Resting-State EEG Reveals Abnormal Microstate Characteristics of Depression with Insomnia.

Previous research revealed various aspects of resting-state EEG for depression and insomnia. However, the EEG characteristics of depressed subjects with insomnia are rarely studied, especially EEG microstates that capture the dynamic activities of the large-scale brain network. To fill these research gaps, the present study collected resting-state EEG data from 32 subclinical depression subjects with insomnia (SDI), 31 subclinical depression subjects without insomnia (SD), and 32 healthy controls (HCs). Four topographic maps were generated from clean EEG data after clustering and rearrangement. Temporal characteristics were obtained for statistical analysis, including cross-group variance analysis (ANOVA) and intra-group correlation analysis. In our study, the global clustering of all individuals in the EEG microstate analysis revealed the four previously discovered categories of microstates (A, B, C, and D). The occurrence of microstate B was lower in SDI than in SD and HC subjects. The correlation analysis showed that the total Pittsburgh Sleep Quality Index (PSQI) score negatively correlated with the occurrence of microstate C in SDI (r = - 0.415, p < 0.05). Conversely, there was a positive correlation between Self-rating Depression Scale (SDS) scores and the duration of microstate C in SD (r = 0.359, p < 0.05). These results indicate that microstates reflect altered large-scale brain network dynamics in subclinical populations. Abnormalities in the visual network corresponding to microstate B are an electrophysiological characteristic of subclinical individuals with symptoms of depressive insomnia. Further investigation is needed for microstate changes related to high arousal and emotional problems in people suffering from depression and insomnia.

Being bold wisely: neural substrates underlying ability to exploit risk.

Nothing ventured, nothing gained. To succeed one must take risks, and more importantly, take risks wisely, which depends on individual ability to exploit risk. Here, we explore neural substrates for the ability to exploit risk by using voxel-based morphometry (VBM). First, we carried out structural magnetic resonance imaging and measured individual risk-taking propensity and corresponding earnings by administrating the Balloon Analogue Risk Task in 1,389 participants. Behavior analysis revealed an inverted-U-shaped relation between risk-taking propensity and earnings, that earnings initially increased and then decreased as risk-taking propensity increased. Then individual ability to exploit risk was estimated by calculating the difference between individual actual earnings and the average earnings of the group at the same level of risk-taking propensity. VBM analysis revealed that individual ability to exploit risk was positively correlated with the gray matter volumes of three clusters located in the right orbitofrontal cortex, left dorsolateral prefrontal cortex (dlPFC), and right dlPFC, respectively. These findings highlight the neural substrates for the ability to exploit risk and implicate that precise valuation, adaptive learning, and self-control may underpin the ability to exploit risk, which expand our understanding of the ability to exploit risk and its neural substrates.

Risk of insomnia during COVID-19: effects of depression and brain functional connectivity.

Normal sleepers may be at risk for insomnia during COVID-19. Identifying psychological factors and neural markers that predict their insomnia risk, as well as investigating possible courses of insomnia development, could lead to more precise targeted interventions for insomnia during similar public health emergencies. Insomnia severity index of 306 participants before and during COVID-19 were employed to determine the development of insomnia, while pre-COVID-19 psychometric and resting-state fMRI data were used to explore corresponding psychological and neural markers of insomnia development. Normal sleepers as a group reported a significant increase in insomnia symptoms after COVID-19 outbreak (F = 4.618, P = 0.0102, df = 2, 609.9). Depression was found to significantly contribute to worse insomnia (ß = 0.066, P = 0.024). Subsequent analysis found that functional connectivity between the precentral gyrus and middle/inferior temporal gyrus mediated the association between pre-COVID-19 depression and insomnia symptoms during COVID-19. Cluster analysis identified that postoutbreak insomnia symptoms followed 3 courses (lessened, slightly worsened, and developed into mild insomnia), and pre-COVID-19 depression symptoms and functional connectivities predicted these courses. Timely identification and treatment of at-risk individuals may help avoid the development of insomnia in the face of future health-care emergencies, such as those arising from COVID-19 variants.

