Subjective sleepiness correlates negatively with global alpha (8–12 Hz) and positively with central frontal theta (4–8 Hz) frequencies in the human resting awake electroencephalogram

Strijkstra, Arjen M.; Beersma, Domien; Drayer, Berdine; Halbesma, Nynke; Daan, Serge

doi:10.1016/s0304-3940(03)00033-8

Cited by 264 publications

(215 citation statements)

References 15 publications

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“…It has been established that spontaneous fluctuations in intrinsic FC are not simply noise (35) and can be correlated with physiological markers, such as EEG or MEG power at different frequency bands (19,20), as well as with heart rate variability (21). Shifts in EEG power in the α-and θ-bands correspond to changes in arousal (36)(37)(38). Although these results are of physiological relevance, they only indirectly link FC fluctuation and behavioral state, require the use of technically demanding and expensive simultaneous EEG-fMRI methodology, and are difficult to deploy for real-time behavioral assessment.…”

Section: Discussionmentioning

confidence: 99%

Spontaneous eyelid closures link vigilance fluctuation with fMRI dynamic connectivity states

Wang

Ong

Patanaik

et al. 2016

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

191

185

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Fluctuations in resting-state functional connectivity occur but their behavioral significance remains unclear, largely because correlating behavioral state with dynamic functional connectivity states (DCS) engages probes that disrupt the very behavioral state we seek to observe. Observing spontaneous eyelid closures following sleep deprivation permits nonintrusive arousal monitoring. During periods of low arousal dominated by eyelid closures, sliding-window correlation analysis uncovered a DCS associated with reduced withinnetwork functional connectivity of default mode and dorsal/ventral attention networks, as well as reduced anticorrelation between these networks. Conversely, during periods when participants' eyelids were wide open, a second DCS was associated with less decoupling between the visual network and higher-order cognitive networks that included dorsal/ventral attention and default mode networks. In subcortical structures, eyelid closures were associated with increased connectivity between the striatum and thalamus with the ventral attention network, and greater anticorrelation with the dorsal attention network. When applied to task-based fMRI data, these two DCS predicted interindividual differences in frequency of behavioral lapsing and intraindividual temporal fluctuations in response speed. These findings with participants who underwent a night of total sleep deprivation were replicated in an independent dataset involving partially sleep-deprived participants. Fluctuations in functional connectivity thus appear to be clearly associated with changes in arousal. T he existence of large-scale functional brain networks is evidenced by well-defined spatial patterns of correlated bloodoxygenation level-dependent (BOLD) signal fluctuation in fMRI data (1). Recent work has shown that functional connectivity (FC) within and between brain networks is dynamic, corresponding to the observation that even while we are performing a task, our mental focus fluctuates (2). Fluctuation of fMRI-based FC occurs over tens of seconds (3, 4) and exhibits different patterns across conscious and unconscious states (5, 6). Furthermore, just as interindividual differences in stationary FC relate to variation in human behavior and cognition (7-10), it seems likely that recurring patterns (11) of fluctuating FC have behavioral significance.Temporal fluctuations in FC can arise from conscious mental activity (12), episodes of random synchrony (3), or simply timevarying levels of physiological noise (13,14). The association between BOLD signal fluctuation in the default mode network (DMN) and mind-wandering episodes (15-17) has prompted investigations into the behavioral correlates of spontaneous resting-state FC fluctuations (11,18). Although these fluctuations in FC have been shown to correlate with several physiological markers, such as electroencephalogram (EEG) power, magnetoencephalography (MEG) power, and heart rate variability (19-21), their behavioral significance remains unclear.A key obstacle to elucidating clear FC...

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Section: Discussionmentioning

confidence: 99%

Spontaneous eyelid closures link vigilance fluctuation with fMRI dynamic connectivity states

Wang

Ong

Patanaik

et al. 2016

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

191

185

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“…Moreover, the positive correlation between beta absolute power increase (e.g., temporal re g i o n ) and the hours of prolonged wakefulness might be i n t e r p reted as an index of eff o rtand temporary activation. A speculative interpretation may be that the o b s e rved beta power increase reflects pro c e s s e s underlying an increasing eff o rt to maintain wakefulness, i.e., the motivation to stay awake, that coincides 16 with an increased feeling of sleepiness.…”

Section: Discussionmentioning

confidence: 99%

Electroencephalographic changes after one nigth of sleep deprivation

Ferreira

Deslandes

Moraes

et al. 2006

Arq. Neuro-Psiquiatr.

