed widespread nearest-neighbor positive synchronous interactions, similar to magnetoencephalography, though less consistent across subjects. Rapid eye movement sleep demonstrated positive synchronous interactions akin to wakefulness but weaker. Slow-wave sleep (SWS), instead, showed strong positive interactions in a large left fronto-temporal-parietal cluster markedly more consistent across subjects. Comparison of connectivity from early SWS to SWS from a later sleep cycle indicated sleep-related reduction in connectivity in this region. The consistency of functional connectivity during SWS within and across subjects suggests this may be a promising technique for comparing functional connectivity between mental illness and health.

Objective Intracortical inhibition (SICI) and facilitation (ICF) in the human motor cortex can be measured using a paired pulse transcranial magnetic stimulation (ppTMS) protocol. Recently, a technical device has been introduced, which allows recording electroencephalographic (EEG) responses to TMS of a given scalp site. The latency, amplitude and scalp topography of such responses are considered a reflection of cortico-cortical connectivity and functional state. The aim of the present study is to better characterize the neuronal circuits underlying motor cortex connectivity as well as the mechanisms regulating its balance between inhibition and facilitation by means of EEG navigated-ppTMS coregistration. Methods Sub-threshold and supra-threshold single and ppTMS of the left primary motor cortex were carried out during a multi-channel EEG recording on 8 healthy volunteers; the between-pulse intervals used in the paired pulse trials were 3 (for SICI) and 11 ms (for ICF). Motor evoked potentials (MEPs) from the opposite hand were simultaneously recorded. Results Single and ppTMS induced EEG responses characterized by a sequence of negative deflections peaking at approximately 7, 18, 44, 100 and 280 ms alternated with positive peaks at approximately 13, 30, 60 and 190 ms post-TMS. Moreover, ppTMS modulated both EEG evoked activity and MEPs. Amplitude variability of EEG responses was correlated with – and therefore might partially explain – amplitude variability of MEPs. Interpretation EEG-ppTMS is a promising tool to better characterize the neuronal circuits underlying cortical effective connectivity as well as the mechanisms regulating the balance between inhibition and facilitation within the human cortices and the corticospinal pathway.

THE MOST PROMINENT ELECTROENCEPHALOGRAPHIC (EEG) CHARACTERISTIC OF DEEP NON-RAPID EYE MOVEMENT (NREM) SLEEP IS SLOW WAVES. THE neuronal correlate of slow waves is the slow oscillation first described in detail by Steriade and coworkers, who showed that membrane potentials of cortical neurons alternate about every second between a depolarized upstate and a hyperpolarized downstate during slow oscillations.1 When these oscillations are near synchronous and involve the majority of the cortical neurons in a given region, they become visible in the surface EEG as slow waves of large amplitude. The alternation of upstates and downstates in cortical neurons is thought to be involved in memory consolidation,2,3 synaptic homeostasis,4 and the recuperative function of sleep.5 Slow waves in the sleep EEG undergo dramatic changes during brain maturation. As early as the 1980s, Feinberg and colleagues reported that the amplitude of slow waves increases until shortly before puberty and then shows a prominent decrease across adolescence.6 Sleep slow-wave activity (SWA, EEG spectral power between 0.5 and 4.5 Hz), a quantitative measure of the activity in the slow-wave frequency range reflecting the homeostatic regulation of sleep,5 follows a similar time course. SWA sharply declines across puberty,7–9 followed by a smaller decrease during the twenties.6,10 The brain undergoes striking morphologic and functional changes during early human development.11–13 For example, Huttenlocher and Dabholkar11 reported that maximum cortical synapse density is achieved shortly before puberty. Furthermore, the course of adolescence is accompanied by a reduction in density of synapses in the grey matter, a process termed pruning.14–17 Longitudinal neuroimaging findings of cortical maturation corroborate these findings and reveal concomitant changes in grey matter. For instance, grey-matter volume has been shown to be greatest in frontal regions of the cortex around early puberty and to decrease thereafter.18,19 With the exception of the temporal lobe, the majority of the other cortical areas show a similar time course.18 In view of the similar time course of cortical synapse density and the amplitude of sleep slow waves, some authors have proposed a link between the two.6,8,9 In fact, recent computer simulations of the thalamocortical system have provided evidence for an association of the number and strength of synapses involved in the generation of sleep slow waves and their amplitude.20 In this simulation, the morphology of sleep slow waves changed as a function of the level of synchronization of cortical neurons. The synchronization level, on the other hand, depended on synaptic strength and synaptic density. Thus, the stronger and denser synapses are on neurons of a certain population, the faster those neurons can synchronize their activity; the faster they show synchronized activity, the steeper is the resulting potential change within that population. It is those potential changes that are measured by EEG recordings.21 Therefore, this computer model implied that the slope of slow waves is a good measure of the synchronization level of the neurons in the cortex, i.e., the slope of slow waves may represent a direct electrophysiological measure of changes in synaptic strength or density.20 On the basis of this observation, we hypothesized that the higher slow-wave amplitude in prepubertal children, compared with mature adolescents, results from higher synaptic strength or density, which should also be seen as a steeper slope of sleep slow waves. Therefore, we compare the characteristics of sleep slow waves in prepubertal children and mature adolescents. With this study, we propose the analysis of slow-wave characteristics as a novel approach to assessing developmental differences in overall cortical structure with possible functional associations.

