Functional Connectome Prediction of Anxiety Related to the COVID-19 Pandemic

Threat- and reward-related brain circuitry, perceived stress, and anxiety in adolescents during the COVID-19 pandemic: a longitudinal investigation

The Coronavirus disease (COVID-19) pandemic led to heightened anxiety in adolescents. The basolateral amygdala (BLA) and the nucleus accumbens (NAcc) are implicated in response to stress and may contribute to anxiety. The role of threat- and reward-related circuitry in adolescent anxiety during the COVID-19 pandemic, however, is not clear. Ninety-nine adolescents underwent resting-state fMRI ∼1 year before the pandemic. Following shelter-in-place orders, adolescents reported their perceived stress and, 1 month later, their anxiety. Generalized multivariate analyses identified BLA and NAcc seed-based whole-brain functional connectivity maps with perceived stress. In the resulting significant clusters, we examined the association between seed-based connectivityand subsequent anxiety. Perceived stress was associated with bilateral BLA and NAcc connectivity across distributed clusters that included prefrontal, limbic, temporal, and cerebellar regions. Several NAcc connectivity clusters located in ventromedial prefrontal, parahippocampal, and temporal cortices were positively associated with anxiety; NAcc connectivity with the inferior frontal gyrus was negatively associated. BLA connectivity was not associated with anxiety. These results underscore the integrative role of the NAcc in responding to acute stressors and its relation to anxiety in adolescents. Elucidating the involvement of subcortical-cortical circuitry in adolescents' capacity to respond adaptively to environmental challenges can inform treatment for anxiety-related disorders.

Psychological resilience-related functional connectomes predict creative personality

Both psychological resilience and creativity are complex concepts that have positive effects on individual adaptation. Previous studies have shown overlaps between the key brain regions or brain functional networks related to psychological resilience and creativity. However, no direct experimental evidence has been provided to support the assumption that psychological resilience and creativity share a common brain basis. Therefore, the present study investigated the relationship between psychological resilience and creativity using neural imaging method with a machine learning approach. At the behavioral level, we found that psychological resilience was positively related to creative personality. Predictive analysis based on static functional connectivity (FC) and dynamic FC demonstrated that FCs related to psychological resilience could effectively predict an individual's creative personality score. Both the static FC and dynamic FC were mainly located in the default mode network. These results prove that psychological resilience and creativity share a common brain functional basis. These findings also provide insights into the possibility of promoting individual positive adaptation from negative events or situations in a creative way.

Protective factors for children with autism spectrum disorder during COVID-19-related strict lockdowns: a Shanghai autism early developmental cohort study

BACKGROUND

COVID-19 lockdowns increased the risk of mental health problems, especially for children with autism spectrum disorder (ASD). However, despite its importance, little is known about the protective factors for ASD children during the lockdowns.

METHODS

Based on the Shanghai Autism Early Developmental Cohort, 188 ASD children with two visits before and after the strict Omicron lockdown were included; 85 children were lockdown-free, while 52 and 51 children were under the longer and the shorter durations of strict lockdown, respectively. We tested the association of the lockdown group with the clinical improvement and also the modulation effects of parent/family-related factors on this association by linear regression/mixed-effect models. Within the social brain structures, we examined the voxel-wise interaction between the grey matter volume and the identified modulation effects.

RESULTS

Compared with the lockdown-free group, the ASD children experienced the longer duration of strict lockdown had less clinical improvement (β = 0.49, 95% confidence interval (CI) [0.19-0.79], p = 0.001) and this difference was greatest for social cognition (2.62 [0.94-4.30], p = 0.002). We found that this association was modulated by parental agreeableness in a protective way (-0.11 [-0.17 to -0.05], p = 0.002). This protective effect was enhanced in the ASD children with larger grey matter volumes in the brain's mentalizing network, including the temporal pole, the medial superior frontal gyrus, and the superior temporal gyrus.

CONCLUSIONS

This longitudinal neuroimaging cohort study identified that the parental agreeableness interacting with the ASD children's social brain development reduced the negative impact on clinical symptoms during the strict lockdown.

