9 resultados para Nights at the circus

em Brock University, Canada


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The purpose of the current undertaking was to study the electrophysiological properties of the sleep onset period (SOP) in order to gain understanding into the persistent sleep difficulties of those who complain of insomnia following mild traumatic brain injury (MTBI). While many believe that symptoms of post concussion syndrome (PCS) following MTBI resolve within 6 to 12 months, there are a number of people who complain of persistent sleep difficulty. Two models were proposed which hypothesize alternate electrophysiological presentations of the insomnia complaints of those sustaining a MTBI: 1) Analyses of standard polysomnography (PSG) sleep parameters were conducted in order to determine if the sleep difficulties of the MTBI population were similar to that of idiopathic insomniacs (i.e. greater proportion ofREM sleep, reduced delta sleep); 2) Power spectral analysis was conducted over the SOP to determine if the sleep onset signature of those with MTBI would be similar to psychophysiological insomniacs (characterized by increased cortical arousal). Finally, exploratory analyses examined whether the sleep difficulties associated with MTBI could be explained by increases in variability of the power spectral data. Data were collected from 9 individuals who had sustained a MTBI 6 months to 5 years earlier and reported sleep difficulties that had arisen within the month subsequent to injury and persisted to the present. The control group consisted of 9 individuals who had experienced neither sleep difficulties, nor MTBI. Previous to spending 3 consecutive uninterrupted nights in the sleep lab, subjects completed questionnaires regarding sleep difficulties, adaptive functioning, and personality.

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The present thesis study is a systematic investigation of information processing at sleep onset, using auditory event-related potentials (ERPs) as a test of the neurocognitive model of insomnia. Insomnia is an extremely prevalent disorder in society resulting in problems with daytime functioning (e.g., memory, concentration, job performance, mood, job and driving safety). Various models have been put forth in an effort to better understand the etiology and pathophysiology of this disorder. One of the newer models, the neurocognitive model of insomnia, suggests that chronic insomnia occurs through conditioned central nervous system arousal. This arousal is reflected through increased information processing which may interfere with sleep initiation or maintenance. The present thesis employed event-related potentials as a direct method to test information processing during the sleep-onset period. Thirteen poor sleepers with sleep-onset insomnia and 1 2 good sleepers participated in the present study. All poor sleepers met the diagnostic criteria for psychophysiological insomnia and had a complaint of problems with sleep initiation. All good sleepers reported no trouble sleeping and no excessive daytime sleepiness. Good and poor sleepers spent two nights at the Brock University Sleep Research Laboratory. The first night was used to screen for sleep disorders; the second night was used to investigate information processing during the sleep-onset period. Both groups underwent a repeated sleep-onsets task during which an auditory oddball paradigm was delivered. Participants signalled detection of a higher pitch target tone with a button press as they fell asleep. In addition, waking alert ERPs were recorded 1 hour before and after sleep on both Nights 1 and 2.As predicted by the neurocognitive model of insomnia, increased CNS activity was found in the poor sleepers; this was reflected by their smaller amplitude P2 component seen during wake of the sleep-onset period. Unlike the P2 component, the Nl, N350, and P300 did not vary between the groups. The smaller P2 seen in our poor sleepers indicates that they have a deficit in the sleep initiation processes. Specifically, poor sleepers do not disengage their attention from the outside environment to the same extent as good sleepers during the sleep-onset period. The lack of findings for the N350 suggest that this sleep component may be intact in those with insomnia and that it is the waking components (i.e., Nl, P2) that may be leading to the deficit in sleep initiation. Further, it may be that the mechanism responsible for the disruption of sleep initiation in the poor sleepers is most reflected by the P2 component. Future research investigating ERPs in insomnia should focus on the identification of the components most sensitive to sleep disruption. As well, methods should be developed in order to more clearly identify the various types of insomnia populations in research contexts (e.g., psychophysiological vs. sleep-state misperception) and the various individual (personality characteristics, motivation) and environmental factors (arousal-related variables) that influence particular ERP components. Insomnia has serious consequences for health, safety, and daytime functioning, thus research efforts should continue in order to help alleviate this highly prevalent condition.