The consolidation of memories in a variety of learning processes benefits from post-training sleep, and recent work has suggested a role for sleep slow wave activity (SWA). Previous studies using a visuomotor learning task showed a local increase in sleep SWA in right parietal cortex, which was correlated with post-sleep performance enhancement. In these as in most similar studies, learning took place in the evening, shortly before sleep. Thus, it is currently unknown whether learning a task in the morning, followed by the usual daily activities, would also result in a local increase in sleep SWA during the night, and in a correlated enhancement in performance the next day. To answer this question, a group of subjects performed a visuomotor learning task in the morning and was retested the following morning. Whole night sleep was recorded with high-density EEG. We found an increase of SWA over the right posterior parietal areas that was most evident during the second sleep cycle. Performance improved significantly the following morning, and the improvement was positively correlated with the SWA increase in the second sleep cycle. These results suggest that training-induced changes in sleep SWA and post-sleep improvements do not depend upon the time interval between original training and sleep.

Dreams are a most remarkable experiment in psychology and neuroscience, conducted every night in every sleeping person. They show that our brain, disconnected from the environment, can generate by itself an entire world of conscious experiences. Content analysis and developmental studies have furthered our understanding of dream phenomenology. In parallel, brain lesion studies, functional imaging, and neurophysiology have advanced our knowledge of the neural basis of dreaming. It is now possible to start integrating these two strands of research in order to address some fundamental questions that dreams pose for cognitive neuroscience: how conscious experiences in sleep relate to underlying brain activity; why the dreamer is largely disconnected from the environment; and whether dreaming is more closely related to mental imagery or to perception.

BackgroundMany recent studies have used repetitive transcranial magnetic stimulation (rTMS) to study brain-behavior relationships. However, the pulse-to-pulse neural effects of rapid delivery of multiple TMS pulses are unknown largely because of TMS-evoked electrical artifacts limiting recording of brain activity.ObjectiveIn this study, TMS-related artifacts were removed with independent component analysis (ICA), which allowed for the investigation of the neurophysiologic effects of rTMS with simultaneous electroencephalographic (EEG) recordings.MethodsRepetitive TMS trains of 10 Hz, 3 seconds (110% of motor threshold) were delivered to the postcentral gyrus and superior parietal lobule in 16 young adults. Simultaneous EEG recordings were made with a TMS-compatible system. The stereotypical pattern of TMS-related electrical artifacts was identified by ICA.ResultsRemoval of artifacts allowed for identification of a series of five evoked brain potentials occurring within 100 milliseconds of each TMS pulse. With the exception of the first potential, for both areas targeted, there was a quadratic relationship between potential peak amplitude and pulse number within the TMS train. This was characterized by a decrease, followed by a rise in amplitude.ConclusionsICA is an effective method for removal of TMS-evoked electrical artifacts in EEG data. With the use of this procedure we found that the physiologic responses to TMS pulses delivered in a high-frequency train of pulses are not independent. The sensitivity of the magnitude of these responses to recent stimulation history suggests a complex recruitment of multiple neuronal events with different temporal dynamics.