Connectome-based predictive modeling of Internet addiction symptomatology

Internet addiction symptomatology (IAS) is characterized by persistent and involuntary patterns of compulsive Internet use, leading to significant impairments in both physical and mental well-being. Here, a connectome-based predictive modeling approach was applied to decode IAS from whole-brain resting-state functional connectivity in healthy population. The findings showed that IAS could be predicted by the functional connectivity between prefrontal cortex with the cerebellum and limbic lobe and connections of the occipital lobe with the limbic lobe and insula lobe. The identified edges associated with IAS exhibit generalizability in predicting IAS within an independent sample. Furthermore, we found that the unique contributing network, which predicted IAS in contrast to the prediction networks of alcohol use disorder symptomatology (the range of symptoms and behaviors associated with alcohol use disorder), prominently comprised connections involving the occipital lobe and other lobes. The current data-driven approach provides the first evidence of the predictive brain features of IAS based on the organization of intrinsic brain networks, thus advancing our understanding of the neurobiological basis of Internet addiction disorder (IAD) susceptibility, and may have implications for the timely intervention of people potentially at risk of IAD.

Multivariate patterns of brain functional connectome associated with COVID-19-related negative affect symptoms

Severe mental health problems with the representation of negative affect symptoms (NAS) have been increasingly reported during the coronavirus disease 2019 (COVID-19) pandemic. This study aimed to explore the multivariate patterns of brain functional connectome predicting COVID-19-related NAS. This cohort study encompassed a group of university students to undergo neuroimaging scans before the pandemic, and we re-contacted participants for 1-year follow-up COVID-related NAS evaluations during the pandemic. Regularized canonical correlation analysis was used to identify connectome-based dimensions of NAS to compute pairs of canonical variates. The predictive ability of identified functional connectome to NAS dimensional scores was examined with a nested cross-validation. Two dimensions (i.e. mode stress and mode anxiety) were related to distinct patterns of brain functional connectome (r2 = 0.911, PFDR = 0.048; r2 = 0.901, PFDR = 0.037, respectively). Mode anxiety was characterized by high loadings in connectivity between affective network (AFN) and visual network (VN), while connectivity of the default mode network with dorsal attention network (DAN) were remarkably prominent in mode stress. Connectivity patterns within the DAN and between DAN and VN, ventral attention network, and AFN was common for both dimensions. The identified functional connectome can reliably predict mode stress (r = 0.37, MAE = 5.1, p < 0.001) and mode anxiety (r = 0.28, MAE = 5.4, p = 0.005) in the cross-validation. Our findings provide new insight into multivariate dimensions of COVID-related NAS, which may have implications for developing network-based biomarkers in psychological interventions for vulnerable individuals in the pandemic.

Students' mental health during the pandemic: results of the observational cross-sectional COVID-19 MEntal health inTernational for university Students (COMET-S) study

Introduction

The aim of the study was to search rates of depression and mental health in university students, during the COVID-19 pandemic.

Materials and methods

This is an observational cross-sectional study. A protocol gathering sociodemographic variables as well as depression, anxiety and suicidality and conspiracism was assembled, and data were collected anonymously and online from April 2020 through March 2021. The sample included 12,488 subjects from 11 countries, of whom 9,026 were females (72.2%; aged 21.11 ± 2.53), 3,329 males (26.65%; aged 21.61 ± 2.81) and 133 "non-binary gender" (1.06%; aged 21.02 ± 2.98). The analysis included chi-square tests, correlation analysis, ANCOVA, multiple forward stepwise linear regression analysis and Relative Risk ratios.

Results

Dysphoria was present in 15.66% and probable depression in 25.81% of the total study sample. More than half reported increase in anxiety and depression and 6.34% in suicidality, while lifestyle changes were significant. The model developed explained 18.4% of the development of depression. Believing in conspiracy theories manifested a complex effect. Close to 25% was believing that the vaccines include a chip and almost 40% suggested that facemask wearing could be a method of socio-political control. Conspiracism was related to current depression but not to history of mental disorders.

Discussion

The current study reports that students are at high risk for depression during the COVID-19 pandemic and identified specific risk factors. It also suggested a role of believing in conspiracy theories. Further research is important, as it is targeted intervention in students' groups that are vulnerable both concerning mental health and conspiracism.

Volume of the Dentate Gyrus/CA4 Hippocampal subfield mediates the interplay between sleep quality and depressive symptoms

Background

Emerging evidence increasingly suggests that poor sleep quality is associated with depressive symptoms. The hippocampus might play a crucial role in the interplay between sleep disturbance and depressive symptomatology, e.g., hippocampal atrophy is typically seen in both insomnia disorder and depression. Thus, examining the role of hippocampal volume in the interplay between poor sleep quality and depressive symptoms in large healthy populations is vital.