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The main purpose ofthis study was to examine the effect ofintention on the sleep onset process from an electrophysiological point ofview. To test this, two nap conditions, the Multiple Sleep Latency Test (MSLT) and the Repeated Test of Sustained Wakefulness (RTSW) were used to compare intentional and inadvertent sleep onset. Sixteen female participants (aged 19-25) spent two non-consecutive nights in the sleep lab; however, due to physical and technical difficulties only 8 participants produced compete sets of data for analysis. Each night participants were given six nap opportunities. For three ofthese naps they were instructed to fall asleep (MSLT), for the remaining three naps they were to attempt to remain awake (RTSW). These two types of nap opportunities represented the conditions ofintentional (MSLT) and inadvertent (RTSW) sleep onset. Several other sleepiness, performance, arousal and questionnaire measures were obtained to evaluate and/or control for demand characteristics, subjective effort and mental activity during the nap tests. The nap opportunities were scored using a new 9 stage scoring system developed by Hori et al. (1994). Power spectral analyses (FFT) were also performed on the sleep onset data provided by the two nap conditions. Longer sleep onset latencies (approximately 1.25 minutes) were obseIVed in the RTSW than the MSLT. A higher incidence of structured mental activity was reported in the RTSW and may have been reflected in higher Beta power during the RTSW. The decent into sleep was more ragged in the RTSW as evidenced by an increased number shifts towards higher arousal as measured using the Hori 9 stage sleep scoring method. 1ll The sleep onset process also appears to be altered by the intention to remain awake, at least until the point ofinitial Stage 2 sleep (i.e. the first appearance of spindle activity). When only examining the final 4.3 minutes ofthe sleep onset process (ending with spindle activity), there were significant interactions between the type ofnap and the time until sleep onset for Theta, Alpha and Beta power. That is to say, the pattern of spectral power measurements in these bands differed across time as a function ofthe type ofnap. The effect ofintention however, was quite small (,,2 < .04) when compared to the variance which could be accounted for by the passage oftime (,,2 == .10 to .59). These data indicate that intention alone cannot greatly extend voluntary wakefulness if a person is sleepy. This has serious implications for people who may be required to perform dangerous tasks while sleepy, particularly for people who are in a situation that does not allow them the opportunity to engage in behavioural strategies in order to maintain their arousal.

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Sleep spindles have been found to increase following an intense period of learning on a combination of motor tasks. It is not clear whether these changes are task specific, or a result of learning in general. The current study investigated changes in sleep spindles and spectral power following learning on cognitive procedural (C-PM), simple procedural (S-PM) or declarative (DM) learning tasks. It was hypothesized that S-PM learning would result in increases in Sigma power during Non-REM sleep, whereas C-PM and DM learning would not affect Sigma power. It was also hypothesized that DM learning would increase Theta power during REM sleep, whereas S-PM and C-PM learning would not affect Theta power. Thirty-six participants spent three consecutive nights in the sleep laboratory. Baseline polysomnographic recordings were collected on night 2. Participants were randomly assigned to one of four conditions: C-PM, S-PM, DM or control (C). Memory task training occurred on night 3 followed by polysomnographic recording. Re-testing on respective memory tasks occurred one-week following training. EEG was sampled at 256Hz from 16 sites during sleep. Artifact-free EEG from each sleep stage was submitted to power spectral analysis. The C-PM group made significantly fewer errors, the DM group recalled more, and the S-PM improved on performance from test to re-test. There was a significant night by group interaction for the duration of Stage 2 sleep. Independent t-tests revealed that the S-PM group had significantly more Stage 2 sleep on the test night than the C group. The C-PM and the DM group did not differ from controls in the duration of Stage 2 sleep on test night. There was no significant change in the duration of slow wave sleep (SWS) or REM sleep. Sleep spindle density (spindles/minute) increased significantly from baseline to test night following S-PM learning, but not for C-PM, DM or C groups. This is the first study to have shown that the same pattern of results was found for spindles in SWS. Low Sigma power (12-14Hz) increased significantly during SWS following S-PM learning but not for C-PM, DM or C groups. This effect was maximal at Cz, and the largest increase in Sigma power was at Oz. It was also found that Theta power increased significantly during REM sleep following DM learning, but not for S-PM, C-PM or C groups. This effect was maximal at Cz and the largest change in Theta power was observed at Cz. These findings are consistent with the previous research that simple procedural learning is consolidated during Stage 2 sleep, and provide additional data to suggest that sleep spindles across all non-REM stages and not just Stage 2 sleep may be a mechanism for brain plasticity. This study also provides the first evidence to suggest that Theta activity during REM sleep is involved in memory consolidation.