STUDY OBJECTIVES: Sleep after learning often benefits memory consolidation, but the underlying mechanisms remain unclear. In previous studies, we found that learning a visuomotor task is followed by an increase in sleep slow wave activity (SWA, the electroencephalographic [EEG] power density between 0.5 and 4.5 Hz during non-rapid eye movement sleep) over the right parietal cortex. The SWA increase correlates with the postsleep improvement in visuomotor performance, suggesting that SWA may be causally responsible for the consolidation of visuomotor learning. Here, we tested this hypothesis by studying the effects of slow wave deprivation (SWD). DESIGN: After learning the task, subjects went to sleep, and acoustic stimuli were timed either to suppress slow waves (SWD) or to interfere as little as possible with spontaneous slow waves (control acoustic stimulation, CAS). SETTING: Sound-attenuated research room. PARTICIPANTS: Healthy subjects (mean age 24.6 +/- 1.0 years; n = 9 for EEG analysis, n = 12 for behavior analysis; 3 women). MEASUREMENTS AND RESULTS: Sleep time and efficiency were not affected, whereas SWA and the number of slow waves decreased in SWD relative to CAS. Relative to the night before, visuomotor performance significantly improved in the CAS condition (+5.93% +/- 0.88%) but not in the SWD condition (-0.77% +/- 1.16%), and the direct CAS vs SWD comparison showed a significant difference (P = 0.0007, n = 12, paired t test). Changes in visuomotor performance after SWD were correlated with SWA changes over right parietal cortex but not with the number of arousals identified using clinically established criteria, nor with any sign of "EEG lightening" identified using a novel automatic method based on event-related spectral perturbation analysis. CONCLUSION: These results support a causal role for sleep slow waves in sleep-dependent improvement of visuomotor performance.

Sleep need is affected by developmental stage and neuronal plasticity, but the underlying mechanisms remain unclear. The fragile X mental retardation geneFmr1, whose loss-of-function mutation causes the most common form of inherited mental retardation in humans, is involved in synaptogenesis and synaptic plasticity, and its expression depends on both developmental stage and waking experience.Fmr1is highly conserved across species andDrosophilamutants carryingdFmr1loss-of-function or gain-of-function mutations are well characterized: amorphs have overgrown dendritic trees with larger synaptic boutons, developmental defects in pruning, and enhanced neurotransmission, while hypermorphs show opposite defects, including dendritic and axonal underbranching and loss of synapse differentiation. We find here thatdFmr1amorphs are long sleepers and hypermorphs are short sleepers, while both show increased locomotor activity and shortened lifespan. Both amorphs and hypermorphs also show abnormal sleep homeostasis, with impaired waking performance and no sleep rebound after sleep deprivation. An impairment in the circadian regulation of sleep cannot account for the altered sleep phenotype ofdFmr1mutants, nor can an abnormal activation of glutamatergic metabotropic receptors. Moreover, overexpression ofdFmr1throughout the mushroom bodies is sufficient to reduce sleep. Finally,dFmr1protein levels are modulated by both developmental stage and behavioral state, with increased expression immediately after eclosure and after prolonged wakefulness. Thus,dFmr1expression dose-dependently affects both sleep and synapses, suggesting that changes in sleep time indFmr1mutants may derive from changes in synaptic physiology.

Considerable biochemical evidence suggests that the protein kinase C (PKC) signaling cascade may be a convergent point for the actions of anti-manic agents, and that excessive PKC activation can disrupt prefrontal cortical regulation of thinking and behavior. To date, however, brain protein targets of PKC's anti-manic effects have not been fully identified. Here we showed that PKC activity was enhanced in the prefrontal cortex of animals treated with the psychostimulant amphetamine. Phosphorylation of MARCKS, a marker of PKC activity, was increased in the prefrontal cortex of animals treated with the psychostimulant amphetamine, as well as in sleep-deprived animals (another animal model of mania), but decreased in lithium-treated animals. The antidepressant imipramine, which shows pro-manic properties in patients with bipolar disorder (BPD), also enhanced phospho-MARCKS in prefrontal cortex in vivo. We further explored the functional targets of PKC in mania-associated behaviors. Neurogranin is a brain-specific, postsynaptically located PKC substrate. PKC phosphorylation of neurogranin was robustly increased by pro-manic manipulations and decreased by anti-manic agents. PKC phosphorylation of the NMDA receptor site GluN1S896 and the AMPA receptor site GluA1T840 was also enhanced in the prefrontal cortex of animals treated with the antidepressant imipramine, as well as in behaviorally sleep-deprived animals, in striking contrast to the reduced activity seen in lithium-treated animals. These results suggest that PKC may play an important role in regulating NMDA and AMPA receptor functions. The biochemical profile of the PKC pathway thus encompasses both pro- and anti-manic effects on behavior. These results suggest that PKC modulators or their intracellular targets may ultimately represent novel avenues for the development of new therapeutics for mood disorders.

