955 resultados para Sleep-wake cycle


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Our previously observations showed that the amplitude of cortical evoked potentials to irrelevant auditory stimulus (probe) recorded from several different cerebral areas was differentially modulated by brain states. At present study, we simultaneously re

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BACKGROUND: Scale-invariant neuronal avalanches have been observed in cell cultures and slices as well as anesthetized and awake brains, suggesting that the brain operates near criticality, i.e. within a narrow margin between avalanche propagation and extinction. In theory, criticality provides many desirable features for the behaving brain, optimizing computational capabilities, information transmission, sensitivity to sensory stimuli and size of memory repertoires. However, a thorough characterization of neuronal avalanches in freely-behaving (FB) animals is still missing, thus raising doubts about their relevance for brain function. METHODOLOGY/PRINCIPAL FINDINGS: To address this issue, we employed chronically implanted multielectrode arrays (MEA) to record avalanches of action potentials (spikes) from the cerebral cortex and hippocampus of 14 rats, as they spontaneously traversed the wake-sleep cycle, explored novel objects or were subjected to anesthesia (AN). We then modeled spike avalanches to evaluate the impact of sparse MEA sampling on their statistics. We found that the size distribution of spike avalanches are well fit by lognormal distributions in FB animals, and by truncated power laws in the AN group. FB data surrogation markedly decreases the tail of the distribution, i.e. spike shuffling destroys the largest avalanches. The FB data are also characterized by multiple key features compatible with criticality in the temporal domain, such as 1/f spectra and long-term correlations as measured by detrended fluctuation analysis. These signatures are very stable across waking, slow-wave sleep and rapid-eye-movement sleep, but collapse during anesthesia. Likewise, waiting time distributions obey a single scaling function during all natural behavioral states, but not during anesthesia. Results are equivalent for neuronal ensembles recorded from visual and tactile areas of the cerebral cortex, as well as the hippocampus. CONCLUSIONS/SIGNIFICANCE: Altogether, the data provide a comprehensive link between behavior and brain criticality, revealing a unique scale-invariant regime of spike avalanches across all major behaviors.

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The relationship between sleep and epilepsy is both complex and clinically significant. Temporal lobe epilepsy (TLE) influences sleep architecture, while sleep plays an important role in facilitating and/or inhibiting possible epileptic seizures. The pilocarpine experimental model reproduces several features of human temporal lobe epilepsy and is one of the most widely used models in basic research. The aim of the present study was to characterize, behaviorally and electrophysiologically, the phases of sleep-wake cycles (SWC) in male rats with pilocarpine-induced epilepsy. Epileptic rats presented spikes in all phases of the SWC as well as atypical cortical synchronization during attentive wakefulness and paradoxical sleep. The architecture of the sleep-wake phases was altered in epileptic rats, as was the integrity of the SWC. Because our findings reproduce many relevant features observed in patients with epilepsy, this model is suitable to study sleep dysfunction in epilepsy. (C) 2009 Elsevier Inc. All rights reserved.

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The development of circadian sleep-wakefulness rhythm was investigated by a longitudinal study of six normal infants. We propose an entropy based measure for the sleep/wake cycle fragmentation. Our results confirm that the sleep/wake cycle fragmentation and the sleep/wake ratio decrease, while the circadian power increases during the maturation process of infants. In addition to these expected linear trends in the variables devised to quantify sleep consolidation, circadian power and sleep/wake ratio, we found that they present infradian rhythms in the monthly range. (C) 2009 Elsevier B.V. All rights reserved.