Multivariate resting-state functional connectomes predict and characterize obesity phenotypes.

The univariate obesity-brain associations have been extensively explored, while little is known about the multivariate associations between obesity and resting-state functional connectivity. We therefore utilized machine learning and resting-state functional connectivity to develop and validate predictive models of 4 obesity phenotypes (i.e. body fat percentage, body mass index, waist circumference, and waist-height ratio) in 3 large neuroimaging datasets (n = 2,992). Preliminary evidence suggested that the resting-state functional connectomes effectively predicted obesity/weight status defined by each obesity phenotype with good generalizability to longitudinal and independent datasets. However, the differences between resting-state functional connectivity patterns characterizing different obesity phenotypes indicated that the obesity-brain associations varied according to the type of measure of obesity. The shared structure among resting-state functional connectivity patterns revealed reproducible neuroimaging biomarkers of obesity, primarily comprising the connectomes within the visual cortex and between the visual cortex and inferior parietal lobule, visual cortex and orbital gyrus, and amygdala and orbital gyrus, which further suggested that the dysfunctions in the perception, attention and value encoding of visual information (e.g. visual food cues) and abnormalities in the reward circuit may act as crucial neurobiological bases of obesity. The recruitment of multiple obesity phenotypes is indispensable in future studies seeking reproducible obesity-brain associations.

Cultural Dimensions Moderate the Association between Loneliness and Mental Health during Adolescence and Younger Adulthood: A Systematic Review and Meta-Analysis.

Cultural factors, such as country or continent, influence the relationship between loneliness and mental health. However, less is known about how cultural dimensions moderate this relationship during adolescence and younger adulthood, even if these dimensions manifest as country or continent differences. This study aims to examine the potential influence of Hofstede's cultural dimensions on this relationship using a three-level meta-analysis approach. A total of 292 studies with 291,946 participants aged 10 to 24 were included in this study. The results indicate that cultural dimensions, such as individualism vs. collectivism, indulgence vs. restraint, power distance, and long-term vs. short-term orientation, moderated the associations between loneliness and social anxiety, stress, Internet overuse, and negative affect. The association between loneliness and mental health was not moderated by cultural dimensions, such as masculinity and uncertainty avoidance. These findings suggest that culture's influence on the association between loneliness and mental health is based on a domain-specific mechanism.

Single-cell sequencing reveals lung cell fate evolution initiated by smoking to explore gene predictions of correlative diseases.

Continuous smoking leads to adaptive regulation and physiological changes in lung tissue and cells, and is an inductive factor for many diseases, making smokers face the risk of malignant and nonmalignant diseases. The impact of research in this area is getting more and more in-depth, but the stimulant effect, mechanism of action and response mechanism of the main cells in the lungs caused by smoke components have not yet been fully elucidated, and the early diagnosis and identification of various diseases induced by smoke toxins have not yet formed a systematic relationship method. In this study, single-cell transcriptome data were generated from three lung samples of smokers and nonsmokers through scRNA-seq technology, revealing the influence of smoking on lung tissue and cells and the changes in immune response. The results show that: through UMAP cell clustering, 16 intermediate cell states of 23 cell clusters of the four main cell types in the lung are revealed, the differences of the main cell groups between smokers and nonsmokers are explained, and the human lung cells are clarified. Components and their marker genes, screen for new marker genes that can be used in the evolution of intermediate-state cells, and at the same time, the analysis of lung cell subgroups reveals the changes in the intermediate state of cells under smoke stimulation, forming a subtype intermediate state cell map. Pseudo-time ordering analysis, to determine the pattern of dynamic processes experienced by cells, differential expression analysis of different branch cells, to clarify the expression rules of cells at different positions, to clarify the evolution process of the intermediate state of cells, and to clarify the response of lung tissue and cells to smoke components mechanism. The development of this study provides new diagnosis and treatment ideas for early disease detection, identification, disease prevention and treatment of patients with smoking-related diseases, and lays a theoretical foundation based on cell and molecular regulation.

