Reduced Resting-State EEG Power Spectra and Functional Connectivity after 24 and 36 Hours of Sleep Deprivation

Electroencephalographic power spectrum patterns related to the intelligence of children with learning disorders

Children with learning disorders (LD) perform below average in tests of academic abilities and intelligence. These children also have a significantly abnormal resting-state electroencephalogram (EEG) compared to children with typical development (TD), i.e., an excess of slow brain oscillations such as delta and theta that may be markers of inefficient cognitive processing. We aimed to explore the relationship between the performance in an intelligence test and the resting-state EEG power spectrum of children with LD. Ninety-one children with LD and 45 control children with TD were evaluated with the Wechsler Intelligence Scale for Children 4th Edition (WISC-IV) test of intelligence and a 19-channel EEG during an eyes-closed resting-state condition. The EEG dimensionality was reduced with a principal component analysis that yielded several components representing EEG bands with functional meaning. The first seven EEG components and the intelligence values were analyzed with multiple linear regression and a between-group discriminant analysis. The EEG power spectrum was significantly related to children's intelligence, predicting 13.1% of the IQ variance. Generalized delta and theta power were inversely related to IQ, whereas frontoparietal gamma activity was directly related. The intelligence test and the resting state EEG had a combined 82.4% success rate to discriminate between children with TD and those with LDs.

Exploring the evolving frontiers of sleep deprivation research in the post-COVID-19 era

Sleep deprivation research (SDR) has undergone substantial transformations in the aftermath of the COVID-19 pandemic. This comprehensive study explores the SDR's evolving trends, hotspots, and conceptual structure in the post-COVID-19 era. Using bibliometric analysis with VOSviewer and Bibliometrix software, we examined the SDR publications from January 2020 to December 2023. Core sources were identified using Bradford law, and a dataset of 4578 English-based data-driven studies was retrieved from the Scopus database. Our analysis revealed a remarkable annual growth rate of 37.92% in SDR from 2020 to 2023, indicating a heightened research focus in this domain. Among prolific authors, Shao emerged as a key contributor, while Sleep ranked prominently as a leading publication venue. The United States and China have emerged as the most impactful countries for advancing SDR research. Uncovering the most cited documents sheds light on various focal points within SDR, including sleep duration, mental health implications, cardiovascular events, the impact of COVID-19 on sleep patterns, dementia, resilience in children, and sleep patterns among preschoolers. Noteworthy keywords that emerged from the analysis encompassed "sleep deprivation," "depression," "insomnia," "COVID-19," "anxiety," "fatigue," and "obesity." Using a thematic mapping approach, we delineated distinct themes characterized by niche, basic, emerging, and declining patterns. Clusters of SDR research were observed in areas such as "Fatigue," "Sleep apnea," "Sleep," "Sleep deprivation," "Migraine," "Caffeine," and "Recovery." Furthermore, trending themes in SDR encompassed "Sleep disturbance," "excessive daytime sleepiness," and "Gamma-Aminobutyric Acid." These comprehensive findings provide valuable insights into the current landscape of SDR, illuminating emerging trends, identifying hotspots, and offering directions for future research in this critical field.

Frontocentral Delta and Theta Oscillatory Responses are Sensitive to Sleep Deprivation During a Working Memory Task

Sleep deprivation has become a severe public health problem in modern societies. Negative consequences of prolonged wakefulness on cognitive abilities have been demonstrated and working memory is one of the main cognitive functions that can be affected by sleep deprivation. This study aims to investigate the effects of sleep deprivation on working memory through EEG event-related oscillations. Thirty healthy young adult university students and graduates were included in this study (15 rested control - 15 sleep-deprived). A 2-back task was used to evaluate working memory, and both groups performed the task during EEG recording. The sleep-deprived (SD) group was required to stay awake for 24 h, and then the EEG session was conducted. The rested control (RC) subjects participated in the morning after a regular night's sleep. Event-related power and phase-locking analyses were applied, and delta (1-3.5 Hz), theta (4-6.5 Hz) and alpha (8-13 Hz) frequencies were investigated in the time-frequency domain. In the 2-back task, significantly prolonged reaction times were observed in the SD group. However, the decrease in accuracy rate was not significant. The EEG analyses revealed that the SD group had decreased frontocentral event-related delta and theta power responses after the presentation of stimuli. Moreover, task accuracy was positively correlated with the left frontocentral delta power in the SD group, and theta power in the RCs. Thus, we propose that the adverse effects of sleep deprivation on working memory can be observed through low-frequency oscillatory responses in the brain.

Impact of sleep deprivation on dynamic functional connectivity states

Sleep deprivation (SD) is an experimental procedure to study the effects of sleep loss on the human brain. Neuroimaging techniques, such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), have been pivotal in studying these effects. The present chapter aims to retrace the state of the art regarding the literature that examines the SD effects on the brain through functional connectivity (FC) evaluated in fMRI and EEG settings, separately. Specifically, we focused on the cognitive domains mainly affected by sleep loss and the underlying brain connectivity alterations. SD disrupts homeostatic and circadian processes, negatively affecting cognitive and cerebral functionality explored through FC. All evidence confirms the detrimental role of SD on brain connectivity impacting several resting-state networks and resulting in cognitive impairments. To conclude, SD may offer useful insights into pathogenic mechanisms likely resembling those induced by chronic sleep loss that might find their application in the clinical context.