The integrated information theory (IIT) starts from phenomenology and makes use of thought experi- ments to claim that consciousness is integrated information. Specifically: (i) the quantity of consciousness corresponds to the amount of integrated information generated by a complex of elements; (ii) the quality of experience is spec- ified by the set of informational relationships generated within that complex. Integrated information () is defined as the amount of information generated by a complex of elements, above and beyond the information generated by its parts. Qualia space (Q) is a space where each axis represents a possible state of the complex, each point is a probability distribution of its states, and arrows between points represent the informational relationships among its elements generated by causal mechanisms (connections). Together, the set of informational relationships within a complex constitute a shape in Q that completely and univo- cally specifies a particular experience. Several observations concerning the neural substrate of consciousness fall natu- rally into place within the IIT framework. Among them are the association of consciousness with certain neural systems rather than with others; the fact that neural processes un- derlying consciousness can influence or be influenced by neural processes that remain unconscious; the reduction of consciousness during dreamless sleep and generalized sei- zures; and the distinct role of different cortical architectures in affecting the quality of experience. Equating conscious- ness with integrated information carries several implications for our view of nature.

Functional neuroimaging studies have produced contradictory data about the extent to which specific regions of the frontal and the posterior parietal cortices contribute to the retention of information in spatial working memory. We used high frequency repetitive transcranial magnetic stimulation (rTMS) to assess the necessity for the short-term retention of spatial information of brain areas identified by previous functional imaging studies: dorsolateral prefrontal cortex (dlPFC), frontal eye fields (FEF), superior parietal lobule (SPL) and intraparietal sulcus (IPS). 10 Hz rTMS spanned the 3-sec delay period of a spatial delayed-recognition task. The postcentral gyrus (PCG) was included to control for any regionally nonspecific effects of rTMS. The only regionally specific effect was a significant decrease in reaction time when rTMS was applied to SPL. Additionally, rTMS lowered accuracy to a greater extent when applied to left than to right hemisphere, and was more disruptive when applied contralaterally vs. ipsilaterally to the visual field in which the memory probe was presented. Although seemingly paradoxical, the finding of rTMS-induced improvement in task performance has a precedent, and is consistent with the idea that regions associated with spatial sensory-motor processing make necessary contributions to the short-term retention of this information. Possible factors underlying rTMS-induced behavioral facilitation are considered.

Consciousness fades during deep nonrapid eye movement (NREM) sleep early in the night, yet cortical neurons remain active, keep receiving sensory inputs, and can display patterns of synchronous activity. Why then does consciousness fade? According to the integrated information theory of consciousness, what is critical for consciousness is not firing rates, sensory input, or synchronization per se, but rather the ability of a system to integrate information. If consciousness is the capacity to integrate information, then the brain should be able to generate consciousness to the extent that it has a large repertoire of available states (information), yet it cannot be decomposed into a collection of causally independent subsystems (integration). A key prediction stemming from this hypothesis is that such ability should be greatly reduced in deep NREM sleep; the dreamless brain either breaks down into causally independent modules, shrinks its repertoire of possible responses, or both. In this article, we report the results of a series of experiments in which we employed a combination of transcranial magnetic stimulation and high-density electroencephalography (TMS/hd-EEG) to directly test this prediction in humans. Altogether, TMS/hdEEG measurements suggest that the sleeping brain, despite being active and reactive, loses its ability of entering states that are both integrated and differentiated; it either breaks down in causally independent modules, responding to TMS with a short and local activation, or it bursts into an explosive and aspecific response, producing a full-fledged slow wave.

This review examines recent advances in the study of brain correlates of consciousness. First, we briefly discuss some useful distinctions between consciousness and other brain functions. We then examine what has been learned by studying global changes in the level of consciousness, such as sleep, anesthesia, and seizures. Next we consider some of the most common paradigms used to study the neural correlates for specific conscious percepts and examine what recent findings say about the role of different brain regions in giving rise to consciousness for that percept. Then we discuss dynamic aspects of neural activity, such as sustained versus phasic activity, feedforward versus reentrant activity, and the role of neural synchronization. Finally, we briefly consider how a theoretical analysis of the fundamental properties of consciousness can usefully complement neurobiological studies.

