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Event-related activity and phase locking during a psychomotor vigilance task over the course of sleep deprivation.

ABSTRACT: There is profound knowledge that sleep restriction increases tonic (event-unrelated) electroencephalographic (EEG) activity. In the present study we focused on time-locked activity by means of phasic (event-related) EEG analysis during a psychomotor vigilance task (PVT) over the course of sleep deprivation. Twenty healthy subjects (10 male; mean age?±?SD: 23.45?±?1.97?years) underwent sleep deprivation for 24?h. Subjects had to rate their sleepiness hourly (Karolinska Sleepiness Scale) and to perform a PVT while EEG was recorded simultaneously. Tonic EEG changes in the ? (1-4?Hz), ? (4-8?Hz) and ? (8-12?Hz) frequency range were investigated by power spectral analyses. Single-trial (phase-locking index, PLI) and event-related potential (ERP) analyses (P1, N1) were used to examine event-related changes in EEG activity. Subjective sleepiness, PVT reaction times and tonic EEG activity (delta and theta spectral power) significantly increased over the night. In contrast, event-related EEG parameters decreased throughout sleep deprivation. Specifically, the ERP component P1 diminished in amplitude, and delta and theta PLI estimates decreased progressively over the night. It is suggested that event-related EEG measures (such as the amplitude of the P1 and especially delta/theta phase-locking) serve as a complimentary method to track the deterioration of attention and performance during sleep loss. As these measures actually reflect the impaired response to specific events rather than tonic changes during sleep deprivation they are a promising tool for future sleep research.

SUBMITTER: Hoedlmoser K

PROVIDER: S-EPMC3439125 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature

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Event-related activity and phase locking during a psychomotor vigilance task over the course of sleep deprivation.

Hoedlmoser Kerstin K Griessenberger Hermann H Fellinger Robert R Freunberger Roman R Klimesch Wolfgang W Gruber Walter W Schabus Manuel M

Journal of sleep research 20101026 3

There is profound knowledge that sleep restriction increases tonic (event-unrelated) electroencephalographic (EEG) activity. In the present study we focused on time-locked activity by means of phasic (event-related) EEG analysis during a psychomotor vigilance task (PVT) over the course of sleep deprivation. Twenty healthy subjects (10 male; mean age ± SD: 23.45 ± 1.97 years) underwent sleep deprivation for 24 h. Subjects had to rate their sleepiness hourly (Karolinska Sleepiness Scale) and to pe ...[more]

PMID: 20977513

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Project description:To identify measures derived from baseline psychomotor vigilance task (PVT) performance that can reliably predict vulnerability to sleep deprivation.Subjects underwent total sleep deprivation and completed a 10-min PVT every 1-2 h in a controlled laboratory setting. Participants were categorized as vulnerable or resistant to sleep deprivation, based on a median split of lapses that occurred following sleep deprivation. Standard reaction time, drift diffusion model (DDM), and wavelet metrics were derived from PVT response times collected at baseline. A support vector machine model that incorporated maximum relevance and minimum redundancy feature selection and wrapper-based heuristics was used to classify subjects as vulnerable or resistant using rested data.Two academic sleep laboratories.Independent samples of 135 (69 women, age 18 to 25 y), and 45 (3 women, age 22 to 32 y) healthy adults.In both datasets, DDM measures, number of consecutive reaction times that differ by more than 250 ms, and two wavelet features were selected by the model as features predictive of vulnerability to sleep deprivation. Using the best set of features selected in each dataset, classification accuracy was 77% and 82% using fivefold stratified cross-validation, respectively.In both datasets, DDM measures, number of consecutive reaction times that differ by more than 250 ms, and two wavelet features were selected by the model as features predictive of vulnerability to sleep deprivation. Using the best set of features selected in each dataset, classification accuracy was 77% and 82% using fivefold stratified cross-validation, respectively.Despite differences in experimental conditions across studies, drift diffusion model parameters associated reliably with individual differences in performance during total sleep deprivation. These results demonstrate the utility of drift diffusion modeling of baseline performance in estimating vulnerability to psychomotor vigilance decline following sleep deprivation.

Dataset Information

Event-related activity and phase locking during a psychomotor vigilance task over the course of sleep deprivation.

Publications

Event-related activity and phase locking during a psychomotor vigilance task over the course of sleep deprivation.

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