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-Total or partial sleep deprivation (SD) causes degrading effects on diff e rent cognitive and psychomotor functions that might be related to electrophysiological changes frequently observed. In the pre sent study, we investigated the effects of one night of sleep deprivation on waking EEG. Experimental protocol consisted of re c o rding electroencephalographic data from eleven healthy young subjects before (baseline) and after (time 2) one night of sleep deprivation. A natural log transformation was carried out and showed a significant increase in theta T6 (p=0.041), O2 (p=0.018) and OZ (p=0.028); and delta T6 (p=0.043) relative power; and a decrease in alpha Fp1 (p=0.040), F3 (p=0.013), Fp2 (p=0.033), T4 (p=0.050), T6 (p=0.018), O2 (p=0.011) and Oz (p=0.025) and beta (p=0.022) absolute power. These outcomes show that the EEG power spectra, after sleep deprivation, exhibit site-specific diff e rences in particular fre q u e ncy bands and corroborate for the premise of local aspects of brain adaptation after sleep deprivation, rather than global.KEY WORDS: sleep deprivation, qEEG, power spectral analysis, cortical activity.Respostas eletrencefalográficas após uma noite de privação de sono RESUMO -Privação total ou parcial de sono causa efeitos deletérios em diferentes funções cognitivas e psicomotoras, que podem estar relacionados às mudanças eletrofísiológicas frequentemente observadas. No p resente estudo, investigou-se os efeitos de uma noite de privação de sono nas respostas eletre n c e f a l ográficas de repouso. O protocolo experimental consistiu na coleta e gravação dos dados do qEEG de onze sujeitos jovens e saudáveis antes (momento baseline) e após (momento 2) uma noite de privação de sono. Todos os dados sofreram transformação logarítmica, que evidenciou um aumento significativo nas potên-cias relativas de teta T6 (p=0,041), O2 (p=0,018) e OZ (p=0,028); e delta T6 (p=0,043). As bandas de freqüência mais rápidas alfa Fp1 (p=0,040), F3 (p=0,013), Fp2 (p=0,033), T4 (p=0,050), T6 (p=0,018), O2 (p=0,011), Oz (p=0,025) e beta (p=0,022) apresentaram reduções significativas na potência absoluta. Os re s u l t a d o s demonstram que o espectro de potência exibe diferenças específicas em freqüências de bandas difere n t e s , e corroboram para a premissa de que o cére b ro, após privação ou perda de sono, promove ajustes e adaptações locais, ao invés de globais. PALAVRAS-CHAVE: privação de sono, qEEG, análise do espectro de potência, atividade cortical.

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“…Quantitative EEG measures-Using more quantitative measures for alertness, there is an increase in slow frequencies and decrease in fast activities during the sleep onset period, with different investigators advocating using various measures or ratios of theta and delta with beta and alpha (Gevins et al, 1977;Matousek and Petersen, 1983;Merica and Gaillard, 1992). The theta changes are more prominent frontally while the alpha changes may be more generalized (Strijkstra et al, 2003). There are differences between drowsiness with the eyes open and closed, e.g.…”

Section: Conditions Associated With Changes In Alertness and Sustainementioning

confidence: 99%

Vigilance, alertness, or sustained attention: physiological basis and measurement

Oken

Salinsky

Elsas

2006

Clinical Neurophysiology

661

463

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Vigilance is a term with varied definitions but the most common usage is sustained attention or tonic alertness. This usage of vigilance implies both the degree of arousal on the sleep-wake axis and the level of cognitive performance. There are many interacting neural and neurotransmitter systems that affect vigilance. Most studies of vigilance have relied on states where the sleep-wake state is altered, e.g. drowsiness, sleep-deprivation, and CNS-active drugs, but there are factors ranging from psychophysics to motivation that may impact vigilance. While EEG is the most commonly studied physiologic measure of vigilance, various measures of eye movement and of autonomic nervous system activity have also been used. This review paper discusses the underlying neural basis of vigilance and its assessment using physiologic tools. Since, assessment of vigilance requires assessment of cognitive function this aspect is also discussed.

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Subjective sleepiness correlates negatively with global alpha (8–12 Hz) and positively with central frontal theta (4–8 Hz) frequencies in the human resting awake electroencephalogram

Cited by 264 publications

References 15 publications

Spontaneous eyelid closures link vigilance fluctuation with fMRI dynamic connectivity states

Spontaneous eyelid closures link vigilance fluctuation with fMRI dynamic connectivity states

Electroencephalographic changes after one nigth of sleep deprivation

Vigilance, alertness, or sustained attention: physiological basis and measurement

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