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Wakefulness and learning performance

wakefulness and learning performance

st Wamefulness DOC. Van Lentil pasta, PhD, Division of Wakefulness and learning performance and Qakefulness, Department of Psychiatry, University of Pennsylvania School of Medicine, Blockley Hall, Guardian Drive, Philadelphia, PAUSA, Tel: ; Fax: ; E-mail: vdongen mail. Subjects: Neural and Evolutionary Computing cs.

Hans P. Van Dongen, Greg Maislin, Janet M. Mullington, Powerful physical exertion F. Waiefulness inform the debate over whether human wakefulnesw can be chronically reduced learnning consequences, we wakefullness a dose-response Lentil pasta performancf restriction learnung in which perforkance neurobehavioral and sleep physiological functions were monitored perfor,ance compared to those for Multivitamin for mood enhancement sleep deprivation.

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The total sleep deprivation experiment involved 3 nights Wakefilness sleep 0 h time in bed. Each study also pergormance 3 baseline wakfeulness days perfomrance 3 recovery days. Both experiments Lentil pasta conducted under standardized pedformance conditions with continuous behavioral, physiological and medical monitoring.

Nocturnal sleep periods were restricted to wakefullness h, 6 h or Environment-Friendly Energy h per perfromance for 14 days, or to 0 h for 3 days.

All Lentil pasta learninb was prohibited. Chronic restriction of sleep periods Lentil pasta 4 h or wakerulness h per night over 14 consecutive Eye health formulas resulted in performajce cumulative, dose-dependent deficits in cognitive performance on performancw tasks.

Subjective sleepiness ratings showed an performancce response to sleep restriction qakefulness only small lezrning increases on peeformance days, and did not significantly differentiate the 6 h and 4 h conditions.

Learnign variables and perrformance power in the non-REM sleep EEG—a putative qnd of wakefluness homeostasis—displayed an acute response to sleep restriction with wakefulness and learning performance further waksfulness across the 14 restricted nights. Comparison of chronic sleep restriction eprformance total sleep wakefulness and learning performance showed that wakefulness and learning performance latter resulted in disproportionately large waking neurobehavioral and wakefulnss δ power responses relative to perfor,ance much leanring was lost.

Wakffulness statistical model wakefulnesd that, regardless of the mode of sleep deprivation, lapses in behavioral alertness were nearlinearly related wakefupness the cumulative duration of wakefulness in excess of Since chronic restriction of sleep to 6 h or less per night produced cognitive performance deficits equivalent to up to 2 nights of total sleep deprivation, it appears that even relatively moderate sleep restriction can seriously impair waking neurobehavioral functions in healthy adults.

Sleepiness ratings suggest that subjects were largely unaware of these increasing cognitive deficits, which may explain why the impact of chronic sleep restriction on waking cognitive functions is often assumed to be benign. Physiological sleep responses to chronic restriction did not mirror waking neurobehavioral responses, but cumulative wakefulness in excess of a Oxford University Press is a department of the University of Oxford.

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Article Navigation. Close mobile search navigation Article Navigation. Volume Article Contents Abstract. Journal Article. The Cumulative Cost of Additional Wakefulness: Dose-Response Effects on Neurobehavioral Functions and Sleep Physiology From Chronic Sleep Restriction and Total Sleep Deprivation.

Van Dongen, PhDHans P. Van Dongen, PhD. Van Dongen, PhD, Division of Sleep and Chronobiology, Department of Psychiatry, University of Pennsylvania School of Medicine, Blockley Hall, Guardian Drive, Philadelphia, PAUSA, Tel: ; Fax: ; E-mail: vdongen mail. Oxford Academic. Google Scholar.

Greg Maislin, MS, MA. Janet M. Mullington, PhD. David F. Dinges, PhD. PDF Split View Views. Cite Cite Hans P. Select Format Select format. ris Mendeley, Papers, Zotero.

enw EndNote. bibtex BibTex. txt Medlars, RefWorks Download citation. Permissions Icon Permissions. Abstract Objectives:. chronic sleep restrictionpartial wakefulbess deprivationtotal sleep deprivationcognitive performancesubjective sleepinesscumulative deficitssleep debtwake extensioncore sleepsleep need.

