The Effects of Two Types of Sleep Deprivation on Visual Working Memory Capacity and Filtering Efficiency

Sleep deprivation has adverse consequences for a variety of cognitive functions. The exact effects of sleep deprivation, though, are dependent upon the cognitive process examined. Within working memory, for example, some component processes are more vulnerable to sleep deprivation than others. Additionally, the differential impacts on cognition of different types of sleep deprivation have not been well studied. The aim of this study was to examine the effects of one night of total sleep deprivation and 4 nights of partial sleep deprivation (4 hours in bed/night) on two components of visual working memory: capacity and filtering efficiency. Forty-four healthy young adults were randomly assigned to one of the two sleep deprivation conditions. All participants were studied: 1) in a well-rested condition (following 6 nights of 9 hours in bed/night); and 2) following sleep deprivation, in a counter-balanced order. Visual working memory testing consisted of two related tasks. The first measured visual working memory capacity and the second measured the ability to ignore distractor stimuli in a visual scene (filtering efficiency). Results showed neither type of sleep deprivation reduced visual working memory capacity. Partial sleep deprivation also generally did not change filtering efficiency. Total sleep deprivation, on the other hand, did impair performance in the filtering task. These results suggest components of visual working memory are differentially vulnerable to the effects of sleep deprivation, and different types of sleep deprivation impact visual working memory to different degrees. Such findings have implications for operational settings where individuals may need to perform with inadequate sleep and whose jobs involve receiving an array of visual information and discriminating the relevant from the irrelevant prior to making decisions or taking actions (e.g., baggage screeners, air traffic controllers, military personnel, health care providers)

Polysomnographic and Subjective Sleep Predictors of Alcoholic Relapse

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65219/1/j.1530-0277.1998.tb03995.x.pd

The Relationship between Regular Sports Participation and Vigilance in Male and Female Adolescents

The present study investigated the relationship between regular sport participation (soccer) and vigilance performance. Two groups of male and female adolescents differentiated in terms of their sport participation (athletes, n = 39, and non-athletes, n = 36) took part in the study. In one session, participants performed the Leger Multi-stage fitness test to estimate their aerobic fitness level. In the other session, participants completed the Psychomotor Vigilance Task (PVT) to evaluate their vigilance performance. Perceived arousal prior to the task and motivation toward the task were also measured in the PVT session. The results revealed that athletes had better cardiovascular fitness and showed better performance in the PVT. However, correlation analyses did not show any significant relationship between cardiovascular fitness and performance in the PVT. Athletes showed larger scores in motivation and perceived arousal measures with respect to non-athletes, although, once again, these variables were not correlated with PVT performance. Gender differences were observed only in the Leger test, with males showing greater fitness level than females. The major outcome of this research points to a positive relationship between regular sport participation and vigilance during adolescence. This relationship did not seem to be influenced by gender, perceived arousal, motivation toward the task or cardiovascular fitness. We discuss our results in terms of the different hypotheses put forward in the literature to explain the relationship between physical activity and cognitive functioning.This research was supported by a Spanish Ministerio de Educación y Cultura (https://sede. educacion.gob.es) predoctoral grant (FPU13-05605) to the first author, and project research grants: Junta de Andalucia Proyecto de Excelencia SEJ-6414 (http://www.juntadeandalucia.es) and Ministerio de Economía y Competitividad PSI2013-46385 (http://www.mineco.gob.es) to DS and FH