Methods

We investigated the association between self-reported sleep quality, depressive symptoms, and hippocampal total and subfields' volumes in 1603 healthy young adults from the Behavioral Brain Research Project. Mediation analysis explored the mediating role of hippocampal volumes between sleep quality and depressive symptoms.

Results

Self-reported sleep quality and depressive symptoms were positively correlated. In addition, it negatively related to three hippocampal subfields but not total hippocampal volume. In particular, hippocampal subfield DG and CA4 volumes mediated the interrelationship between poor sleep quality and depressive symptoms.

Conclusions

Our findings improved the current understanding of the relationship between sleep disturbance, depressive symptomatology, and hippocampal subfields in healthy populations. Considering the crucial role of DG in hippocampal neurogenesis, our results suggest that poor sleep quality may contribute to depression through a reduction of DG volume leading to impaired neurogenesis which is crucial for the regulation of mood.

Pre-COVID resting-state brain activity in the fusiform gyrus prospectively predicts social anxiety alterations during the pandemic

BACKGROUND

Social anxiety (SA) has been linked to the coronavirus disease 2019 (COVID-19) pandemic, but the neurobiopsychological mechanisms underlying this relationship remain unclear. This study aimed to elucidate the neurofunctional markers for COVID-induced SA development and the potential role of COVID-related posttraumatic stress symptoms (PTSS) in the brain-SA alterations link.

METHODS

Before the COVID-19 pandemic (T1), 100 general college students underwent resting-state magnetic resonance imaging and behavioral tests. During the period of community-level outbreaks (T2), these students were re-contacted to undergo follow-up behavioral assessments.

RESULTS

Whole-brain correlation and prediction analyses found that pre-pandemic spontaneous neural activity (measured by fractional amplitude of low-frequency fluctuations) in the right fusiform gyrus (FG) was positively correlated to SA alterations (T2 - T1). Mediation analyses revealed that COVID-specific PTSS mediated the effects of right FG on SA alterations.

LIMITATIONS

The results should be interpreted carefully because only one-session neuroimaging data in a sample of normal adults were included.

CONCLUSIONS

The results provide evidence for neurofunctional markers of COVID-induced SA and may help develop targeted brain-based interventions that reduce SA.

The double-edged sword of the hippocampus-ventromedial prefrontal cortex resting-state connectivity in stress susceptibility and resilience: A prospective study

The hippocampus has long been considered a pivotal region implicated in both stress susceptibility and resilience. A wealth of evidence from animal and human studies underscores the significance of hippocampal functional connectivity with the ventromedial prefrontal cortex (vmPFC) in these stress-related processes. However, there remains a scarcity of research that explores and contrasts the roles of hippocampus-vmPFC connectivity in stress susceptibility and resilience when facing a real-life traumatic event from a prospective standpoint. In the present study, we investigated the contributions of undirected and directed connectivity between the hippocampus and vmPFC to stress susceptibility and resilience within the context of the COVID-19 pandemic. Our findings revealed that the left hippocampus-left vmPFC connectivity prior to the pandemic exhibited a negative correlation with both stress susceptibility and resilience. Specifically, individuals with stronger left hippocampus-left vmPFC connectivity reported experiencing fewer stress-related feelings during the outbreak period of the epidemic but displayed lower levels of stress resilience five months later. Our application of spectral dynamic causal modeling unveiled an additional inhibitory connectivity pathway from the left hippocampus to the left vmPFC in the context of stress susceptibility, which was notably absent in stress resilience. Furthermore, we observed a noteworthy positive association between self-inhibition of the vmPFC and stress susceptibility, with this effect proving substantial enough to predict an individual's susceptibility to stress; conversely, these patterns did not manifest in the realm of stress resilience. These findings enrich our comprehension of stress susceptibility and stress resilience and might have implications for innovative approaches to managing stress-related disorders.

Pre-COVID brain network topology prospectively predicts social anxiety alterations during the COVID-19 pandemic

Background

Social anxiety (SA) is a negative emotional response that can lead to mental health issues, which some have experienced during the coronavirus disease 2019 (COVID-19) pandemic. Little attention has been given to the neurobiological mechanisms underlying inter-individual differences in SA alterations related to COVID-19. This study aims to identify neurofunctional markers of COVID-specific SA development.