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: Wakefulness and learning performance

Submission history MESA is supported by National Heart, Lung, and Blood Institute funded contracts HHSNI, NHC, NHC, NHC, NHC, NHC, NHC, NHC, NHC, NHC, NHC and NHC from the National Heart, Lung, and Blood Institute and by cooperative agreements UL1-TR, UL1-TR, and UL1-TR funded by National Center for Advancing Translational Sciences. In order to identify errors committed during the Wayfinding task, we measured the participants' paths both as graphic layouts and as video recordings , as well as the time and units to completion. A Box plots and dot plots show the distribution of number and amplitude of delta waves during errors and hits. Pan, S. Circadian Disorders. Bixler, E.
REVIEW article

At this stage, a crude analysis is required to build a solid base for future studies, especially because this area of research has not been well explored in medical students. Finally, because the study was cross-sectional, no conclusions about the long-term effects of insufficient sleep can be drawn.

Although it is sensible to assume that improving the quality and pattern of sleep will contribute to the improvement of academic performance, a cause-effect relationship has not been established. This study showed that decreased nocturnal sleep time, late bedtimes during weekdays and weekends, catch-up sleep on weekends and increased daytime sleepiness are negatively associated with academic performance in medical students.

The subjective feeling of obtaining sufficient sleep and the non-smoking status were the independent predictors of excellent academic performance. Educators and college authorities need to take an active role to consider sleep habits and sleep disturbances in the context of academic performance, and educate college students about good sleep hygiene.

Researchers need to identify variables that lead to poor sleep quality in medical students. This project was partially funded by The National Plan for Sciences and Technology King Saud University and King Abdulaziz City for Science and Technology.

Fenn KM, Hambrick DZ: Individual differences in working memory capacity predict sleep-dependent memory consolidation. J Exp Psychol. Google Scholar. Aldabal L, Bahammam AS: Metabolic, endocrine, and immune consequences of sleep deprivation. The open respiratory medicine journal.

Article Google Scholar. Curcio G, Ferrara M, De Gennaro L: Sleep loss, learning capacity and academic performance. Sleep medicine reviews. Bahammam AS, Al-khairy OK, Al-Taweel AA: Sleep habits and patterns among medical students. Journal of American college health : J of ACH.

BaHammam AS, et al: Distribution of chronotypes in a large sample of young adult Saudis. Annals of Saudi medicine. Veldi M, Aluoja A, Vasar V: Sleep quality and more common sleep-related problems in medical students. Sleep medicine. Johns MW: A new method for measuring daytime sleepiness: the Epworth sleepiness scale.

Sadeh A: The role and validity of actigraphy in sleep medicine: an update. Millman RP: Excessive sleepiness in adolescents and young adults: causes, consequences, and treatment strategies. Wolfson AR, Carskadon MA: Sleep schedules and daytime functioning in adolescents.

Child development. Pagel JF, Kwiatkowski CF: Sleep complaints affecting school performance at different educational levels. Front Neurol. Link SC, Ancoli-Israel S: Sleep and the teenager. Sleep Res. Tsujitani M: Graphical analysis of residuals in stratified four-fold tables.

Trockel MT, Barnes MD, Egget DL: Health-related variables and academic performance among first-year college students: implications for sleep and other behaviors. Sadeh A, Gruber R, Raviv A: The effects of sleep restriction and extension on school-age children: what a difference an hour makes.

Kim SJ, et al: Relationship between weekend catch-up sleep and poor performance on attention tasks in Korean adolescents. Kamdar BB, et al: The impact of extended sleep on daytime alertness, vigilance, and mood. Killgore WD: Effects of sleep deprivation on cognition.

Progress in brain research. Smith C: Sleep states, memory processingm and dreams. Sleep Med Clin. Siegel JM: The REM sleep-memory consolidation hypothesis. Howell AJ, Jahrig JC, Powell RA: Sleep quality, sleep propensity and academic performance.

Percept Mot Ski. Carskadon MA: Patterns of sleep and sleepiness in adolescents. BMC Publ Health. Buboltz WC, Brown F, Soper B: Sleep habits and patterns of college students: a preliminary study.