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College students usually exhibit an irregular sleep-wake cycle characterized by great phase delays on weekends and short sleep length on weekdays. As the temporal organization of social activities is an important synchronizer of human biological rhythms, we investigated the role played by study's schedules and work on the sleep-wake cycle. Three groups of female college students were investigated: (1) no-job morning group, (2) no-job evening group, (3) job evening group. The volunteers answered a sleep questionnaire in the classroom. The effects of day of the week and group on the sleep schedules and sleep length were analyzed by a two way ANOVA for repeated measures. The three groups showed delays in the wake up time on weekends. No-job evening and morning groups also delayed bedtime, but the job evening group slept at the same time on weekdays as on weekends. Sleep length increased on weekends for morning group and job evening group, whereas the no-job evening group maintained the amount of sleep from weekdays to weekends. This survey showed that the tendency of phase delay on weekends was differently expressed according to study's schedules and work.

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In addition to cognitive decline, individuals affected by Alzheimer's disease (AD) can experience important neuropsychiatric symptoms including sleep disturbances. We characterized the sleep-wake cycle in the TgCRND8 mouse model of AD, which overexpresses a mutant human form of amyloid precursor protein resulting in high levels of β-amyloid and plaque formation by 3 months of age. Polysomnographic recordings in freely-moving mice were conducted to study sleep-wake cycle architecture at 3, 7 and 11 months of age and corresponding levels of β-amyloid in brain regions regulating sleep-wake states were measured. At all ages, TgCRND8 mice showed increased wakefulness and reduced non-rapid eye movement (NREM) sleep during the resting and active phases. Increased wakefulness in TgCRND8 mice was accompanied by a shift in the waking power spectrum towards fast frequency oscillations in the beta (14-20 Hz) and low gamma range (20-50 Hz). Given the phenotype of hyperarousal observed in TgCRND8 mice, the role of noradrenergic transmission in the promotion of arousal, and previous work reporting an early disruption of the noradrenergic system in TgCRND8, we tested the effects of the alpha-1-adrenoreceptor antagonist, prazosin, on sleep-wake patterns in TgCRND8 and non-transgenic (NTg) mice. We found that a lower dose (2 mg/kg) of prazosin increased NREM sleep in NTg but not in TgCRND8 mice, whereas a higher dose (5 mg/kg) increased NREM sleep in both genotypes, suggesting altered sensitivity to noradrenergic blockade in TgCRND8 mice. Collectively our results demonstrate that amyloidosis in TgCRND8 mice is associated with sleep-wake cycle dysfunction, characterized by hyperarousal, validating this model as a tool towards understanding the relationship between β-amyloid overproduction and disrupted sleep-wake patterns in AD.

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Earlier extracellular recordings during natural sleep have shown that, during slow-wave sleep (SWS), neocortical neurons display long-lasting periods of silence, whereas they are tonically active and discharge at higher rates during waking and sleep with rapid eye movements (REMs). We analyzed the nature of long-lasting periods of neuronal silence in SWS and the changes in firing rates related to ocular movements during REM sleep and waking using intracellular recordings from electrophysiologically identified neocortical neurons in nonanesthetized and nonparalyzed cats. We found that the silent periods during SWS are associated with neuronal hyperpolarizations, which are due to a mixture of K+ currents and disfacilitation processes. Conventional fast-spiking neurons (presumably local inhibitory interneurons) increased their firing rates during REMs and eye movements in waking. During REMs, the firing rates of regular-spiking neurons from associative areas decreased and intracellular traces revealed numerous, short-lasting, low-amplitude inhibitory postsynaptic potentials (IPSPs), that were reversed after intracellular chloride infusion. In awake cats, regular-spiking neurons could either increase or decrease their firing rates during eye movements. The short-lasting IPSPs associated with eye movements were still present in waking; they preceded the spikes and affected their timing. We propose that there are two different forms of firing rate control: disfacilitation induces long-lasting periods of silence that occur spontaneously during SWS, whereas active inhibition, consisting of low-amplitude, short-lasting IPSPs, is prevalent during REMs and precisely controls the timing of action potentials in waking.

