Social stressors at work and sleep during weekends: The mediating role of psychological detachment

Social stressors at work may result in long-term health impairments if recovery is insufficient. In the present psychophysiological field study, we tested whether the inability to psychologically detach from work issues mediates the negative effect of social stressors at work on sleep during weekends. Sixty full-time employees participated in the study. Daily assessment included diaries on psychological detachment and continuous ambulatory actigraphy to assess psychophysiological indicators of sleep. Hierarchical regression analyses revealed that enduring social stressors at work were negatively related with psychological detachment on Sunday evening and negatively related with various sleep indicators on Sunday night. Furthermore, psychological detachment from work on Sunday evening partially mediated the effect of social stressors at work on two sleep indicators. Social stressors at work may threaten recovery processes just before the working week starts.

Illegitimate Tasks and Sleep Quality: An Ambulatory Study

The current study investigated the short-term effect of illegitimate tasks on sleep quality, assessed by actigraphy. Seventy-six employees of different service jobs participated in a 2-week data collection. Data were analysed by way of multilevel analyses. As predicted, illegitimate tasks were positively related to sleep fragmentation and sleep-onset latency, but not to sleep efficiency and not to sleep duration. Time pressure, social stressors at work and at home, and the value of the dependent variable from the previous day were controlled. Results confirm the predictive power of illegitimate tasks for a variable that can be considered crucial in the development of long-term outcomes of daily experiences.

Word encoding during sleep

To test whether humans can encode words during sleep we played everyday words to men while they were napping and assessed priming from sleep-played words following waking. Words were presented during non-rapid eye movement (NREM) sleep. Priming was assessed using a semantic and a perceptual priming test. These tests measured differences in the processing of words that had been or had not been played during sleep. Synonyms to sleep-played words were the targets in the semantic priming test that tapped the meaning of sleep-played words. All men responded to sleep-played words by producing up-states in their electroencephalogram. Up-states are NREM sleep-specific phases of briefly increased neuronal excitability. The word-evoked up-states might have promoted word processing during sleep. Yet, the mean performance in the priming tests administered following sleep was at chance level, which suggests that participants as a group failed to show priming following sleep. However, performance in the two priming tests was positively correlated to each other and to the magnitude of the word-evoked up-states. Hence, the larger a participant's word-evoked up-states, the larger his perceptual and semantic priming. Those participants who scored high on all variables must have encoded words during sleep. We conclude that some humans are able to encode words during sleep, but more research is needed to pin down the factors that modulate this ability.

The Influence of Pre-stimulus EEG Activity on Reaction Time During a Verbal Sternberg Task is Related to Musical Expertise

Abstract Previous work highlighted the possibility that musical training has an influence on cognitive functioning. The suggested reason for this influence is the strong recruitment of attention, planning, and working memory functions during playing a musical instrument. The purpose of the present work was twofold, namely to evaluate the general relationship between pre-stimulus electrophysiological activity and cognition, and more specifically the influence of musical expertise on working memory functions. With this purpose in mind, we used covariance mapping analyses to evaluate whether pre-stimulus electroencephalographic activity is predictive for reaction time during a visual working memory task (Sternberg paradigm) in musicians and non-musicians. In line with our hypothesis, we replicated previous findings pointing to a general predictive value of pre-stimulus activity for working memory performance. Most importantly, we also provide first evidence for an influence of musical expertise on working memory performance that could distinctively be predicted by pre-stimulus spectral power. Our results open novel perspectives for better comprehending the vast influences of musical expertise on cognition.

The functional significance of EEG microstates-Associations with modalities of thinking