Methods

110 healthy participants underwent resting-state magnetic resonance imaging and behavioral tests before the pandemic (T1, October 2019 to January 2020) and completed follow-up behavioral measurements during the pandemic (T2, February to May 2020). We constructed individual functional networks and used graph theoretical analysis to estimate their global and nodal topological properties, then used Pearson correlation and partial least squares correlations examine their associations with COVID-specific SA alterations.

Results

In terms of global network parameters, SA alterations (T2-T1) were negatively related to pre-pandemic brain small-worldness and normalized clustering coefficient. In terms of nodal network parameters, SA alterations were positively linked to a pronounced degree centrality pattern, encompassing both the high-level cognitive networks (dorsal attention network, cingulo-opercular task control network, default mode network, memory retrieval network, fronto-parietal task control network, and subcortical network) and low-level perceptual networks (sensory/somatomotor network, auditory network, and visual network). These findings were robust after controlling for pre-pandemic general anxiety, other stressful life events, and family socioeconomic status, as well as by treating SA alterations as categorical variables.

Conclusions

The individual functional network associated with SA alterations showed a disrupted topological organization with a more random state, which may shed light on the neurobiological basis of COVID-related SA changes at the network level.

Connectome-based modeling reveals a resting-state functional network that mediates the relationship between social rejection and rumination

Background

Rumination impedes problem solving and is one of the most important factors in the onset and maintenance of multiple psychiatric disorders. The current study aims to investigate the impact of social rejection on rumination and explore the underlying neural mechanisms involved in this process.

Methods

We utilized psychological questionnaire and resting-state brain imaging data from a sample of 560 individuals. The predictive model for rumination scores was constructed using resting-state functional connectivity data through connectome-based predictive modeling. Additionally, a mediation analysis was conducted to investigate the mediating role of the prediction network in the relationship between social rejection and rumination.

Results

A positive correlation between social rejection and rumination was found. We obtained the prediction model of rumination and found that the strongest contributions came from the intra- and internetwork connectivity within the default mode network (DMN), dorsal attention network (DAN), frontoparietal control network (FPCN), and sensorimotor networks (SMN). Analysis of node strength revealed the significance of the supramarginal gyrus (SMG) and angular gyrus (AG) as key nodes in the prediction model. In addition, mediation analysis showed that the strength of the prediction network mediated the relationship between social rejection and rumination.

Conclusion

The findings highlight the crucial role of functional connections among the DMN, DAN, FPCN, and SMN in linking social rejection and rumination, particular in brain regions implicated in social cognition and emotion, namely the SMG and AG regions. These results enhance our understanding of the consequences of social rejection and provide insights for novel intervention strategies targeting rumination.

Neural Representation of Collective Self-esteem in Resting-state Functional Connectivity and its Validation in Task-dependent Modality

INTRODUCTION

Collective self-esteem (CSE) is an important personality variable, defined as self-worth derived from membership in social groups. A study explored the neural basis of CSE using a task-based functional magnetic resonance imaging (fMRI) paradigm; however, task-independent neural basis of CSE remains to be explored, and whether the CSE neural basis of resting-state fMRI is consistent with that of task-based fMRI is unclear.

METHODS

We built support vector regression (SVR) models to predict CSE scores using topological metrics measured in the resting-state functional connectivity network (RSFC) as features. Then, to test the reliability of the SVR analysis, the activation pattern of the identified brain regions from SVR analysis was used as features to distinguish collective self-worth from other conditions by multivariate pattern classification in task-based fMRI dataset.

RESULTS

SVR analysis results showed that leverage centrality successfully decoded the individual differences in CSE. The ventromedial prefrontal cortex, anterior cingulate cortex, posterior cingulate gyrus, precuneus, orbitofrontal cortex, posterior insula, postcentral gyrus, inferior parietal lobule, temporoparietal junction, and inferior frontal gyrus, which are involved in self-referential processing, affective processing, and social cognition networks, participated in this prediction. Multivariate pattern classification analysis found that the activation pattern of the identified regions from the SVR analysis successfully distinguished collective self-worth from relational self-worth, personal self-worth and semantic control.

CONCLUSION

Our findings revealed CSE neural basis in the whole-brain RSFC network, and established the concordance between leverage centrality and the activation pattern (evoked during collective self-worth task) of the identified regions in terms of representing CSE.

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.

