Dataset Information

EEG measures index neural and cognitive recovery from sleep deprivation.

ABSTRACT: Sleep deprivation impairs many cognitive abilities, but these impairments can be reversed after a certain quantity and quality of sleep. The ability to inhibit responding is particularly susceptible to disruption after prolonged wakefulness. How recovery sleep (RS) alters brain activity, leading to improved performance on a variety of cognitive tasks, remains unclear. This issue was examined in the current study using spectral analysis of electroencephalogram (EEG) data during sleep. These measures of sleep physiology were acquired after both normal sleep (NS) and RS, and were related to measures of inhibitory control and concurrent brain activity. Subjects were nine young adults who underwent functional magnetic resonance imaging twice, after 9 h of NS and after 10 h of RS that followed 38 h of being awake. A multiple regression model was used to examine differences between conditions in (1) EEG spectral power during sleep, (2) probability of successful inhibition in a go/no-go task, and (3) activation within a region of right prefrontal cortex during the task. Performance recovery, as indexed by reduced performance differences between conditions, was predicted by increased delta power and decreased sigma power in RS compared with NS. These EEG variables predicted most of the variance in inhibitory performance difference between conditions. Regressions also suggested that RS improved performance because of changes in brain function including prefrontal regions that resulted from delta rebound. We thus propose that slow waves, reflected in delta power during RS, act to restore brain function, thereby improving cognitive performance that entails response inhibition.

SUBMITTER: Mander BA

PROVIDER: S-EPMC2835412 | biostudies-literature | 2010 Feb

REPOSITORIES: biostudies-literature

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EEG measures index neural and cognitive recovery from sleep deprivation.

Mander Bryce A BA Reid Kathryn J KJ Baron Kelly G KG Tjoa Tjoson T Parrish Todd B TB Paller Ken A KA Gitelman Darren R DR Zee Phyllis C PC

The Journal of neuroscience : the official journal of the Society for Neuroscience 20100201 7

Sleep deprivation impairs many cognitive abilities, but these impairments can be reversed after a certain quantity and quality of sleep. The ability to inhibit responding is particularly susceptible to disruption after prolonged wakefulness. How recovery sleep (RS) alters brain activity, leading to improved performance on a variety of cognitive tasks, remains unclear. This issue was examined in the current study using spectral analysis of electroencephalogram (EEG) data during sleep. These measu ...[more]

PMID: 20164352

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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

EEG measures index neural and cognitive recovery from sleep deprivation.

Publications

EEG measures index neural and cognitive recovery from sleep deprivation.

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