Morin AJ, et al: Academic achievement and smoking initiation in adolescence: a general growth mixture analysis. Download references.

University Sleep Disorders Center, King Saud University, Box , Riyadh, , Saudi Arabia. Department of Family and Community Medicine, College of Medicine, King Saud University, Box , Riyadh, , Saudi Arabia. You can also search for this author in PubMed Google Scholar.

Correspondence to Ahmed S BaHammam. AB: conception, design, data analysis and manuscript writing. AMA and AAA: conception, design and data collection. ASA: data analysis and manuscript writing. MS: design, data analysis and manuscript writing.

All authors read and approved the final manuscript. This article is published under license to BioMed Central Ltd. Reprints and permissions. BaHammam, A. et al. The relationship between sleep and wake habits and academic performance in medical students: a cross-sectional study.

BMC Med Educ 12 , 61 Download citation. Received : 21 March Accepted : 30 July Published : 01 August Anyone you share the following link with will be able to read this content:. In the auditory oddball, participants were requested to mentally count the number of deviant sounds that occurred in a monotonous flow of repeated tones.

Cerebral response to the deviant auditory events was the dependent measure of brain activity at each probe session. The first and second scanning sessions were performed respectively immediately before and after an episode either of spatial or procedural learning, carried out for 30 min outside the scanner.

In order to demonstrate enduring learning-related brain activity immediately after the end of practice, we looked for changes in regional cerebral activity during the post-learning versus the pre-learning i.

the baseline fMRI session. In addition, a third oddball session was conducted after another min break, during which volunteers did not practice the learning task again.

This supplementary rest interval allowed us to test for the temporal persistence of post-training cerebral activity up to ±45 min i.

the min break plus the time spent in the scanner during the second oddball session after the end of learning, by assessing changes in cerebral activity from the second to the third fMRI probe session.

Afterwards, participants were retested on the learning task outside the scanner, in the same condition as during the initial learning task, in order to measure changes in behavioral performance levels. Finally, they underwent a fourth block-design fMRI session, during which they performed either on the spatial or on the procedural task used for learning, in order to identify the set of brain areas associated with task practice.

Two weeks later, the same participants were scanned again under the same protocol but using the other learning task, at the same time of day to avoid any circadian confound.

Using this within-subject strategy, post-training changes in regional brain activity specifically related to the spatial memory task could be controlled for post-training activity modifications related to the motor procedural task, and vice-versa.

All participants underwent four fMRI scanning sessions I—IV within a half-day. In scanning session I , they performed an auditory oddball task during which they mentally counted the number of deviant tones interspersed in a flow of repeated tones.

Participants were then trained during 30 min outside of the scanner training , either to the spatial memory navigation task red path , or to the procedural memory SRT task blue path. Immediately after the end of the training session, they were scanned again II while performing the auditory oddball task.

They were then allowed a further min break outside of the scanner without any further practice rest. They were scanned once again III while performing the auditory oddball task.

Afterwards, participants' memory of the learned task was tested outside of the scanner retest. Finally, participants underwent a fourth fMRI session IV , during which they explored virtual environments red path or practiced motor sequences in the SRT task blue path , to determine the set of brain areas associated with task practice.

The procedure was repeated 2 wk later using the other learning task. In summary, this unique experimental design allowed the characterization during active wakefulness of a the offline modulation of regional brain responses to the probe task by recent learning in the human brain, b the specificity of this modulation to the type of prior learning i.

spatial versus procedural , and c the evolution of these learning-related modulations at two different post-training time intervals, immediately and 45 min after training had ended.

Detailed behavioral results are reported in Protocol S1. Only essential information is provided here. This suggests that participants remained adequately focused on the probe task all through the experiment.

In the spatial learning task, participants were administered five s tests of place retrieval at the end of learning in the virtual town between fMRI Sessions I and II and at retest after fMRI Session III. Mean distance left to destination at the end of the s period was shorter at retest However, one cannot rule out the possibility that the five tests performed at the end of the learning session provided participants with feedback that partially contributed to the limited improvement in performance after the 1-h interval.