SLOW WAVES ARE A PROMINENT FEATURE OF NON-RAPID EYE MOVEMENT (NREM) SLEEP THAT CAN BE OBSERVED IN THE ELECTROENCEPHALOGRAM (EEG) and local field potentials (LFP). Slow-wave activity (SWA, EEG power 0.5–4.0 Hz) provides a reliable indicator of sleep need, as it increases as a function of prior waking and declines during sleep.1–3 Although the homeostatic regulation of SWA is suggestive of a restorative function of sleep, the underlying mechanisms remain unknown. Different mechanisms can be conceived that might lead to a progressive decline in SWA during sleep, such as an increase in the level of arousal-promoting neuromodulators or a reduction of accumulated metabolites (e.g., adenosine). A recent proposal suggests that the level of SWA may reflect the strength of corticocortical synapses and that a progressive reduction in synaptic strength during sleep would be associated with a corresponding decrease in SWA.4, 5 Mechanistically, stronger cortical connections would produce stronger network synchronization and thus a higher level of SWA, whereas weaker connections would reduce network synchronization and thereby SWA. Connections would become. on average, stronger at the end of a waking day due to synaptic potentiation associated with learning and would weaken during sleep due to sleep-dependent synaptic depression, as suggested by molecular and other evidence.4, 5 Supporting the hypothesis, procedures associated with synaptic potentiation and depression in local cortical areas lead to corresponding changes in sleep SWA. For example, sleep SWA increases over right parietal cortex after a visuomotor learning task6 and decreases over the right sensorimotor cortex after immobilization of the left arm.7 In this paper, we employed a large-scale computer model of the cat thalamocortical system to investigate in detail the relationship between synaptic strength and SWA. The model incorporates key aspects of the neuroanatomic organization of visual thalamocortical circuits, including more than 65,000 integrate-and-fire neurons organized into multiple cortical, thalamic, and reticular areas, and produces physiologically realistic sleep activity patterns.8 Specifically, due to the interaction between several intrinsic and synaptic currents, simulated neurons undergo slow oscillations at around 1 Hz between depolarized periods of activity (up states) and hyperpolarized periods of silence (down states), as observed in intracellular recordings in vivo.9, 10 These single-cell oscillations are synchronized by corticocortical connections and produce realistic slow waves in the calculated LFP. On the basis of this model, we examined the dynamics of single-cell oscillations, cortical synchronization, and LFP slow waves under conditions with a high or low strength of corticocortical connections. We show here that a reduction in cortical synaptic strength leads to a decrease in sleep SWA, a decreased incidence of large-amplitude slow waves, a decrease in their slope, and an increase in the number of multipeak waves. In 2 companion papers, we tested the predictions of the model by examining how slow waves change between early-sleep and late-sleep conditions using LFP recordings in rats11 and high-density EEG recordings in humans.12

STUDY OBJECTIVE: Sleep slow-wave activity (SWA, EEG power between 0.5 and 4.0 Hz) decreases homeostatically in the course of non-rapid eye movement sleep (NREM) sleep. According to a recent hypothesis, the homeostatic decrease of sleep SWA is due to a progressive decrease in the strength of corticocortical connections. This hypothesis was evaluated in a large-scale thalamocortical model, which showed that a decrease in synaptic strength, implemented through a reduction of postsynaptic currents, resulted in lower sleep SWA in simulated local field potentials (LFP). The decrease in SWA was associated with a decreased proportion of high-amplitude slow waves, a decreased slope of the slow waves, and an increase in the number of multipeak waves. Here we tested the model predictions by obtaining LFP recordings from the rat cerebral cortex and comparing conditions of high homeostatic sleep pressure (early sleep) and low homeostatic sleep pressure (late sleep). DESIGN: Intracortical LFP recordings during baseline sleep and after 6 hours of sleep deprivation. SETTING: Basic sleep research laboratory. PATIENTS OR PARTICIPANTS: WKY adult male rats. INTERVENTIONS: N/A. MEASUREMENTS AND RESULTS: Early sleep (sleep at the beginning of the major sleep phase, sleep immediately after sleep deprivation) was associated with (1) high SWA, (2) many large slow waves, (3) steep slope of slow waves, and (4) rare occurrence of multipeak waves. By contrast, late sleep (sleep at the end of the major sleep phase, sleep several hours after the end of sleep deprivation) was associated with (1) low SWA, (2) few high-amplitude slow waves, (3) reduced slope of slow waves, and (4) more frequent multipeak waves. CONCLUSION: In rats, changes in sleep SWA are associated with changes in the amplitude and slope of slow waves, and in the number of multi-peak waves. Such changes in slow-wave parameters are compatible with the hypothesis that average synaptic strength decreases in the course of sleep.

What are the precise brain regions supporting the short-term retention of verbal information? A previous functional magnetic resonance imaging (fMRI) study suggested that they may be topographically variable across individuals, occurring, in most, in regions posterior to prefrontal cortex (PFC), and that detection of these regions may be best suited to a single-subject (SS) approach to fMRI analysis (Feredoes and Postle, 2007). In contrast, other studies using spatially normalized group-averaged (SNGA) analyses have localized storage-related activity to PFC. To evaluate the necessity of the regions identified by these two methods, we applied repetitive transcranial magnetic stimulation (rTMS) to SS- and SNGA-identified regions throughout the retention period of a delayed letter-recognition task. Results indicated that rTMS targeting SS analysis-identified regions of left perisylvian and sensorimotor cortex impaired performance, whereas rTMS targeting the SNGA-identified region of left caudal PFC had no effect on performance. Our results support the view that the short-term retention of verbal information can be supported by regions associated with acoustic, lexical, phonological, and speech-based representation of information. They also suggest that the brain bases of some cognitive functions may be better detected by SS than by SNGA approaches to fMRI data analysis.