The momentary, global functional state of the brain is reflected by its electric field configuration. Cluster analytical approaches consistently extracted four head-surface brain electric field configurations that optimally explain the variance of their changes across time in spontaneous EEG recordings. These four configurations are referred to as EEG microstate classes A, B, C, and D and have been associated with verbal/phonological, visual, attention reorientation, and subjective interoceptive-autonomic processing, respectively. The present study tested these associations via an intra-individual and inter-individual analysis approach. The intra-individual approach tested the effect of task-induced increased modality-specific processing on EEG microstate parameters. The inter-individual approach tested the effect of personal modality-specific parameters on EEG microstate parameters. We obtained multichannel EEG from 61 healthy, right-handed, male students during four eyes-closed conditions: object-visualization, spatial-visualization, verbalization (6 runs each), and resting (7 runs). After each run, we assessed participants' degrees of object-visual, spatial-visual, and verbal thinking using subjective reports. Before and after the recording, we assessed modality-specific cognitive abilities and styles using nine cognitive tests and two questionnaires. The EEG of all participants, conditions, and runs was clustered into four classes of EEG microstates (A, B, C, and D). RMANOVAs, ANOVAs and post-hoc paired t-tests compared microstate parameters between conditions. TANOVAs compared microstate class topographies between conditions. Differences were localized using eLORETA. Pearson correlations assessed interrelationships between personal modality-specific parameters and EEG microstate parameters during no-task resting. As hypothesized, verbal as opposed to visual conditions consistently affected the duration, occurrence, and coverage of microstate classes A and B. Contrary to associations suggested by previous reports, parameters were increased for class A during visualization, and class B during verbalization. In line with previous reports, microstate D parameters were increased during no-task resting compared to the three internal, goal-directed tasks. Topographic differences between conditions concerned particular sub-regions of components of the metabolic default mode network. Modality-specific personal parameters did not consistently correlate with microstate parameters except verbal cognitive style which correlated negatively with microstate class A duration and positively with class C occurrence. This is the first study that aimed to induce EEG microstate class parameter changes based on their hypothesized functional significance. Beyond, the associations of microstate classes A and B with visual and verbal processing, respectively and microstate class D with interoceptive-autonomic processing, our results suggest that a finely-tuned interplay between all four EEG microstate classes is necessary for the continuous formation of visual and verbal thoughts, as well as interoceptive-autonomic processing. Our results point to the possibility that the EEG microstate classes may represent the head-surface measured activity of intra-cortical sources primarily exhibiting inhibitory functions. However, additional studies are needed to verify and elaborate on this hypothesis.

Chow–Liu trees are sufficient predictive models for reproducing key features of functional networks of periictal EEG time-series

Seizure freedom in patients suffering from pharmacoresistant epilepsies is still not achieved in 20–30% of all cases. Hence, current therapies need to be improved, based on a more complete understanding of ictogenesis. In this respect, the analysis of functional networks derived from intracranial electroencephalographic (iEEG) data has recently become a standard tool. Functional networks however are purely descriptive models and thus are conceptually unable to predict fundamental features of iEEG time-series, e.g., in the context of therapeutical brain stimulation. In this paper we present some first steps towards overcoming the limitations of functional network analysis, by showing that its results are implied by a simple predictive model of time-sliced iEEG time-series. More specifically, we learn distinct graphical models (so called Chow–Liu (CL) trees) as models for the spatial dependencies between iEEG signals. Bayesian inference is then applied to the CL trees, allowing for an analytic derivation/prediction of functional networks, based on thresholding of the absolute value Pearson correlation coefficient (CC) matrix. Using various measures, the thus obtained networks are then compared to those which were derived in the classical way from the empirical CC-matrix. In the high threshold limit we find (a) an excellent agreement between the two networks and (b) key features of periictal networks as they have previously been reported in the literature. Apart from functional networks, both matrices are also compared element-wise, showing that the CL approach leads to a sparse representation, by setting small correlations to values close to zero while preserving the larger ones. Overall, this paper shows the validity of CL-trees as simple, spatially predictive models for periictal iEEG data. Moreover, we suggest straightforward generalizations of the CL-approach for modeling also the temporal features of iEEG signals.

Physiological time structure of the tibialis anterior motor activity during sleep in mice, rats and humans

The validation of rodent models for restless legs syndrome (Willis-Ekbom disease) and periodic limb movements during sleep requires knowledge of physiological limb motor activity during sleep in rodents. This study aimed to determine the physiological time structure of tibialis anterior activity during sleep in mice and rats, and compare it with that of healthy humans. Wild-type mice (n = 9) and rats (n = 8) were instrumented with electrodes for recording the electroencephalogram and electromyogram of neck muscles and both tibialis anterior muscles. Healthy human subjects (31 ± 1 years, n = 21) underwent overnight polysomnography. An algorithm for automatic scoring of tibialis anterior electromyogram events of mice and rats during non-rapid eye movement sleep was developed and validated. Visual scoring assisted by this algorithm had inter-rater sensitivity of 92-95% and false-positive rates of 13-19% in mice and rats. The distribution of the time intervals between consecutive tibialis anterior electromyogram events during non-rapid eye movement sleep had a single peak extending up to 10 s in mice, rats and human subjects. The tibialis anterior electromyogram events separated by intervals <10 s mainly occurred in series of two-three events, their occurrence rate in humans being lower than in mice and similar to that in rats. In conclusion, this study proposes reliable rules for scoring tibialis anterior electromyogram events during non-rapid eye movement sleep in mice and rats, demonstrating that their physiological time structure is similar to that of healthy young human subjects. These results strengthen the basis for translational rodent models of periodic limb movements during sleep and restless legs syndrome/Willis-Ekbom disease.