Detecting deteriorated vigilance using percentage of eyelid closure time during behavioral maintenance of wakefulness tests

Abe, Takashi; Nonomura, Tomohide; Komada, Yoko; Asaoka, Shoichi; Sasai, Taeko; Ueno, Atsushi; Inoue, Yuichi

doi:10.1016/j.ijpsycho.2011.09.012

Cited by 76 publications

(58 citation statements)

References 38 publications

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“…4, 9, and 56; SI Appendix, SI Text) on a 3-min test (PVT-B) that was obtained weekly (once in the morning, once in the evening) by computer (4), with 100% complete data acquisition (n = 888 tests). Facial videos were recorded at 30 frames per second during each PVT-B and evaluated for slow eyelid closures indicative of sleepiness (57). Immediately before or after each PVT-B test, crewmembers completed computerized scales that included 100-mm visual analog scales with the following binary anchors: good/poor sleep quality (morning only), high/low workload (evening only), and high/ low tiredness (evening only).…”

Section: Methodsmentioning

confidence: 99%

Mars 520-d mission simulation reveals protracted crew hypokinesis and alterations of sleep duration and timing

Basner

Dinges

Mollicone

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

154

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The success of interplanetary human spaceflight will depend on many factors, including the behavioral activity levels, sleep, and circadian timing of crews exposed to prolonged microgravity and confinement. To address the effects of the latter, we used a highfidelity ground simulation of a Mars mission to objectively track sleep-wake dynamics in a multinational crew of six during 520 d of confined isolation. Measurements included continuous recordings of wrist actigraphy and light exposure (4.396 million min) and weekly computer-based neurobehavioral assessments (n = 888) to identify changes in the crew's activity levels, sleep quantity and quality, sleep-wake periodicity, vigilance performance, and workload throughout the record-long 17 mo of mission confinement. Actigraphy revealed that crew sedentariness increased across the mission as evident in decreased waking movement (i.e., hypokinesis) and increased sleep and rest times. Light exposure decreased during the mission. The majority of crewmembers also experienced one or more disturbances of sleep quality, vigilance deficits, or altered sleep-wake periodicity and timing, suggesting inadequate circadian entrainment. The results point to the need to identify markers of differential vulnerability to hypokinesis and sleepwake changes during the prolonged isolation of exploration spaceflight and the need to ensure maintenance of circadian entrainment, sleep quantity and quality, and optimal activity levels during exploration missions. Therefore, successful adaptation to such missions will require crew to transit in spacecraft and live in surface habitats that instantiate aspects of Earth's geophysical signals (appropriately timed light exposure, food intake, exercise) required for temporal organization and maintenance of human behavior.sleep-wake regulation | astronaut T he success of human interplanetary spaceflight, which is anticipated to be in this century, will depend on the ability of spacefarers to remain confined and isolated from Earth much longer than previous missions or simulations, while maintaining the intensity and timing of behavioral activity necessary to accomplish the mission and mitigate the effects of microgravity. A total of four people have spent >1 y in space, with the record of 437 consecutive days on the Mir space station set by Valery Polyakov. The longest Earth-based spaceflight simulation involved four Russians confined in connected hyperbaric chambers for 240 consecutive days. Antarctic winter-over missions have extended up to 363 d. Prediction of how prolonged confinement affects activity levels and sleep-wake dynamics of space explorers is needed to inform spacecraft habitability requirements, crew selection, and behavioral countermeasures during interplanetary missions (1-3). To address this need, we obtained objective neurobehavioral data on the activity patterns of a multinational, culturally diverse crew of six males with backgrounds in engineering, medicine, physiology, and space training, who participated in a high-fidelity g...

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Section: Methodsmentioning

confidence: 99%

Mars 520-d mission simulation reveals protracted crew hypokinesis and alterations of sleep duration and timing

Basner

Dinges

Mollicone

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

154

View full text Add to dashboard Cite

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“…Figure 1 The devices used in this research (Source: emotiv.com, citycardriving.com, and photos taken during the experiment) Figure 2 The six measurement points used in this research Besides using EEG measurement as an objective indicator, this study also employed a subjective scale as a reference point for fatigue measurement. The Karolinska Sleepiness Scale, which consists of a 9-point scale, was used in this study based on its validity as stated in several previous studies (Kaida et al, 2006;Abe et al, 2011;Jagannath & Balasubramanian, 2014).…”

Section: Methodsmentioning

confidence: 99%

“…A Receiver Operating Characteristics (ROC) curve was also utilized to determine the best parameter to measure fatigue, along with the sensitivity and specificity of each parameter (Abe et al, 2011). A comparison of each band was also discussed along with the previous literature.…”

