scholarly journals A comparison of continuous and intermittent EEG recordings in geese: How much data are needed to reliably estimate sleep–wake patterns?

2021 ◽  
Author(s):  
Sjoerd J. van Hasselt ◽  
Simon Verhulst ◽  
Theunis Piersma ◽  
Niels C. Rattenborg ◽  
Peter Meerlo
Keyword(s):  
Wake Patterns ◽  
Eeg Recordings

Efficient but less mobilized monitoring system in individuals with high aggressive predispositions

2019 ◽  
Author(s):  
Fanny Grisetto ◽  
Yvonne N. Delevoye-Turrell ◽  
Clémence Roger
Keyword(s):  
Cognitive Control ◽  
Monitoring System ◽  
Congruency Effect ◽  
Performance Monitoring ◽  
Simon Task ◽  
Aggressive Behaviors ◽  
Eeg Recordings ◽  
Global Reduction

Aggressive behaviors in pathological and healthy populations have been largely related to poor cognitive control functioning. However, few studies investigated the influence of aggressive traits (i.e., aggressiveness) on cognitive control. In the current study, we investigated the effects of aggressiveness on cognitive control abilities and particularly, on performance monitoring. Thirty-two participants performed a Simon task while electroencephalography (EEG) and electromyography (EMG) were recorded. Participants were classified as high and low aggressive using the BPAQ questionnaire (Buss & Perry, 1992). EMG recordings were used to reveal three response types by uncovering small incorrect muscular activations in ~15% of correct trials (i.e., partial-errors) that have to be distinguished from full-error and pure-correct responses. For these three response types, EEG recordings were used to extract fronto-central negativities indicative of performance monitoring, the error and correct (-related) negativities (ERN/Ne and CRN/Nc). Behavioral results indicated that the high aggressiveness group had a larger congruency effect compared to the low aggressiveness group, but there were no differences in accuracy. EEG results revealed a global reduction in performance-related negativities amplitudes in all the response types in the high aggressiveness group compared to the low aggressiveness group. Interestingly, the distinction between the ERN/Ne and the CRN/Nc components was preserved both in high and low aggressiveness groups. In sum, high aggressive traits did not affect the capacity to self-evaluate erroneous from correct actions but are associated with a decrease in the importance given to one’s own performance. The implication of these findings are discussed in relation to pathological aggressiveness.

scholarly journals Endogenous memory reactivation during sleep in humans is clocked by slow oscillation-spindle complexes

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Thomas Schreiner ◽  
Marit Petzka ◽  
Tobias Staudigl ◽  
Bernhard P. Staresina
Keyword(s):  
Memory Function ◽  
Nrem Sleep ◽  
Stimulus Category ◽  
Learning Material ◽  
Memory Reactivation ◽  
Eeg Recordings ◽  
Surface Eeg ◽  
Function Of Sleep ◽  
Prior Experiences

AbstractSleep is thought to support memory consolidation via reactivation of prior experiences, with particular electrophysiological sleep signatures (slow oscillations (SOs) and sleep spindles) gating the information flow between relevant brain areas. However, empirical evidence for a role of endogenous memory reactivation (i.e., without experimentally delivered memory cues) for consolidation in humans is lacking. Here, we devised a paradigm in which participants acquired associative memories before taking a nap. Multivariate decoding was then used to capture endogenous memory reactivation during non-rapid eye movement (NREM) sleep in surface EEG recordings. Our results reveal reactivation of learning material during SO-spindle complexes, with the precision of SO-spindle coupling predicting reactivation strength. Critically, reactivation strength (i.e. classifier evidence in favor of the previously studied stimulus category) in turn predicts the level of consolidation across participants. These results elucidate the memory function of sleep in humans and emphasize the importance of SOs and spindles in clocking endogenous consolidation processes.

scholarly journals Sounding It Out: Auditory Stimulation and Overnight Memory Processing

2021 ◽  
Author(s):  
Marcus O. Harrington ◽  
Scott A. Cairney
Keyword(s):  
Rem Sleep ◽  
Memory Consolidation ◽  
Slow Wave Sleep ◽  
Auditory Stimulation ◽  
Theta Oscillations ◽  
Neural Oscillations ◽  
Healthy Children ◽  
Factors Affecting ◽  
Memory Processing ◽  
Eeg Recordings

Abstract Purpose of Review Auditory stimulation is a technique that can enhance neural oscillations linked to overnight memory consolidation. In this review, we evaluate the impacts of auditory stimulation on the neural oscillations of sleep and associated memory processes in a variety of populations. Recent Findings Cortical EEG recordings of slow-wave sleep (SWS) are characterised by two cardinal oscillations: slow oscillations (SOs) and sleep spindles. Auditory stimulation delivered in SWS enhances SOs and phase-coupled spindle activity in healthy children and adults, children with ADHD, adults with mild cognitive impairment and patients with major depression. Under certain conditions, auditory stimulation bolsters the benefits of SWS for memory consolidation, although further work is required to fully understand the factors affecting stimulation-related memory gains. Recent work has turned to rapid eye movement (REM) sleep, demonstrating that auditory stimulation can be used to manipulate REM sleep theta oscillations. Summary Auditory stimulation enhances oscillations linked to overnight memory processing and shows promise as a technique for enhancing the memory benefits of sleep.