Anterior Cingulate Cortex Signals the Need to Control Intrusive Thoughts during Motivated Forgetting.

How do people limit awareness of unwanted memories? When such memories intrude, a control process engages the right DLPFC (rDLPFC) to inhibit hippocampal activity and stop retrieval. It remains unknown how the need for control is detected, and whether control operates proactively to prevent unwelcome memories from being retrieved, or responds reactively, to counteract intrusions. We hypothesized that dorsal ACC (dACC) detects the emergence of an unwanted trace in awareness and transmits the need for inhibitory control to rDLPFC. During a memory suppression task, we measured in humans (both sexes) trial-by-trial variations in the theta power and N2 amplitude of dACC, two EEG markers that are thought to reflect the need for control. With simultaneous EEG-fMRI recordings, we tracked interactions among dACC, rDLPFC, and hippocampus during suppression. We found a clear role of dACC in detecting the need for memory control and upregulating prefrontal inhibition. Importantly, we identified distinct early (300-450 ms) and late (500-700 ms) dACC contributions, suggesting both proactive control before recollection and reactive control in response to intrusions. Stronger early activity was associated with reduced hippocampal activity and diminished BOLD signal in dACC and rDLPFC, suggesting that preempting retrieval reduced overall control demands. In the later window, dACC activity was larger, and effective connectivity analyses revealed robust communication from dACC to rDLPFC and from rDLPFC to hippocampus, which are tied to successful forgetting. Together, our findings support a model in which dACC detects the emergence of unwanted content, triggering top-down inhibitory control, and in which rDLPFC countermands intruding thoughts that penetrate awareness.SIGNIFICANCE STATEMENT Preventing unwanted memories from coming to mind is an adaptive ability of humans. This ability relies on inhibitory control processes in the prefrontal cortex to modulate hippocampal retrieval processes. How and when reminders to unwelcome memories come to trigger prefrontal control mechanisms remains unknown. Here we acquired neuroimaging data with both high spatial and temporal resolution as participants suppressed specific memories. We found that the anterior cingulate cortex detects the need for memory control, responding both proactively to early warning signals about unwelcome content and reactively to intrusive thoughts themselves. When unwanted traces emerge in awareness, anterior cingulate communicates with prefrontal cortex and triggers top-down inhibitory control over the hippocampus through specific neural oscillatory networks.

Connectome-based prediction of eating disorder-associated symptomatology.

BACKGROUND: Despite increasing knowledge on the neuroimaging patterns of eating disorder (ED) symptoms in non-clinical populations, studies using whole-brain machine learning to identify connectome-based neuromarkers of ED symptomatology are absent. This study examined the association of connectivity within and between large-scale functional networks with specific symptomatic behaviors and cognitions using connectome-based predictive modeling (CPM). METHODS: CPM with ten-fold cross-validation was carried out to probe functional networks that were predictive of ED-associated symptomatology, including body image concerns, binge eating, and compensatory behaviors, within the discovery sample of 660 participants. The predictive ability of the identified networks was validated using an independent sample of 821 participants. RESULTS: The connectivity predictive of body image concerns was identified within and between networks implicated in cognitive control (frontoparietal and medial frontal), reward sensitivity (subcortical), and visual perception (visual). Crucially, the set of connections in the positive network related to body image concerns identified in one sample was generalized to predict body image concerns in an independent sample, suggesting the replicability of this effect. CONCLUSIONS: These findings point to the feasibility of using the functional connectome to predict ED symptomatology in the general population and provide the first evidence that functional interplay among distributed networks predicts body shape/weight concerns.

The Relation Between the Power of Prefrontal Gamma in Rest-state EEG Under Normal Sleep and Reactive Aggression Behaviour After Sleep Deprivation.