Section: Methodsmentioning

confidence: 99%

Effect Of Driving Duration On Eeg Fluctuations

Puspasari¹,

Iridiastadi

Sutalaksana

et al. 2017

IJTech

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Road accidents are a major issue in Indonesia, and their number increases every year. Based on previous studies, mental fatigue is one of the biggest factors leading to road accidents and is majorly affected by mental workload. Driving duration is one of the factors that triggers mental fatigue. The prior literature cites electroencephalogram (EEG) measurement as the gold standard for measuring fatigue. However, there has been only limited study to examine the EEG indicators that are affected by driving duration, and the prior research still contains disagreements regarding the best EEG parameter for use in measuring fatigue. Therefore, this study aimed to evaluate the effect of driving duration on EEG fluctuation and determine the best EEG parameter related to fatigue. Seven participants were asked to spend three hours driving in a medium-fidelity simulator. A one-way ANOVA and correlation analysis were performed to measure the effect of driving duration on the EEG indicators and determine the correlation of the indicators. A Receiver Operating Characteristics (ROC) curve was also utilized to determine the variable with the greatest correlation with the subjective sleepiness indices. The results showed that at the end of three hours' driving, there was an increment in delta and theta activities, followed by a decrement in alpha and beta activities. In addition, the correlation of all bands was significant, with positive results for the alpha-beta and theta-delta bands, and a negative result in relation to each other. Furthermore, the results from the ROC curve revealed the Relative Power Ratio (RPR) of theta, the RPR of alpha, and the ratio of θ/α+β to be the best indicators among others, demonstrating a high degree of accuracy (above 85%).

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“…Specifically, behavioral arousal was quantified during each fMRI measurement (every 2.6 s for monkeys A and S and every 2.5 s for monkeys F and Z), wherein the fraction of eyelid opening was taken directly as a "behavioral arousal index" (SI Methods). Although eye behavior is often indicative of altered responsiveness and changes in neurophysiological arousal (39,40), it provides only an indirect measure of electrophysiological arousal and is not always informative: For instance, fluctuations in arousal can persist while the eyes remain completely closed. Nevertheless, as a first step, we examined the potential for this behavioral measure to yield a reproducible map of arousal-correlated fMRI signals in each subject.…”

Section: Significancementioning

confidence: 99%

Tracking brain arousal fluctuations with fMRI

Chang

Leopold

Schölvinck

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

302

299

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Changes in brain activity accompanying shifts in vigilance and arousal can interfere with the study of other intrinsic and task-evoked characteristics of brain function. However, the difficulty of tracking and modeling the arousal state during functional MRI (fMRI) typically precludes the assessment of arousal-dependent influences on fMRI signals. Here we combine fMRI, electrophysiology, and the monitoring of eyelid behavior to demonstrate an approach for tracking continuous variations in arousal level from fMRI data. We first characterize the spatial distribution of fMRI signal fluctuations that track a measure of behavioral arousal; taking this pattern as a template, and using the local field potential as a simultaneous and independent measure of cortical activity, we observe that the timevarying expression level of this template in fMRI data provides a close approximation of electrophysiological arousal. We discuss the potential benefit of these findings for increasing the sensitivity of fMRI as a cognitive and clinical biomarker.resting-state fMRI | spontaneous fluctuations | arousal | electrophysiology D uring both active task engagement and rest, the human brain exhibits fluctuations in neural activity that can be readily measured using functional MRI (fMRI). In recent years, examining the spatiotemporal organization of these fluctuations has generated novel insight into the functional architecture of the human brain and its changes with development and disease (1). A prominent approach for mapping this architecture is to study interregional correlations in the fMRI signal fluctuations, which, even during rest, appear to be indicative of networks supporting specific functions. However, despite the promise and rapidly increasing application of this technique in the endeavor of brain connectomics (2-4), its sensitivity and specificity are compromised by unexplained variability arising from multiple neural and nonneural sources (e.g., refs. 5-11). As a result, the interpretation of resting-state fMRI data and the efficacy of these data as a biomarker rely critically on understanding and accounting for such sources of variability (11-13).Changes in arousal, mediated by an interaction between the ascending arousal system and the neocortex, may strongly modulate neuronal activity in much of the brain (14-18). Indeed, changes in vigilance and arousal (hereafter described jointly as "arousal"), which can be especially prevalent during the passive and uncontrolled resting state, result in fMRI signal variability that may confound the extraction of functional networks (5, 19). For example, the amplitude and extent of correlations in restingstate fMRI data vary with EEG-and behaviorally defined indicators of drowsiness and light sleep (20)(21)(22)(23)(24)(25) and are altered by sleep deprivation (26-28) and caffeine-induced changes in arousal state (29). Distinct patterns of functional connectivity across multiple networks have been associated with distinct EEG-defined sleep stages (30, 31) with sufficient reliabilit...

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Detecting deteriorated vigilance using percentage of eyelid closure time during behavioral maintenance of wakefulness tests

Cited by 76 publications

References 38 publications

Mars 520-d mission simulation reveals protracted crew hypokinesis and alterations of sleep duration and timing

Mars 520-d mission simulation reveals protracted crew hypokinesis and alterations of sleep duration and timing

Effect Of Driving Duration On Eeg Fluctuations

Tracking brain arousal fluctuations with fMRI

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