Changes in electroencephalographic microstates between evening and morning are associated with overnight sleep slow waves in healthy individuals

STUDY OBJECTIVES

Microstates are semi-stable voltage topographies that account for most of electroencephalogram (EEG) variance. However, the impact of time of the day and sleep on microstates has not been examined. To address this gap, we assessed whether microstates differed between the evening and morning and whether sleep slow waves correlated with microstate changes in healthy participants.

METHODS

Forty-five healthy participants were recruited. Each participant underwent 6 minutes of resting state EEG recordings in the evening and morning, interleaved by sleep EEGs. Evening-to-morning changes in microstate duration, coverage, and occurrence were assessed. Furthermore, correlation between microstate changes and sleep slow-wave activity (SWA) and slow-wave density (SWD) were performed.

RESULTS

Two-way ANOVAs with microstate class (A, B, C, and D) and time (evening and morning) revealed significant microstate class × time interaction for duration (F(44) = 5.571, p = 0.002), coverage (F(44) = 6.833, p = 0.001), and occurrence (F(44) = 5.715, p = 0.002). Post hoc comparisons showed significant effects for microstate C duration (padj = 0.048, Cohen's d = -0.389), coverage (padj = 0.002, Cohen's d = -0.580), and occurrence (padj = 0.002, Cohen's d = -0.606). Topographic analyses revealed inverse correlations between SWD, but not SWA, and evening-to-morning changes in microstate C duration (r = -0.51, padj = 0.002), coverage (r = -0.45, padj = 0.006), and occurrence (r = -0.38, padj = 0.033).

CONCLUSIONS

Microstate characteristics showed significant evening-to-morning changes associated with, and possibly regulated by, sleep slow waves. These findings suggest that future microstate studies should control for time of day and sleep effects.

The influence of sleep disorders on perioperative neurocognitive disorders among the elderly: A narrative review

This review comprehensively assesses the epidemiology, interaction, and impact on patient outcomes of perioperative sleep disorders (SD) and perioperative neurocognitive disorders (PND) in the elderly. The incidence of SD and PND during the perioperative period in older adults is alarmingly high, with SD significantly contributing to the occurrence of postoperative delirium. However, the clinical evidence linking SD to PND remains insufficient, despite substantial preclinical data. Therefore, this study focuses on the underlying mechanisms between SD and PND, underscoring that potential mechanisms driving SD-induced PND include uncontrolled central nervous inflammation, blood-brain barrier disruption, circadian rhythm disturbances, glial cell dysfunction, neuronal and synaptic abnormalities, impaired central metabolic waste clearance, gut microbiome dysbiosis, hippocampal oxidative stress, and altered brain network connectivity. Additionally, the review also evaluates the effectiveness of various sleep interventions, both pharmacological and nonpharmacological, in mitigating PND. Strategies such as earplugs, eye masks, restoring circadian rhythms, physical exercise, noninvasive brain stimulation, dexmedetomidine, and melatonin receptor agonists have shown efficacy in reducing PND incidence. The impact of other sleep-improvement drugs (e.g., orexin receptor antagonists) and methods (e.g., cognitive-behavioral therapy for insomnia) on PND is still unclear. However, certain drugs used for treating SD (e.g., antidepressants and first-generation antihistamines) may potentially aggravate PND. By providing valuable insights and references, this review aimed to enhance the understanding and management of PND in older adults based on SD.

The Effect of Sleep Deprivation on Brain Fingerprint Stability: A Magnetoencephalography Validation Study

This study examined the stability of the functional connectome (FC) over time using fingerprint analysis in healthy subjects. Additionally, it investigated how a specific stressor, namely sleep deprivation, affects individuals' differentiation. To this aim, 23 healthy young adults underwent magnetoencephalography (MEG) recording at three equally spaced time points within 24 h: 9 a.m., 9 p.m., and 9 a.m. of the following day after a night of sleep deprivation. The findings indicate that the differentiation was stable from morning to evening in all frequency bands, except in the delta band. However, after a night of sleep deprivation, the stability of the FCs was reduced. Consistent with this observation, the reduced differentiation following sleep deprivation was found to be negatively correlated with the effort perceived by participants in completing the cognitive task during sleep deprivation. This correlation suggests that individuals with less stable connectomes following sleep deprivation experienced greater difficulty in performing cognitive tasks, reflecting increased effort.

A Study of Brain Function Characteristics of Service Members at High Risk for Accidents in the Military

Military accidents are often associated with stress and depressive psychological conditions among soldiers, and they often fail to adapt to military life. Therefore, this study analyzes whether there are differences in EEG and pulse wave indices between general soldiers and three groups of soldiers who have not adapted to military life and are at risk of accidents. Data collection was carried out using a questionnaire and a device that can measure EEG and pulse waves, and data analysis was performed using SPSS. The results showed that the concentration level and brain activity indices were higher in the general soldiers and the soldiers in the first stage of accident risk. The body stress index was higher for each stage of accident risk, and the physical vitality index was higher for general soldiers. Therefore, it can be seen that soldiers who have not adapted to military life and are at risk of accidents have somewhat lower concentration and brain activity than general soldiers, and have symptoms of stress and lethargy. The results of this study will contribute to reducing human accidents through EEG and pulse wave measurements not only in the military but also in occupations with a high risk of accidents such as construction.