Whether sleep deprivation affects aggressive behaviour is still under debate. The present study examined the influence of individual differences in reactive aggressive behaviour after sleep deprivation and preliminarily explored an electrophysiological marker to identify individuals with more aggressive behaviour after sleep deprivation. Thirty participants performed the Taylor Aggression Paradigm task under two sleep conditions: normal sleep (NS, one night of regular sleep) and total sleep deprivation (SD, 30 h of wakefulness), and 5-minute resting-state electroencephalogram (EEG) acquisition was completed under the NS condition. The results found that although sleep deprivation did not have significant effects on aggressive behaviour in the overall analysis, the participants can be classified as an increased group (n = 16) and a no-increased group (n = 14) by different changes in the two sleep conditions (SD-NS). In addition, prefrontal gamma (γ) power was significantly lower in the increased group than in the no-increased group, which may reflect the difference in ability on inhibition. Furthermore, more critical is that γ power was significantly negatively correlated with change in the reactive aggressive behaviour. These results indicate that the effect of SD on reactive aggression varies between individuals, and prefrontal γ power may be an effective electrophysiological marker for identifying people at risk of aggressive behaviour after SD.

Mindfulness Training Improves Attention: Evidence from Behavioral and Event-related Potential Analyses.

Mindfulness meditation helps to improve attentional capacity. However, the neural correlates that indicate the mechanism through which mindfulness improves attention are unclear. To address this gap, we aimed to assess the effects of mindfulness training on sustained attentional capacity. Event-related potentials (ERPs) associated with the modified sustained attention response task (mSART) were used in this study. A total of 45 college students were randomly assigned to either the mindfulness group (n = 21) or the control group (n = 24). Participants in the mindfulness group received a three-week mindfulness training. The self-report results showed that the mindfulness group reported higher mindfulness scores (observing and non-judgment of inner experiences) after the training. The mindfulness group also scored lower on the state anxiety than the control group. Behavioral results also showed that self-caught mind wandering in the mindfulness group significantly decreased after the training, and the mindfulness group showed a faster response after the training. The ERP results showed that N2 amplitudes in the post-test were significantly greater than those in the pre-test in the mindfulness group. We did not find any interactions between group and time for P3. The findings suggest that mindfulness training can effectively improve sustained attentional capacity, as indicated by reduced mind wandering and increased N2 responses.

Reliability and validity of prognostic indicators for Guillain-Barré syndrome in children.

AIM: To explore the clinical characteristics and prognostic predictors of Guillain-Barré syndrome (GBS) in Chinese paediatric patients. METHOD: The clinical features of children with GBS hospitalized in the Children's Hospital of Chongqing Medical University were summarized retrospectively. The correlation between the Erasmus GBS Outcome Score (EGOS)/modified Erasmus GBS Outcome Score (mEGOS), GBS disability score (GDS)/modified Rankin Scale (MRS), Erasmus GBS Respiratory Insufficiency Score (EGRIS), and mechanical ventilation were evaluated. RESULTS: One hundred forty-two patients (86 males, 56 females; median 62.50 months [interquartile range 41.00-97.50]) with classic GBS were enrolled in the study. In the present GBS cohort, 134 (94.37%) patients could walk independently (GDS ≤2) and 121 (85.21%) could manage without assistance (MRS ≤2) at 6 months. Eighteen (12.68%) patients with GBS required mechanical ventilation. The performance of mEGOS on admission, mEGOS on day 7, and EGOS-predicted GDS outcome at 4 weeks, 3 months, and 6 months in the paediatric patients with GBS admitted within 2 weeks of disease onset and that of the MRS outcome were evaluated. The EGRIS in individuals who required mechanical ventilation was significantly higher than in patients without mechanical ventilation (median = 6 vs median = 3, p < 0.001). INTERPRETATION: In Chinese paediatric patients with GBS who were admitted 2 weeks after disease onset, the mEGOS and EGOS are validated indicators for the prediction of clinical outcomes 6 months after onset. EGRIS is helpful in predicting the implementation of mechanical ventilation in the acute phase. WHAT THIS PAPER ADDS: The Erasmus Guillain-Barré syndrome (GBS) Outcome Score (EGOS) and modified EGOS are reliable prognostic predictors in paediatric patients with GBS. The Erasmus GBS Respiratory Insufficiency Score (EGRIS) is an effective predictor of mechanical ventilation in paediatric patients with GBS. An EGRIS of ≥5 indicates a high risk of mechanical ventilation in the acute phase.