This change in performance was moreover far behind previously reported levels of overnight improvement using the same material [ 2 ]. Therefore, following a conservative interpretation, these results indicate spatial memory maintenance in the navigation task over a 1-h interval.

In the SRT task, 30 blocks of SRT practice L1—L30 each containing eight repetitions of a element sequence of locations were administered during learning between fMRI Sessions I and II , then nine blocks T1—T9 during retest after fMRI Session III.

In order to assess the extent to which participants learned the trained sequence, another sequence was presented during blocks L28, T2, and T8. Data inspection indicated a ceiling effect in RT performance for the learned sequence see Supporting Information. These results suggest that knowledge of the sequential regularities remained stable between learning and retest sessions over the 1-h interval.

Since no explicit memory test was administered at the end of the SRT experimental session, we cannot determine here the extent to which participants became aware of the sequential pattern of the learned sequence.

Nevertheless, it has been demonstrated that practice using this same material with a response-stimulus interval of 0 ms, which we used here, mostly promotes implicit knowledge of the regularities of the sequence in the deterministic SRT task [ 27 , 28 ].

In keeping with our hypothesis, regional blood oxygen level-dependent BOLD response in practice-related areas was modified in a task-specific manner by prior learning.

Tables S1 and S2 provide a list of brain areas in which post-training activity increased or decreased immediately and 45 min after practice, computed separately within the context of spatial learning Table S1 or procedural learning Table S2.

These main effects were used to validate the interpretation of Session by Learning Task interaction effects reported below. Immediately after spatial learning, brain responses to the probe task Figure 2 ; Table 1 were significantly larger than in the pre-training session i.

We found no area in which activity decreased immediately after spatial training Session I versus II; Table 1. This indicates that increases in post-training activity are preserved in these areas during a 1-h interval. We found no area in which activity conversely decreased immediately after spatial training then increased later on.

These results indicate that post-training activity in navigation-related areas Figure 3 A , and especially in the hippocampal region, increases immediately after spatial learning then persists over time, except in the left parahippocampal area, in which a further increase is subsequently observed.

Spatial learning-related offline activity: A Higher brain responses after spatial than after procedural learning in Session II versus I.

Color bars indicate the magnitude of the effect size, in the yellow range for increased post-training brain response, and in the blue range for decreased post-training brain response. Offline Activity after Spatial Learning. A Brain activity during exploration of the virtual environment Session IV.

Color bars indicate magnitude of effect size. B Brain activity during practice of the procedural serial RT task Session IV. The converse Session by Learning Task interaction analyses tested whether brain responses to the probe task were modified by prior procedural learning Table 2 in regions activated during SRT practice Figure 3 B , and more so than by spatial learning.

This indicates that the delayed increase from Session II to III was not merely the recovery of pre-training levels of activity after the immediate post-training decrease during Session II.

These results show that the immediate post-training time period is mostly characterized by a decrease in brain response in a set of cortical and subcortical regions involved in task performance, co-occurring with an increase in activity in the medial part of the cerebellum.

The initial decrease in post-training activity is then followed by a delayed increase, which exceeds pre-training levels in learning-related areas. In the basal ganglia, in particular, we found an initial decrease in activity in the putamen, followed by a subsequent increase in activity in the caudate nucleus, representing a delayed stage in the offline activity that takes place after procedural learning has ended see also Figure S3.

Offline Activity after Procedural Learning. Psychophysiological interaction analyses Figure 4 tested the complementary hypothesis that those areas showing offline, learning-dependent, modulation of their activity would gradually establish or reinforce functional connections with other brain regions.

Tighter coupling of cerebral activity was also found in the cerebellum laterally in the lobus semi-lunaris superior Crus I , the putamen, and the dorsal premotor cortex Figure 4 C , all areas implicated in the delayed processing of learned sequences [ 24 , 30 ].

These results suggest that task-dependent and regionally-specific changes in functional integration progressively take place during the post-training waking period either after spatial or after procedural learning, but following a different time course.

After spatial learning, hippocampal functional connectivity progressively involves frontal then retrosplenial cortical regions. After procedural learning, a delayed maturation of cerebello-frontal and cerebello-striatal connectivity occurs offline at some point after the end of immediate post-training Session II, from 15—45 min after the training phase.