Decoding the Torque of Lower Limb Joints from EEG Recordings of Pre-gait Movements Using a Machine Learning Scheme

2021 ◽  
Author(s):  
Luis Mercado ◽  
Lucero Alvarado ◽  
Griselda Quiroz-Compean ◽  
Rebeca Romo-Vazquez ◽  
Hugo Vélez-Pérez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lower Limb ◽  
Learning Scheme ◽  
Eeg Recordings

scholarly journals Measuring sleep regularity: Theoretical properties and practical usage of existing metrics

SLEEP ◽  
2021 ◽  
Author(s):  
Dorothee Fischer ◽  
Elizabeth B Klerman ◽  
Andrew J K Phillips
Keyword(s):  
Large Scale ◽  
Research Question ◽  
Group Differences ◽  
Regularity Index ◽  
Wake Patterns ◽  
Health Related ◽  
Phase Deviation ◽  
Unbiased Estimates ◽  
Large Scale Simulations ◽  
Larger Sample

Abstract Study Objectives Sleep regularity predicts many health-related outcomes. Currently, however, there is no systematic approach to measuring sleep regularity. Traditionally, metrics have assessed deviations in sleep patterns from an individual’s average. Traditional metrics include intra-individual standard deviation (StDev), Interdaily Stability (IS), and Social Jet Lag (SJL). Two metrics were recently proposed that instead measure variability between consecutive days: Composite Phase Deviation (CPD) and Sleep Regularity Index (SRI). Using large-scale simulations, we investigated the theoretical properties of these five metrics. Methods Multiple sleep-wake patterns were systematically simulated, including variability in daily sleep timing and/or duration. Average estimates and 95% confidence intervals were calculated for six scenarios that affect measurement of sleep regularity: ‘scrambling’ the order of days; daily vs. weekly variation; naps; awakenings; ‘all-nighters’; and length of study. Results SJL measured weekly but not daily changes. Scrambling did not affect StDev or IS, but did affect CPD and SRI; these metrics, therefore, measure sleep regularity on multi-day and day-to-day timescales, respectively. StDev and CPD did not capture sleep fragmentation. IS and SRI behaved similarly in response to naps and awakenings but differed markedly for all-nighters. StDev and IS required over a week of sleep-wake data for unbiased estimates, whereas CPD and SRI required larger sample sizes to detect group differences. Conclusions Deciding which sleep regularity metric is most appropriate for a given study depends on a combination of the type of data gathered, the study length and sample size, and which aspects of sleep regularity are most pertinent to the research question.

scholarly journals Conditional Entropy: A Potential Digital Marker for Stress

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 286
Author(s):  
Soheil Keshmiri
Keyword(s):  
Resting State ◽  
Nearest Neighbor ◽  
Brain Activity ◽  
Solution Concept ◽  
Conditional Entropy ◽  
K Nearest Neighbor ◽  
Brain Response ◽  
Substantial Progress ◽  
Eeg Recordings ◽  
The Brain

Recent decades have witnessed a substantial progress in the utilization of brain activity for the identification of stress digital markers. In particular, the success of entropic measures for this purpose is very appealing, considering (1) their suitability for capturing both linear and non-linear characteristics of brain activity recordings and (2) their direct association with the brain signal variability. These findings rely on external stimuli to induce the brain stress response. On the other hand, research suggests that the use of different types of experimentally induced psychological and physical stressors could potentially yield differential impacts on the brain response to stress and therefore should be dissociated from more general patterns. The present study takes a step toward addressing this issue by introducing conditional entropy (CE) as a potential electroencephalography (EEG)-based resting-state digital marker of stress. For this purpose, we use the resting-state multi-channel EEG recordings of 20 individuals whose responses to stress-related questionnaires show significantly higher and lower level of stress. Through the application of representational similarity analysis (RSA) and K-nearest-neighbor (KNN) classification, we verify the potential that the use of CE can offer to the solution concept of finding an effective digital marker for stress.

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