Predisposing Variations in Fear-Related Brain Networks Prospectively Predict Fearful Feelings during the 2019 Coronavirus (COVID-19) Pandemic.

The novel coronavirus (COVID-19) pandemic has led to a surge in mental distress and fear-related disorders, including posttraumatic stress disorder (PTSD). Fear-related disorders are characterized by dysregulations in fear and the associated neural pathways. In the present study, we examined whether individual variations in the fear neural connectome can predict fear-related symptoms during the COVID-19 pandemic. Using machine learning algorithms and back-propagation artificial neural network (BP-ANN) deep learning algorithms, we demonstrated that the intrinsic neural connectome before the COVID-19 pandemic could predict who would develop high fear-related symptoms at the peak of the COVID-19 pandemic in China (Accuracy rate = 75.00%, Sensitivity rate = 65.83%, Specificity rate = 84.17%). More importantly, prediction models could accurately predict the level of fear-related symptoms during the COVID-19 pandemic by using the prepandemic connectome state, in which the functional connectivity of lvmPFC (left ventromedial prefrontal cortex)-rdlPFC (right dorsolateral), rdACC (right dorsal anterior cingulate cortex)-left insula, lAMY (left amygdala)-lHip (left hippocampus) and lAMY-lsgACC (left subgenual cingulate cortex) was contributed to the robust prediction. The current study capitalized on prepandemic data of the neural connectome of fear to predict participants who would develop high fear-related symptoms in COVID-19 pandemic, suggesting that individual variations in the intrinsic organization of the fear circuits represent a neurofunctional marker that renders subjects vulnerable to experience high levels of fear during the COVID-19 pandemic.

Maladaptive changes in delay discounting in males during the COVID-19 pandemic: the predictive role of functional connectome.

The Coronavirus disease of 2019 (COVID-19) and measures to curb it created population-level changes in male-dominant impulsive and risky behaviors such as violent crimes and gambling. One possible explanation for this is that the pandemic has been stressful, and males, more so than females, tend to respond to stress by altering their focus on immediate versus delayed rewards, as reflected in their delay discounting rates. Delay discounting rates from healthy undergraduate students were collected twice during the pandemic. Discounting rates of males (n=190) but not of females (n=493) increased during the pandemic. Using machine learning, we show that prepandemic functional connectome predict increased discounting rates in males (n=88). Moreover, considering that delay discounting is associated with multiple psychiatric disorders, we found the same neural pattern that predicted increased discounting rates in this study, in secondary datasets of patients with major depression and schizophrenia. The findings point to sex-based differences in maladaptive delay discounting under real-world stress events, and to connectome-based neuromarkers of such effects. They can explain why there was a population-level increase in several impulsive and risky behaviors during the pandemic and point to intriguing questions about the shared underlying mechanisms of stress responses, psychiatric disorders and delay discounting.

[Research progress on the effects of childhood obstructive sleep apnea syndrome on cognition and brain functions].