Benzodiazepines are commonly used medications that alter sleep spindles during non-rapid eye movement (NREM) sleep, however the topographic changes to these functionally significant waveforms have yet to be fully elucidated. This study utilized high-density electroencephalography (hdEEG) to investigate topographic changes in sleep spindles and spindle-range activity caused by temazepam during NREM sleep in 18 healthy adults. After an accommodation night, sleep for all participants was recorded on two separate nights after taking either placebo or oral temazepam 15 mg. Sleep was monitored using 256-channel hdEEG. Spectral analysis and spindle waveform detection of sleep EEG data were performed for each participant night. Global and topographic data were subsequently compared between temazepam and placebo conditions. Temazepam was associated with significant increases in spectral power from 10.33 to 13.83 Hz. Within this frequency band, temazepam broadly increased sleep spindle duration, and topographically increased spindle amplitude and density in frontal and central-posterior regions, respectively. Higher frequency sleep spindles demonstrated increased spindle amplitude and a paradoxical decrease in spindle density in frontal and centroparietal regions. Further analysis demonstrated temazepam both slowed the average frequency of spindle waveforms and increased the relative proportion of spindles at peak frequencies in frontal and centroparietal regions. These findings suggest that benzodiazepines have diverse effects on sleep spindles that vary by frequency and cortical topography. Further research that explores the relationships between topographic and frequency-dependent changes in pharmacologically-induced sleep spindles and the functional effects of these waveforms is indicated.

Sleeping brain activity reflects brain anatomy and physiology. The aim of this study was to use high density (256 channel) electroencephalography (EEG) during sleep to characterize topographic changes in sleep EEG power across normal aging, with high spatial resolution. Sleep was evaluated in 92 healthy adults aged 18-65 years old using full polysomnography and high density EEG. After artifact removal, spectral power density was calculated for standard frequency bands for all channels, averaged across the NREM periods of the first 3 sleep cycles. To quantify topographic changes with age, maps were generated of the Pearson's coefficient of the correlation between power and age at each electrode. Significant correlations were determined by statistical non-parametric mapping. Absolute slow wave power declined significantly with increasing age across the entire scalp, whereas declines in theta and sigma power were significant only in frontal regions. Power in fast spindle frequencies declined significantly with increasing age frontally, whereas absolute power of slow spindle frequencies showed no significant change with age. When EEG power was normalized across the scalp, a left centro-parietal region showed significantly less age-related decline in power than the rest of the scalp. This partial preservation was particularly significant in the slow wave and sigma bands. The effect of age on sleep EEG varies substantially by region and frequency band. This non-uniformity should inform the design of future investigations of aging and sleep. This study provides normative data on the effect of age on sleep EEG topography, and provides a basis from which to explore the mechanisms of normal aging as well as neurodegenerative disorders for which age is a risk factor.

Are rapid eye movements (REMs) in sleep associated with visual-like activity, as during wakefulness? Here we examine single-unit activities (n=2,057) and intracranial electroencephalography across the human medial temporal lobe (MTL) and neocortex during sleep and wakefulness, and during visual stimulation with fixation. During sleep and wakefulness, REM onsets are associated with distinct intracranial potentials, reminiscent of ponto-geniculate-occipital waves. Individual neurons, especially in the MTL, exhibit reduced firing rates before REMs as well as transient increases in firing rate immediately after, similar to activity patterns observed upon image presentation during fixation without eye movements. Moreover, the selectivity of individual units is correlated with their response latency, such that units activated after a small number of images or REMs exhibit delayed increases in firing rates. Finally, the phase of theta oscillations is similarly reset following REMs in sleep and wakefulness, and after controlled visual stimulation. Our results suggest that REMs during sleep rearrange discrete epochs of visual-like processing as during wakefulness., Since the discovery of rapid eye movements (REMs), a critical question endures as to whether they represent time points at which visual-like processing is updated. Here the authors demonstrate that cortical activity during sleep REMs shares many properties with that observed during saccades and vision.