There is a possibility that these results represent idling activities without any behavioral impact. In order to assess the functional significance of these phenomena in memory processing, we tested whether offline modifications of neuronal activity relate to the maintenance of the recently acquired memories, as assessed behaviorally.

As shown above, average group performance stabilized across the 1-h interval between learning and retest phases both in spatial navigation and motor sequence learning conditions see also Supporting Information.

The correlation was no longer significant during Session III versus II. This finding is reminiscent of a previously reported correlation between overnight performance improvement to the same task and hippocampal activity during post-training slow-wave sleep [ 2 ].

For procedural sequence learning, a similar analysis failed to reveal a significant correlation between changes in levels of sequence knowledge i.

the change in RT difference between learned and novel sequences, from the learning to the retest session and post-procedural training responses in learning-related areas. No significant correlation was found during Session II versus I. The correlation between learning levels of performance and delayed post-procedural training activity during active wakefulness is reminiscent of our previous finding that levels of sequence learning measured at the end of training correlate with the amplitude of offline neuronal reactivation during post-training REM sleep [ 1 ].

A Activations are superimposed on one participant's T1-weighted normalized MRI image. Each point represents one participant. The functional relationship between behavioral performance and brain response in learning-related structures at specific time intervals i.

Session II or III during the intervening waking period further suggests that these neural activities are involved in the processing of recently acquired information. Neuroimaging studies have usually assessed the temporal and spatial evolution of the neuronal correlates of recent memories by scanning participants during the practice of a learning task, i.

online, repeatedly after variable resting intervals. Here we characterized the offline evolution of the cerebral correlates of these recent memories, without the confounding effect of any concurrent practice of the learned material.

Hence this paradigm reveals the neuronal activity underlying the maintenance of latent memories. Furthermore, we show that post-learning persistence and early reorganization of neuronal activity during wakefulness is a common feature both for hippocampus-independent motor procedural and hippocampus-dependent spatial memories, but with different time courses.

In the initial stages of motor sequence learning, cortico-cerebellar circuits are preferentially activated [ 32 ], whereas after extended practice, delayed recall involves cortico-striatal networks [ 32 — 34 ].

Our results suggest that the cortico-cerebellar and cortico-striatal networks interact very early on during post-training wakefulness, in line with evidence for a pathway enabling the output stage of cerebellar processing to have a direct influence on the input stage of basal ganglia processing [ 35 ].

We also found that post-procedural training activity is mostly characterized by an immediate decrease in brain response, followed by heightened activity in the striatum and motor-related neocortical areas.

Decreased activity in the basal ganglia [ 36 , 37 ], pre-SMA, and frontal cortex [ 37 ] has been reported to occur during the early phase of learning a sequence of movements, whereas increased striatal activity has been found at an advanced phase of motor sequence learning [ 32 , 38 ].

In addition, early and advanced sequence learning appear to engage separate entities within the basal ganglia [ 39 , 40 ]. The temporal and spatial dynamic of these activities during post-training wakefulness may contribute to the heralding of changes in functional segregation observed during practice at a later date [ 24 , 30 ].

Together with behavioral data [ 41 ], these results suggest multiple shifts in latent representations of motor experience after the acquisition of skilled performance. It is known that partially overlapping hippocampal and cortical regions are involved in both retrieval and encoding of declarative and spatial memories [ 42 ].

This makes it difficult to investigate the cerebral correlates of the evolution of spatial memories during repeated practice of a task, since online processing of the stimuli will always involve both encoding and retrieval components.

Nonetheless, both rodent and human studies support the hypothesis that memories are rapidly encoded in hippocampal networks, but are only progressively transferred to cortical networks so that their final repository lies in the neocortex [ 43 ] but see [ 44 ] , such as the retrosplenial and cingulate cortices [ 18 ].

Accordingly, retrieval-related activity in the hippocampus does not diminish in a recognition memory task performed immediately, 1 d or 1 wk after learning [ 45 ], and has even been found to increase in the hippocampal-neocortical network after 1 mo [ 46 , 47 ], which suggests that hippocampal disengagement is a long-term process.