Obstructive sleep apnea syndrome (OSAS), a prevalent sleep disorder in children, is characterized by recurring upper airway obstruction during sleep. OSAS in children can cause intermittent hypoxia and sleep fragmentation, ultimately affect brain development and further lead to cognitive impairment if lack of timely effective intervention. In recent years, magnetic resonance imaging (MRI) and electroencephalogram (EEG) have been employed to investigate brain structure and function abnormalities in children with OSAS. Previous studies have indicated that children with OSAS showed extensive gray and white matter damage, abnormal brain function in regions such as the frontal lobe and hippocampus, as well as a significant decline in general cognitive function and executive function. However, the existing studies mainly focused on the regional activity, and the mechanism of pediatric OSAS affecting brain networks remains unknown. Moreover, it's unclear whether the alterations in brain structure and function are associated with their cognitive impairment. In this review article, we proposed two future research directions: 1) future studies should utilize the multimodal neuroimaging techniques to reveal the alterations of brain networks organization underlying pediatric OSAS; 2) further investigation is necessary to explore the relationship between brain network alteration and cognitive dysfunction in children with OSAS. With these efforts, it will be promising to identify the neuroimaging biomarkers for monitoring the brain development of children with OSAS as well as aiding its clinical diagnosis, and ultimately develop more effective strategies for intervention, diagnosis, and treatment.

Spatio-temporal evolution and trend prediction of urban ecosystem service value based on CLUE-S and GM (1,1) compound model.

Ecosystem service value (ESV) is a significant indicator related to regional ecological well-being. Evaluating ESV premised on continuous time series land benefit data can provide an accurate reference for regional ecological civilization construction and sustainable development. Taking Shijiazhuang, the capital city of Hebei Province as an example, the study analyzed land use changes based on the land use data of the continuous time series from 2000 to 2020 and introduced a socio-economic adjustment factor and biomass factor adjustment factor to construct a dynamic assessment model of ecosystem service value. The spatiotemporal changes of the ecosystem service value in Shijiazhuang City were evaluated, and the dynamic prediction of the ecosystem service value was made using the CLUE-S model and the GM (1,1) model. (1) The changes in the overall ESV and spatial pattern in Shijiazhuang are strongly linked to the change in land use, and the contribution of cultivated land, woodland, and grassland to ecosystem service value exceeds 90%. (2) Between 2000 and 2020, the value of ecosystem services illustrated a dynamic change and gradually declined, with the total amount falling from 28.003 to 19.513 billion yuan. Among individual ecosystem services, the value of regulation services suffered the most serious loss. (3) CLUE-S and GM (1,1) perform well in the prediction of ESV. The prediction outcomes illustrate that the ecosystem service value of Shijiazhuang will continue to decline by 2025, and the ecosystem value will drop to 16.771 billion yuan. This research may offer a reference for the dynamic assessment of ESV of the continuous sequence and help to promote regional ecological protection and sustainable development.

Harmonized-Multinational qEEG norms (HarMNqEEG).

This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross-spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects" and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.

Impaired alpha rhythm in bad sleeper during nap: A high-density electroencephalogram study.

As the pace of life accelerates, poor sleep quality has become a common health problem. A wealth of sleep studies based on electroencephalogram (EEG) have found that poor sleep quality has decreased alpha rhythm during night-time sleep. However, whether the EEG rhythm during an afternoon nap has a similar impairment has seldom been investigated. By recruiting 35 subjects for a 90-min nap, we explored the relationship between sleep quality and the alpha rhythm, and the contribution of naps to subjective sleep experience. We found that the power of alpha rhythm in the occipital lobe negatively correlated with sleep quality. However, there was no correlation between the sleep quality at night and the sleep duration of the nap, nor the sleep quality and the proportion of each sleep period of a nap. Our present results indicated that the quality of night-time sleep did not affect the macrostructure of the nap. Interestingly, the negative correlation between the alpha rhythm of a nap and sleep quality at night continuously existed in Wake, N1 sleep, and N2 sleep, and had the most substantial value during N2 sleep of a nap. This implies that an impaired occipital lobe alpha rhythm is a robust biomarker for poor sleep. The research is of particular significance for developing electrical neural stimulation therapy to improve sleep quality, especially for the stimuli regions for selection and the time windows for implementation.

EEG microstate temporal Dynamics Predict depressive symptoms in College Students.