In the two-process model of sleep regulation, slow-wave activity (SWA, i.e. the EEG power in the 0.5-4 Hz frequency band) is considered a direct indicator of sleep need. SWA builds up during non-rapid eye movement (NREM) sleep, declines before the onset of rapid-eye-movement (REM) sleep, remains low during REM and the level of increase in successive NREM episodes gets progressively lower. Sleep need dissipates with a speed that is proportional to SWA and can be characterized in terms of the initial sleep need, and the decay rate. The goal in this paper is to automatically characterize sleep need from a single EEG signal acquired at a frontal location. To achieve this, a highly specific and reasonably sensitive NREM detection algorithm is proposed that leverages the concept of a single-class Kernel-based classifier. Using automatic NREM detection, we propose a method to estimate the decay rate and the initial sleep need. This method was tested on experimental data from 8 subjects who recorded EEG during three nights at home. We found that on average the estimates of the decay rate and the initial sleep need have higher values when automatic NREM detection was used as compared to manual NREM annotation. However, the average variability of these estimates across multiple nights of the same subject was lower when the automatic NREM detection classifier was used. While this method slightly over estimates the sleep need parameters, the reduced variability across subjects makes it more effective for within subject statistical comparisons of a given sleep intervention.

Objective Changes in slow waves during non-rapid eye movement (NREM) sleep in response to acute total sleep deprivation are well-established measures of sleep homeostasis. This investigation utilized high-density electroencephalography (hdEEG) to examine topographic changes in slow waves during repeated partial sleep deprivation. Methods Twenty-four participants underwent a 6-day sleep restriction protocol. Spectral and period-amplitude analyses of sleep hdEEG data were used to examine changes in slow wave energy, count, amplitude, and slope relative to baseline. Results Changes in slow wave energy were dependent on the quantity of NREM sleep utilized for analysis, with widespread increases during sleep restriction and recovery when comparing data from the first portion of the sleep period, but restricted to recovery sleep if the entire sleep episode was considered. Period-amplitude analysis was less dependent on the quantity of NREM sleep utilized, and demonstrated topographic changes in the count, amplitude, and distribution of slow waves, with frontal increases in slow wave amplitude, numbers of high-amplitude waves, and amplitude/slopes of low amplitude waves resulting from partial sleep deprivation. Conclusions Topographic changes in slow waves occur across the course of partial sleep restriction and recovery. Significance These results demonstrate a homeostatic response to partial sleep loss in humans.

The brain regulates information flow by balancing the segregation and integration of incoming stimuli to facilitate flexible cognition and behaviour. The topological features of brain networks--in particular, network communities and hubs--support this segregation and integration but do not provide information about how external inputs are processed dynamically (that is, over time). Experiments in which the consequences of selective inputs on brain activity are controlled and traced with great precision could provide such information. However, such strategies have thus far had limited success. By contrast, recent whole-brain computational modelling approaches have enabled us to start assessing the effect of input perturbations on brain dynamics in silico. G.D. is supported by the European Research Council (ERC) Advanced grant: DYSTRUCTURE (no. 295129), by the Spanish Research Project SAF2010-16085, by the FP7-ICT BrainScales and by the Brain Network Recovery Group through the James S. McDonnell Foundation. G.T. is supported by the Paul Allen Family Foundation and by the James S. McDonnell Foundation. M.B. is supported by the Mind Science Foundation. M.L.K. is supported by the ERC Consolidator grant: CAREGIVING (no. 615539) and by the TrygFonden Charitable Foundation. The authors thank P. Maquet for agreeing to share the previously published sleep and wakefulness functional MRI data for the purposes of this article.

Deficits in both resting alpha-range (8-12Hz) electroencephalogram (EEG) activity and steady state evoked potential (SSVEP) responses have been reported in schizophrenia. However, the topographic specificity of these effects, the relationship between resting EEG and SSVEP, as well as the impact of antipsychotic medication on these effects, have not been clearly delineated. The present study sought to address these questions with 256 channel high-density EEG recordings in a group of 13 schizophrenia patients, 13 healthy controls, and 10 non-schizophrenia patients with psychiatric diagnoses currently taking antipsychotic medication. At rest, the schizophrenia group demonstrated decreased alpha EEG power in frontal and occipital areas relative to healthy controls. With SSVEP stimulation centered in the alpha band (10Hz), but not with stimulation above (15Hz) or below (7Hz) this range, the occipital deficit in alpha power was partially reverted. However, the frontal deficit persisted and contributed to a significantly reduced topographic relationship between occipital and frontal alpha activity for resting EEG and 10Hz SSVEP alpha power in schizophrenia patients. No significant differences were observed between healthy and medicated controls or between medicated controls and schizophrenia. These findings suggest a potential intrinsic deficit in frontal eyes-closed EEG alpha oscillations in schizophrenia, whereby potent visual stimulation centered in that frequency range results in an increase in the occipital alpha power of these patients, which however does not extend to frontal regions. Future research to evaluate the cortical and subcortical mechanisms of these effects is warranted.