Resisting Sleep Pressure:Impact on Resting State Functional Network Connectivity

In today's 24/7 society, sleep restriction is a common phenomenon which leads to increased levels of sleep pressure in daily life. However, the magnitude and extent of impairment of brain functioning due to increased sleep pressure is still not completely understood. Resting state network (RSN) analyses have become increasingly popular because they allow us to investigate brain activity patterns in the absence of a specific task and to identify changes under different levels of vigilance (e.g. due to increased sleep pressure). RSNs are commonly derived from BOLD fMRI signals but studies progressively also employ cerebral blood flow (CBF) signals. To investigate the impact of sleep pressure on RSNs, we examined RSNs of participants under high (19 h awake) and normal (10 h awake) sleep pressure with three imaging modalities (arterial spin labeling, BOLD, pseudo BOLD) while providing confirmation of vigilance states in most conditions. We demonstrated that CBF and pseudo BOLD signals (measured with arterial spin labeling) are suited to derive independent component analysis based RSNs. The spatial map differences of these RSNs were rather small, suggesting a strong biological substrate underlying these networks. Interestingly, increased sleep pressure, namely longer time awake, specifically changed the functional network connectivity (FNC) between RSNs. In summary, all FNCs of the default mode network with any other network or component showed increasing effects as a function of increased 'time awake'. All other FNCs became more anti-correlated with increased 'time awake'. The sensorimotor networks were the only ones who showed a within network change of FNC, namely decreased connectivity as function of 'time awake'. These specific changes of FNC could reflect both compensatory mechanisms aiming to fight sleep as well as a first reduction of consciousness while becoming drowsy. We think that the specific changes observed in functional network connectivity could imply an impairment of information transfer between the affected RSNs

The impact of structured sleep schedules prior to an in-laboratory study: Individual differences in sleep and circadian timing

INTRODUCTION: Many sleep and circadian studies require participants to adhere to structured sleep-wake schedules designed to stabilize sleep outcomes and circadian phase prior to in-laboratory testing. The effectiveness of this approach has not been rigorously evaluated, however. We therefore investigated the differences between participants' unstructured and structured sleep over a three-week interval. METHODS: Twenty-three healthy young adults completed three weeks of sleep monitoring, including one week of unstructured sleep and two weeks of structured sleep with consistent bed and wake times. Circadian phase was assessed via salivary dim light melatonin onset (DLMO) during both the unstructured and structured sleep episodes. RESULTS: Compared to their unstructured sleep schedule, participants' bed- and wake times were significantly earlier in their structured sleep, by 34 ± 44 mins (M ± SD) and 44 ± 41 mins, respectively. During structured sleep, circadian phase was earlier in 65% of participants (40 ± 32 mins) and was later in 35% (41 ± 25 mins) compared to unstructured sleep but did not change at the group level. While structured sleep reduced night-to-night variability in sleep timing and sleep duration, and improved the alignment (phase angle) between sleep onset and circadian phase in the most poorly aligned individuals (DLMO 3h before sleep onset time; 25% of our sample), sleep duration and quality were unchanged. CONCLUSION: Our results show adherence to a structured sleep schedule results in more regular sleep timing, and improved alignment between sleep and circadian timing for those individuals who previously had poorer alignment. Our findings support the use of structured sleep schedules prior to in-laboratory sleep and circadian studies to stabilize sleep and circadian timing in healthy volunteers

A machine learning model for multi-night actigraphic detection of chronic insomnia: development and validation of a pre-screening tool

We propose a novel machine learning-based method for analysing multi-night actigraphy signals to objectively classify and differentiate nocturnal awakenings in individuals with chronic insomnia (CI) and their cohabiting healthy partners. We analysed nocturnal actigraphy signals from 40 cohabiting couples with one partner seeking treatment for insomnia. We extracted 12 time-domain dynamic and nonlinear features from the actigraphy signals to classify nocturnal awakenings in healthy individuals and those with CI. These features were then used to train two machine learning classifiers, random forest (RF) and support vector machine (SVM). An optimization algorithm that incorporated the predicted quality of each night for each individual was used to classify individuals into CI or healthy sleepers. Using the proposed actigraphic signal analysis technique, coupled with a rigorous leave-one-out validation approach, we achieved a classification accuracy of 80% (sensitivity: 76%, specificity: 82%) in classifying CI individuals and their healthy bed partners. The RF classifier (accuracy: 80%) showed a better performance than SVM (accuracy: 75%). Our approach to analysing the multi-night nocturnal actigraphy recordings provides a new method for screening individuals with CI, using wrist-actigraphy devices, facilitating home monitoring