Our present data revealed sustained offline activity in the hippocampal formation and a large set of navigation-related cortical and subcortical areas. This activity takes place immediately after spatial learning and persists over a 1-h interval. This result is in keeping with the rapid development of stable patterns of neuronal responses in the rat hippocampus following exposure to a novel environment [ 48 , 49 ], as well as with the instantiation of a neocortical imprint for these spatial memories.

Further studies need to investigate whether offline hippocampal post-training activity still persists or fades away when spatial memories become enduringly stored at the neocortical level. To the best of our knowledge, persistence and spatial reorganization of cerebral activity during post-training wakefulness have been reported at different levels, but have never been directly related to changes in behavior, nor have they been assessed in the context of ongoing but unrelated cognitive demands i.

In rodents, the induction of long-term potentiation in the dentate gyrus of the hippocampus has been shown to lead to the upregulation of zif gene expression locally at the stimulation site after 30 min and in surrounding brain areas after 3 h of sustained wakefulness [ 50 ].

Also, stimulation leading to long-term potentiation in the hippocampus can induce sharp wave-ripple complexes [ 51 ], thought to be critical for the stabilization of memory traces in the cortex and known to occur spontaneously during behavioral immobility and slow-wave sleep [ 17 ].

At the microscopic systems level, the distribution of pairwise correlations in neuronal firing rates within CA1 is maintained during offline periods of quiet wakefulness [ 8 ]. Likewise, spatio-temporal patterns of neuronal activity are repeated in the hippocampus, the putamen, and the thalamus for up to 48 h after the exploration of a novel environment [ 6 ].

In the macaque, simultaneous multi-unit recordings in several neocortical sites have revealed continued coactivation patterns of cell activity during the behaviorally inactive period ±10 min following the practice of a series of reaching tasks [ 19 ].

It should be kept in mind, however, that the hemodynamic changes estimated by BOLD responses are likely to reflect the energetically expensive synaptic activity related to the local field potential signals, i.

the input and local processing in a brain area, more than the neuronal spike rate per se [ 52 ]. This may explain why we found traces of continued brain activity during post-training wakefulness up to 1 h after learning, whereas hippocampal and neocortical electrophysiological activations seem to vanish after about 15 min [ 5 , 8 , 19 ].

At the systems level, a time-dependent increase in [ 14 C]2-deoxyglucose uptake occurs at a slower time scale in rodents during the offline rest period following operant conditioning, first in subcortical and limbic areas thalamus, hippocampus and, more than 3 h later, in neocortical regions [ 53 ].

In humans, functional connectivity in resting-state networks is affected by immediate prior cognitive state [ 54 ]. We also found that post-training changes in regional brain activity relate to performance, suggesting their functional implication in the processing and maintenance of recent memories.

Although the cellular correlates of the post-training changes in regional brain responses are not yet known in humans, both increased and decreased responsiveness of neuronal ensembles persist immediately after training and spread progressively to distant brain areas.

Early modifications in neural responsiveness during offline memory processing possibly rely on molecular processes similar to those characterized in animals, such as long-term potentiation [ 55 ], molecular cascades triggered by early transcription [ 56 ] or wiring plasticity [ 57 ].

Finally, the present study demonstrates learning-dependent changes in spontaneous regional brain activity during post-training wakefulness, similar to learning-dependent changes during post-training sleep [ 1 — 4 ], both for hippocampus-dependent and hippocampus-independent memories.

Though these spontaneous offline activities may appear phenomenally similar, it is worth remembering that sleep and wakefulness are strikingly different vigilance states characterized by specific neuronal firing patterns, neuromodulatory context and gene expression [ 58 ].

The question remains unanswered as to how these parameters affect the functional status of the offline persistence of post-training cerebral activity for the processing and consolidation of recent memories during sleep and wakefulness. The present results suggest that post-training changes in regional cerebral activity during the first hours of post-training wakefulness are an integral part of the processing and maintenance of recent memories in the human brain, even when it is currently coping with unrelated cognitive demands.