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The present study has both theoretical and practical aspects. The theoretical intent of the study was to closely examine the relationship between muscle activity (EMG) and EEG state during the process of falling asleep. Sleep stages during sleep onset (SO) have been generally defined with regards to brain wave activity (Recht schaff en & Kales (1968); and more precisely by Hori, Hayashi, & Morikawa (1994)). However, no previous study has attempted to quantify the changes in muscle activity during this same process. The practical aspect of the study examined the reliability ofa commercially developed wrist-worn alerting device (NovAlert™) that utilizes changes in muscle activity/tension in order to alert its user in the event that he/she experiences reduced wakefulness that may result in dangerous consequences. Twelve female participants (aged 18-42) sp-ent three consecutive nights in the sleep lab ("Adaptation", "EMG", and "NOVA" nights). Each night participants were given 5, twenty-minute nap opportunities. On the EMG night, participants were allowed to fall asleep freely. On the NOV A night, participants wore the Nov Alert™ wrist device that administered a Psychomotor Vigilance Test (PVT) when it detected that muscle activity levels had dropped below baseline. Nap sessions were scored using Hori's 9-stage scoring system (Hori et aI, 1994). Power spectral analyses (FFT) were also performed. Effects ofthe PVT administration on EMG and EEG frequencies were also examined. Both chin and wrist EMG activity showed reliable and significant decline during the early stages ofHori staging (stages HO to H3 characterized by decreases in alpha activity). All frequency bands studied went through significant changes as the participants progressed through each ofHori's 9 SO stages. Delta, theta, and sigma activity increased later in the SO continuum while a clear alpha dominance shift was noted as alpha activity shifted from the posterior regions of the brain (during Hori stages HO to H3) to the anterior portions (during Hori stages H7 to H9). Administration of the PVT produced significant increases in EMG activity and was effective in reversing subjective drowsiness experienced during the later stages of sleep onset. Limitations of the alerting effects of the PVTs were evident following 60 to 75 minutes of use in that PVTs delivered afterwards were no longer able to significantly increase EMG levels. The present study provides a clearer picture of the changes in EMG and EEG during the sleep onset period while testing the efficacy of a commercially developed alerting device. EMG decreases were found to begin during Hori stage 0 when EEG was - dominated by alpha wave activity and were maximal as Hori stages 2 to 5 were traversed (coincident with alpha and beta activity). This signifies that EMG decrements and the loss of resting alpha activity are closely related. Since decreased alpha has long been associated with drowsiness and impending sleep, this investigation links drops in muscle tone with sleepiness more directly than in previous investigations. The EMG changes were reliably demonstrated across participants and the NovAlert™ detected the EMG decrements when Hori stage 3 was entered. The alerting vibrations produced by the NovAlert™ occurred early enough in the SO process to be of practical importance as a sleepiness monitoring and alerting device.