Background PD (Parkinson's disease) is characterized by impairments in cortical plasticity, in beta frequency at rest and in beta power modulation during movement (i.e., event-related ERS [synchronization] and ERD [desynchronization]). Recent results with experimental protocols inducing long-term potentiation in healthy subjects suggest that cortical plasticity phenomena might be reflected by changes of beta power recorded with EEG during rest. Here, we determined whether motor practice produces changes in beta power at rest and during movements in both healthy subjects and patients with PD. We hypothesized that such changes would be reduced in PD. Methods We thus recorded EEG in patients with PD and age-matched controls before, during and after a 40-minute reaching task. We determined posttask changes of beta power at rest and assessed the progressive changes of beta ERD and ERS during the task over frontal and sensorimotor regions. Results We found that beta ERS and ERD changed significantly with practice in controls but not in PD. In PD compared to controls, beta power at rest was greater over frontal sensors but posttask changes, like those during movements, were far less evident. In both groups, kinematic characteristics improved with practice; however, there was no correlation between such improvements and the changes in beta power. Conclusions We conclude that prolonged practice in a motor task produces use-dependent modifications that are reflected in changes of beta power at rest and during movement. In PD, such changes are significantly reduced; such a reduction might represent, at least partially, impairment of cortical plasticity.

Background Astrocytes can mediate neurovascular coupling, modulate neuronal excitability, and promote synaptic maturation and remodeling. All these functions are likely to be modulated by the sleep/wake cycle, because brain metabolism, neuronal activity and synaptic turnover change as a function of behavioral state. Yet, little is known about the effects of sleep and wake on astrocytes. Results Here we show that sleep and wake strongly affect both astrocytic gene expression and ultrastructure in the mouse brain. Using translating ribosome affinity purification technology and microarrays, we find that 1.4 % of all astrocytic transcripts in the forebrain are dependent on state (three groups, sleep, wake, short sleep deprivation; six mice per group). Sleep upregulates a few select genes, like Cirp and Uba1, whereas wake upregulates many genes related to metabolism, the extracellular matrix and cytoskeleton, including Trio, Synj2 and Gem, which are involved in the elongation of peripheral astrocytic processes. Using serial block face scanning electron microscopy (three groups, sleep, short sleep deprivation, chronic sleep restriction; three mice per group, >100 spines per mouse, 3D), we find that a few hours of wake are sufficient to bring astrocytic processes closer to the synaptic cleft, while chronic sleep restriction also extends the overall astrocytic coverage of the synapse, including at the axon–spine interface, and increases the available astrocytic surface in the neuropil. Conclusions Wake-related changes likely reflect an increased need for glutamate clearance, and are consistent with an overall increase in synaptic strength when sleep is prevented. The reduced astrocytic coverage during sleep, instead, may favor glutamate spillover, thus promoting neuronal synchronization during non-rapid eye movement sleep. Electronic supplementary material The online version of this article (doi:10.1186/s12915-015-0176-7) contains supplementary material, which is available to authorized users.

Long-term sleep deprivation in rats produces dramatic physiological changes including increase in energy expenditure, decrease in body weight, and death after 2-3 weeks. Despite several studies, the sleep deprivation syndrome remains largely unexplained. Here, to elucidate how prolonged sleep loss affects brain cells we used microarrays and screened the expression of26 000 transcripts in the cerebral cortex. Rats were sleep deprived using the disk-over-water method for 1 week. Seventy-five transcripts showed increased expression in these animals relative to controls that had been spontaneously awake or sleep deprived for a few hours. Most of them were induced as a result of chronic sleep loss and not non-specific effects of the disk stimulation. They include transcripts coding for several immunoglobulins, stress response proteins (macrophage inhibitor factor-related protein 14, heat-shock protein 27, alpha-B-crystallin), minoxidil sulfotransferase, globins and cortistatin. Twenty-eight transcripts decreased their expression in long-term sleep-deprived rats. Sixteen of them were specifically decreased as a result of chronic sleep loss, including those coding for type I procollagen and dihydrolipoamide acetyltransferase. We also compared sleeping rats to short-term and long-term sleep-deprived rats, and found that acute and chronic sleep loss led to some differences at the molecular level. Several plasticity-related genes were strongly induced after acute sleep deprivation only, and several glial genes were down-regulated in both sleep deprivation conditions, but to a different extent. These findings suggest that sustained sleep loss may trigger a generalized inflammatory and stress response in the brain.