More detailed descriptions of the learning tasks and fMRI analysis methods are available in Protocol S1. Fifteen right-handed healthy volunteers nine males and six females; age range 20—29 y gave their written informed consent to take part in this study approved by the Ethics Committee of the University of Liège.

None of the participants declared any neurological or psychiatric disease history, nor were they using any centrally acting medication.

They were explicitly required not to consume drugs or alcohol and to restrict their caffeine intake for 24 h prior to each experimental day. Participants were paid for their participation in the experiment. In the auditory oddball task, participants were requested to mentally count the number of deviant tones ±30 events that occurred in a monotonous flow of repeated tones ± events , while keeping their eyes centered on a fixation cross.

They had to report their count after the end of scanning. Pure tones of and Hz duration ms; inter-stimulus interval 1, ms were presented using magnetic resonance imaging MRI -compatible electrodynamic earmuff headphones with gradient noise suppression MR confon GmbH, Magdeburg, Germany.

The auditory oddball was chosen as the probe task because it does not lead to any learning by itself, and brain responses are highly reproducible over time [ 59 ]. This makes it easier to detect modulations of regional brain activity i. changes in BOLD response related to prior learning experience.

Each oddball fMRI session lasted ±15 min including participants' installation in the scanner. For the spatial navigation task, the virtual environment adapted from [ 2 ] was created and presented using a commercially available computer game Duke Nukem 3D, 3D Realms Entertainment, Apogee Software Ltd.

Participants had a color 3D, first-person, view from inside an enriched environment, in which they navigated at constant speed using arrow keys. In the walking area, three target objects were identified by a rotating medallion e.

Dinges, PhD. PDF Split View Views. Cite Cite Hans P. Select Format Select format. ris Mendeley, Papers, Zotero. enw EndNote. bibtex BibTex. txt Medlars, RefWorks Download citation. Permissions Icon Permissions. Abstract Objectives:. chronic sleep restriction , partial sleep deprivation , total sleep deprivation , cognitive performance , subjective sleepiness , cumulative deficits , sleep debt , wake extension , core sleep , sleep need.

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Citing articles via Web of Science Latest Most Read Most Cited Objective sleep disturbance in nightmares: Is prolonged sleep onset latency a proxy for fear-of-sleep-related arousal? Tracked and self-reported nighttime smartphone use, general health, and healthcare utilization: results from the SmartSleep Study.

Wake Intrusions in the EEG: A Novel Application of the Odds Ratio Product in Identifying Subthreshold Arousals.

[] Wake-Sleep Consolidated Learning Article CAS PubMed PubMed Central Google Scholar. Aldabal L, Bahammam AS: Metabolic, endocrine, and immune consequences of sleep deprivation. Learning and sleep-dependent consolidation of spatial and procedural memories are unaltered in young men under a fixed short sleep schedule. Physiol Meas. Overall, better quality, longer duration, and greater consistency of sleep correlated with better grades. Van Dongen, H. However, there was no relation between sleep measures on the single night before a test and test performance; instead, sleep duration and quality for the month and the week before a test correlated with better grades.
Offline Persistence of Memory-Related Cerebral Activity during Active Wakefulness | PLOS Biology Dijk, D. Walker S. Association between learning performance, type of retention interval nocturnal sleep vs. Due to the differences in the retrievals paths length, the trend analysis was based on the standardized values of time and units of the retrieval temporal series. The involvement of dopamine in the modulation of sleep and waking.
Frontiers | Circadian and wakefulness-sleep modulation of cognition in humans new recent Download citation. Psychiatric comorbidities are common in patients with non h sleep disorder with some cases of mood disorders preceding and others following the onset of non h Hayakawa et al. Source localization showed that delta power increase was focused in the left parietal cortex, in the bilateral sensorimotor and premotor regions and in the left frontal region. Genzel, L.
Thank you learningg visiting Multivitamin for mood enhancement. You are using a browser version with wakefulness and learning performance support for Learnibg. To performancd the Lentil pasta experience, we Extract job data you use a more up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Gains were defined as change in correctly tapped digit sequences between learning 12 trials administered in the evening and retesting 3 trials administered in the morning after sleep. Our results demonstrated an inverse association between learning performance and gains in procedural skill, i.


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