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Recent dose-response sleep restriction studies, in which nightly sleep is curtailed to varying degrees (e.g., 3-, 5-, 7-hours), have found cumulative, dose-dependent changes in sleepiness, mood, and reaction time. However, brain activity has typically not been measured, and attentionbased tests employed tend to be simple (e.g., reaction time). One task addressing the behavioural and electrophysiological aspects of a specific attention mechanism is the Attentional Blink (AB), which shows that the report accuracy of a second target (T2) is impaired when it is presented soon after a first target (Tl). The aim of the present study was to examine behavioural and electrophysioiogical responses to the AB task to elucidate how sleep restriction impacts attentional capacity. Thirty-six young-adults spent four consecutive days and nights in a sleep laboratory where sleep, food, and activity were controlled. Nightly sleep began with a baseline sleep (8 hours), followed by two nights of sleep restriction (3,5 or 8 hours of sleep), and a recovery sleep (8 hours). An AB task was administered each day at 11 am. Results from a basic battery oftests (e.g., sleepiness, mood, reaction time) confirmed the effectiveness of the sleep restriction manipulation. In terms of the AB, baseline performance was typical (Le., T2 accuracy impaired when presented soon after Tl); however, no changes in any AB behavioural measures were observed following sleep restriction for the 3- or 5-hour groups. The only statistically significant electrophysiological result was a decrease in P300 amplitude (for Tl) from baseline to the second sleep restriction night for the 3-hour group. Therefore, following a brief, two night sleep restriction paradigm, brain functioning was impaired for the TI of the AB in the absence of behavioural deficit. Study limitations and future directions are discussed.

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The EEG of the sleep onset period of psychophysiological insomniacs, psychiatric insomniacs and controls was compared using power spectral analysis (FFT). Eighteen drug-free subjects were equally divided into three groups according to their responses in the Brock Sleep and Insomnia Questionnaire, the Minnesota Multiphasic Personality Inventory and the Sleep Disorders Questionnaire. Group 1 consisted of psychophysiological insomniacs, group 2 included insomniacs with an indication of psychiatric disturbances, and group 3 was a control group. EEG, EOG and EMG were recorded for two consecutive nights. Power spectral analysis (FFT) of EEG at C4 from the sleep onset period (defined as lights out to the first five minutes of stage 2) was performed on all standard frequency bands, delta: .5-4 Hz; theta: 4-8 Hz; alpha: 8-12 Hz; sigma: 12-15 Hz beta: 15-25 Hz. Psychophysiological insomniacs had less alpha during wakefulness than the other two groups and did not show the dramatic drop in alpha across the sleep onset period, which characterizes normal sleep. They also had less delta, especially during stage 2 on night 2. They also showed less delta in the last quartile of the chronological analysis of the sleep onset period. Psychiatric insomniacs showed lower relative beta power values overall while psychophysiological insomniacs showed higher relative beta power values during wakefulness. This microanalysis 11 confirms that the sleep onset period is generally similar for psychiatric insomniacs and normal sleepers. This may be due to the sample of psychiatric insomniacs being heterogeneous or may reflect a sleep onset system that is essentially intact. Psychophysiological insomniacs have higher cortical arousal during the sleep onset period than do the psychiatric insomniacs and the controls. Clear differences in the sleep onset period of psychophysiological insomniacs exist. The dramatic changes in power values in these two groups are not seen in the psychophysiological insomniacs, which may make the discrimination between wakefulness and sleep more difficult.