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We consider a wireless sensor network whose main function is to detect certain infrequent alarm events, and to forward alarm packets to a base station, using geographical forwarding. The nodes know their locations, and they sleep-wake cycle, waking up periodically but not synchronously. In this situation, when a node has a packet to forward to the sink, there is a trade-off between how long this node waits for a suitable neighbor to wake up and the progress the packet makes towards the sink once it is forwarded to this neighbor. Hence, in choosing a relay node, we consider the problem of minimizing average delay subject to a constraint on the average progress. By constraint relaxation, we formulate this next hop relay selection problem as a Markov decision process (MDP). The exact optimal solution (BF (Best Forward)) can be found, but is computationally intensive. Next, we consider a mathematically simplified model for which the optimal policy (SF (Simplified Forward)) turns out to be a simple one-step-look-ahead rule. Simulations show that SF is very close in performance to BF, even for reasonably small node density. We then study the end-to-end performance of SF in comparison with two extremal policies: Max Forward (MF) and First Forward (FF), and an end-to-end delay minimising policy proposed by Kim et al. 1]. We find that, with appropriate choice of one hop average progress constraint, SF can be tuned to provide a favorable trade-off between end-to-end packet delay and the number of hops in the forwarding path.

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Adolescents usually exhibit late sleep phase and irregular sleep patterns. As a result, they do not get enough sleep and report daytime sleepiness. This condition could be aggravated in working students who have a more limited time for sleep. In this survey, we investigated the impact of evening classes and employment on the sleep patterns of adolescents. We compared female (n = 17) and male (n = 14) non-worker students to female (n = 28) and male (n = 20) worker students who attended the same high school. The volunteers (aged 17.4 years +/- 11 months) answered a sleep log during a 16-day period. Worker students slept and woke up earlier, had a shorter nocturnal sleep length and a shorter daily (nocturnal plus diurnal) sleep length compared to non-working pupils. The four groups of students delayed sleep onset time on weekends, but only worker students delayed wake-up time on Sundays. The wake-up time was similar among groups on Sundays. While student workers tended to increase the sleep length in the weekends, non-working students increased it on Mondays and/or Tuesdays. The results showed that sleep schedules and sleep length were different according to the work status. Going to bed later on Saturday by the four groups of students suggests the influence of social activities, while a later wake-up time on Sundays could result from a shorter sleep length on workdays.

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This project described sleep-wake behaviour in community-dwelling older adults and in community dementia care. It examined the applicability of a newly presented conceptual model (the Multifactorial Influences on Sleep Health model) to evaluate factors influencing sleep in ageing, with a particular focus on the importance of daytime light exposure and the impact of partners.

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We consider the problem of quickest detection of an intrusion using a sensor network, keeping only a minimal number of sensors active. By using a minimal number of sensor devices, we ensure that the energy expenditure for sensing, computation and communication is minimized (and the lifetime of the network is maximized). We model the intrusion detection (or change detection) problem as a Markov decision process (MDP). Based on the theory of MDP, we develop the following closed loop sleep/wake scheduling algorithms: (1) optimal control of Mk+1, the number of sensors in the wake state in time slot k + 1, (2) optimal control of qk+1, the probability of a sensor in the wake state in time slot k + 1, and an open loop sleep/wake scheduling algorithm which (3) computes q, the optimal probability of a sensor in the wake state (which does not vary with time), based on the sensor observations obtained until time slot k. Our results show that an optimum closed loop control on Mk+1 significantly decreases the cost compared to keeping any number of sensors active all the time. Also, among the three algorithms described, we observe that the total cost is minimum for the optimum control on Mk+1 and is maximum for the optimum open loop control on q.

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We study a sensor node with an energy harvesting source. In any slot,the sensor node is in one of two modes: Wake or Sleep. The generated energy is stored in a buffer. The sensor node senses a random field and generates a packet when it is awake. These packets are stored in a queue and transmitted in the wake mode using the energy available in the energy buffer. We obtain energy management policies which minimize a linear combination of the mean queue length and the mean data loss rate. Then, we obtain two easily implementable suboptimal policies and compare their performance to that of the optimal policy. Next, we extend the Throughput Optimal policy developed in our previous work to sensors with two modes. Via this policy, we can increase the through put substantially and stabilize the data queue by allowing the node to sleep in some slots and to drop some generated packets. This policy requires minimal statistical knowledge of the system. We also modify this policy to decrease the switching costs.