Previous studies on resting-state electroencephalographic responses in patients with depressive disorders have identified electroencephalogram (EEG) parameters as potential biomarkers for the early detection and diagnosis of depressive disorders. However, these studies did not investigate the relationship between resting-state EEG microstates and the early detection of depressive symptoms in preclinical individuals. To explore the possible association between resting-state EEG microstate temporal dynamics and depressive symptoms among college students, EEG microstate analysis was performed on eyes-closed resting-state EEG data for approximately 5 min from 34 undergraduates with high intensity of depressive symptoms and 34 age- and sex-matched controls with low intensity of depressive symptoms. Five microstate classes (A-E) were identified to best explain the datasets of both groups. Compared to controls, the mean duration, occurrence, and coverage of microstate class B increased significantly, whereas the occurrence and coverage of microstate classes D and E decreased significantly in individuals with high intensity of depressive symptoms. Additionally, the presence of microstate class B was positively correlated with participants' Beck Depression Inventory-II (BDI-II) scores, and the presence of microstate classes D and E were negatively correlated with their BDI-II scores. Further, individuals with high intensity of depressive symptoms had higher transition probabilities of A→B, B→A, B→C, B→D, and C→B, with lower transition probabilities of A→D, A→E, D→A, D→E, E→A, E→C, and E→D than controls. These results highlight resting-state EEG microstate temporal dynamics as potential biomarkers for the early detection and timely treatment of depression in college students.

Mortality Risk factors and SOX2 and mTOR expression in Patients with Esophageal Cancer.

Causes of mortality in EC patients are not confined to cancer-specific mortality but include various protein expressions of SOX2 and mTOR in Esophageal Cancer patients and their correlation with the clinical stage. Data about the risk factors and involvement of cancer-specific protein are still lacking. This study aimed to define the risk factors and association of SOX2 and mTOR expression in mortality in patients with EC. We conducted a retrospective cohort study to assess the risk factors for cancer-specific mortality and cardiovascular mortality in patients with esophageal cancer (EC). The expression rates of SOX2, as well as MTO, were checked in patients. The multivariate analysis revealed a high-risk EC mortality with age ≥ 65 years, black race, grade, stage, and sequence of treatment; radiation after surgery; radiation before and after surgery; Surgery both before and after radiation. While the cardiovascular mortality increased with age ≥ 65 years, adenocarcinoma type, grade, stage, and sequence of treatment. The expression rates of SOX2, as well as mTOR, were 75.5 percent and 86.8 percent in Esophageal Cancer, while were 10.7 percent and 7.5 percent in osteochondroma, respectively, which was statistically significant (P<0.05). Risk factors for cancer-specific mortality and cardiovascular mortality in EC patients include older age at diagnosis, male sex, non-married status, grade III of the tumor, the regional or distant spread of the tumor, no cancer-directed therapy. The expression levels of SOX2, mTOR, and the total survival time were related to the different stages. It shows an upward trend for the expression levels of mTOR and SOX2 in Esophageal Cancer tissues. The expression levels of SOX2 and mTOR are related to the clinical stage, metastasis, and prognosis.

Wake after Sleep Onset Time Moderated Age-related Emotional Memory Bias.

BACKGROUND: Numerous studies have shown that older adults have positive emotional bias. However, how sleep characteristics such as sleep continuity moderate this emotional bias in older adults is less understood. OBJECTIVES: The present study aimed to determine whether positive emotional memory bias is associated with sleep continuity in older adults. METHODS: We recruited 92 community-dwelling older adults for a cross-sectional study. Participants underwent 1 week of objective actigraphic sleep monitoring and completed a visuospatial associative memory task at baseline and 1 week later. RESULTS: Older adults exhibited better memory performance for positive pictures than for neutral and negative pictures at baseline, and this positive emotional bias was maintained for at least 1 week. Crucially, this effect was moderated by sleep continuity: Older adults with shorter wake after sleep onset times (WASO) exhibited this positive bias, whereas those with longer WASO did not. DISCUSSION: The present results are the first to demonstrate that positive emotional bias is moderated by sleep continuity in older adults. These findings have implications for emotion regulation in older adults and highlight the need for targeted interventions to increase their sleep continuity, which may help to improve emotional processing in this population.