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Sexual behavior in the field crickets, Gryllus veletis and G. pennsylvanicus , was studied in outdoor arenas (12 m2) at high and low levels of population density in 1983 and 1984. Crickets were weighed, individually marked, and observed from 2200 until 0800 hrs for at least 9 continuous nights. Calling was measured at 5 min intervals, and movement and matings were recorded hourly. Continuous 24 hr observations were also conducted,·and occurrences of aggressive and courtship songs were noted. The timing of males searching, calling, courting, and fighting for females should coincide with female movement and mating patterns. For most samples female movement and matings occurred at night in the 24 hr observations and were randomly distributed with time for both species in the 10 hr observations. Male movement for G. veletis high density only was enhanced at night in the 24 hr observations, however, males called more at night in both species at high and low densities. Male movement was randomly distributed with time in the 10 hr observations, and calling increased at dawn for the G. pennsylvanicus 1984 high density sample, but was randomly distributed in other samples. Most courtship and aggression songs in the 24 hr observations were too infrequent for statistical testing and generally did not coincide with matings. Assuming residual reproductive value, and costs attached to a male trait in terms of future reproductive success decline with age, males should behave in more costly ways with age; by calling and moving more with age. Consequently, mating rates should increase with age. Female behavior may not change with age. G. veletis , females moved more with age at both low density samples, however, crickets moved less with age at high density. G. pennsylvanicus females moved more with age in the 1984 low density sample, whereas crickets moved less with age in the 1983 high density sample. For both species males in the 1984 high density samples called less with age. For G. pennsylvanicus in 1983 calling and mating rates increased with age. Mating rates decreased with age for G. veletis males in the high density sample. Aging may not affect cricket behavior. As population density increases fewer calling sites become available, costs of territoriality increase, and matings resulting from non-calling behavior should increase. For both species the amount of calling and in G. veletis the distance travelled per night was not different between densities. G. pennsylvanicus males and females moved more at low density. At the same deneity levels there were no differences in calling, mating, and, movement rates in G. veletis , however, G. pennsylvanicus males moved more at high density in 1983 than 1984. There was a positive relationship between calling and mating for the G. pennsylvanicus low density sample only, and selection was acting directly to increase calling. For both species no relationships between movement and mating success was found, however, the selection gradient on movement in the G. veletis high density population was significant. The intensity of selection was not significant and was probably due to the inverse relationship between displacement and weight. Larger males should call more, mate more, and move less than smaller males. There were no correlations between calling and individual weight, and an inverse correlation between movement and size in the G. veletis high density population only. In G. pennsylvanicus , there was a positive correlation between individual weight and mating, but, some correlate of weight was under counter selection pressure and-prevented significance of the intensity of selection. In contrast, there was an inverse correlation in the G.·veletis low density B sample. Both measures of selection intensities were significant and showed that weight only was under selection pressures. An inverse correlation between calling and movement was found for G. veletis at low density only. Because males are territorial, females are predicted to move more than males, however, if movement is a mode of male-male reproductive competition then males may move more than females. G. pennsylvanicus males moved more than females in all samples, however, G. veletis males and females moved similar distances at all densities. The variation in relative mating success explained by calling scores, movement, and weight for both species and all samples were not significant In addition, for both species and all samples the intensity of selection never equalled the opportunity for selection.

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Several recent studies have described the period of impaired alertness and performance known as sleep inertia that occurs upon awakening from a full night of sleep. They report that sleep inertia dissipates in a saturating exponential manner, the exact time course being task dependent, but generally persisting for one to two hours. A number of factors, including sleep architecture, sleep depth and circadian variables are also thought to affect the duration and intensity. The present study sought to replicate their findings for subjective alertness and reaction time and also to examine electrophysiological changes through the use of event-related potentials (ERPs). Secondly, several sleep parameters were examined for potential effects on the initial intensity of sleep inertia. Ten participants spent two consecutive nights and subsequent mornings in the sleep lab. Sleep architecture was recorded for a fiiU nocturnal episode of sleep based on participants' habitual sleep patterns. Subjective alertness and performance was measured for a 90-minute period after awakening. Alertness was measured every five minutes using the Stanford Sleepiness Scale (SSS) and a visual analogue scale (VAS) of sleepiness. An auditory tone also served as the target stimulus for an oddball task designed to examine the NlOO and P300 components ofthe ERP waveform. The five-minute oddball task was presented at 15-minute intervals over the initial 90-minutes after awakening to obtain six measures of average RT and amplitude and latency for NlOO and P300. Standard polysomnographic recording were used to obtain digital EEG and describe the night of sleep. Power spectral analyses (FFT) were used to calculate slow wave activity (SWA) as a measure of sleep depth for the whole night, 90-minutes before awakening and five minutes before awakening.