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We consider the problem of quickest detection of an intrusion using a sensor network, keeping only a minimal number of sensors active. By using a minimal number of sensor devices,we ensure that the energy expenditure for sensing, computation and communication is minimized (and the lifetime of the network is maximized). We model the intrusion detection (or change detection) problem as a Markov decision process (MDP). Based on the theory of MDP, we develop the following closed loop sleep/wake scheduling algorithms: 1) optimal control of Mk+1, the number of sensors in the wake state in time slot k + 1, 2) optimal control of qk+1, the probability of a sensor in the wake state in time slot k + 1, and an open loop sleep/wake scheduling algorithm which 3) computes q, the optimal probability of a sensor in the wake state (which does not vary with time),based on the sensor observations obtained until time slot k.Our results show that an optimum closed loop control onMk+1 significantly decreases the cost compared to keeping any number of sensors active all the time. Also, among the three algorithms described, we observe that the total cost is minimum for the optimum control on Mk+1 and is maximum for the optimum open loop control on q.

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Our work is motivated by geographical forwarding of sporadic alarm packets to a base station in a wireless sensor network (WSN), where the nodes are sleep-wake cycling periodically and asynchronously. We seek to develop local forwarding algorithms that can be tuned so as to tradeoff the end-to-end delay against a total cost, such as the hop count or total energy. Our approach is to solve, at each forwarding node enroute to the sink, the local forwarding problem of minimizing one-hop waiting delay subject to a lower bound constraint on a suitable reward offered by the next-hop relay; the constraint serves to tune the tradeoff. The reward metric used for the local problem is based on the end-to-end total cost objective (for instance, when the total cost is hop count, we choose to use the progress toward sink made by a relay as the reward). The forwarding node, to begin with, is uncertain about the number of relays, their wake-up times, and the reward values, but knows the probability distributions of these quantities. At each relay wake-up instant, when a relay reveals its reward value, the forwarding node's problem is to forward the packet or to wait for further relays to wake-up. In terms of the operations research literature, our work can be considered as a variant of the asset selling problem. We formulate our local forwarding problem as a partially observable Markov decision process (POMDP) and obtain inner and outer bounds for the optimal policy. Motivated by the computational complexity involved in the policies derived out of these bounds, we formulate an alternate simplified model, the optimal policy for which is a simple threshold rule. We provide simulation results to compare the performance of the inner and outer bound policies against the simple policy, and also against the optimal policy when the source knows the exact number of relays. Observing the good performance and the ease of implementation of the simple policy, we apply it to our motivating problem, i.e., local geographical routing of sporadic alarm packets in a large WSN. We compare the end-to-end performance (i.e., average total delay and average total cost) obtained by the simple policy, when used for local geographical forwarding, against that obtained by the globally optimal forwarding algorithm proposed by Kim et al. 1].

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The aim in this paper is to allocate the `sleep time' of the individual sensors in an intrusion detection application so that the energy consumption from the sensors is reduced, while keeping the tracking error to a minimum. We propose two novel reinforcement learning (RL) based algorithms that attempt to minimize a certain long-run average cost objective. Both our algorithms incorporate feature-based representations to handle the curse of dimensionality associated with the underlying partially-observable Markov decision process (POMDP). Further, the feature selection scheme used in our algorithms intelligently manages the energy cost and tracking cost factors, which in turn assists the search for the optimal sleeping policy. We also extend these algorithms to a setting where the intruder's mobility model is not known by incorporating a stochastic iterative scheme for estimating the mobility model. The simulation results on a synthetic 2-d network setting are encouraging.