Karakatsoulis G, Abraham S, Adorjan K, Ahmed HU, Alarcón RD, Arai K, Auwal SS, Bobes J, Bobes-Bascaran T, Bourgin-Duchesnay J, Bredicean CA, Bukelskis L, Burkadze A, Cabrera Abud II, Castilla-Puentes R, Cetkovich M, Colon-Rivera H, Corral R, Cortez-Vergara C, Crepin P, de Berardis D, Zamora Delgado S, de Lucena D, de Sousa A, di Stefano R, Dodd S, Elek LP, Elissa A, Erdelyi-Hamza B, Erzin G, Etchevers MJ, Falkai P, Farcas A, Fedotov I, Filatova V, Fountoulakis NK, Frankova I, Franza F, Frias P, Galako T, Garay CJ, Garcia-Álvarez L, García-Portilla P, Gonda X, Gondek TM, Morera González D, Gould H, Grandinetti P, Grau A, Groudeva V, Hagin M, Harada T, Hasan TM, Azreen Hashim N, Hilbig J, Hossain S, Iakimova R, Ibrahim M, Iftene F, Ignatenko Y, Irarrazaval M, Ismail Z, Ismayilova J, Jacobs A, Jakovljević M, Jakšić N, Javed A, Yilmaz Kafali H, Karia S, Kazakova O, Khalifa D, Khaustova O, Koh S, Kopishinskaia S, Kosenko K, Koupidis SA, Kovacs I, Kulig B, Lalljee A, Liewig J, Majid A, Malashonkova E, Malik K, Iqbal Malik N, Mammadzada G, Mandalia B, Marazziti D, Marčinko D, Martinez S, Matiekus E, Mejia G, Memon RS, Meza Martínez XE, Mickevičiūtė D, Milev R, Mohammed M, Molina-López A, Morozov P, Muhammad NS, Mustač F, Naor MS, Nassieb A, Navickas A, Okasha T, Pandova M, Panfil AL, Panteleeva L, Papava I, Patsali ME, Pavlichenko A, Pejuskovic B, Pinto da Costa M, Popkov M, Popovic D, Raduan NJN, Vargas Ramírez F, Rancans E, Razali S, Rebok F, Rewekant A, Reyes Flores EN, Rivera-Encinas MT, Saiz PA, Sánchez de Carmona M, Saucedo Martínez D, Saw JA, Saygili G, Schneidereit P, Shah B, Shirasaka T, Silagadze K, Sitanggang S, Skugarevsky O, Spikina A, Mahalingappa SS, Stoyanova M, Szczegielniak A, Tamasan SC, Tavormina G, Tavormina MGM, Theodorakis PN, Tohen M, Tsapakis EM, Tukhvatullina D, Ullah I, Vaidya R, Vega-Dienstmaier JM, Vrublevska J, Vukovic O, Vysotska O, Widiasih N, Yashikhina A, Prezerakos PE, Berk M, Levaj S, Smirnova D. Results of the COVID-19 mental health international for the health professionals (COMET-HP) study: depression, suicidal tendencies and conspiracism

INTRODUCTION

The current study aimed to investigate the rates of anxiety, clinical depression, and suicidality and their changes in health professionals during the COVID-19 outbreak.

MATERIALS AND METHODS

The data came from the larger COMET-G study. The study sample includes 12,792 health professionals from 40 countries (62.40% women aged 39.76 ± 11.70; 36.81% men aged 35.91 ± 11.00 and 0.78% non-binary gender aged 35.15 ± 13.03). Distress and clinical depression were identified with the use of a previously developed cut-off and algorithm, respectively.

STATISTICAL ANALYSIS

Descriptive statistics were calculated. Chi-square tests, multiple forward stepwise linear regression analyses, and Factorial Analysis of Variance (ANOVA) tested relations among variables.

RESULTS

Clinical depression was detected in 13.16% with male doctors and 'non-binary genders' having the lowest rates (7.89 and 5.88% respectively) and 'non-binary gender' nurses and administrative staff had the highest (37.50%); distress was present in 15.19%. A significant percentage reported a deterioration in mental state, family dynamics, and everyday lifestyle. Persons with a history of mental disorders had higher rates of current depression (24.64% vs. 9.62%; p < 0.0001). Suicidal tendencies were at least doubled in terms of RASS scores. Approximately one-third of participants were accepting (at least to a moderate degree) a non-bizarre conspiracy. The highest Relative Risk (RR) to develop clinical depression was associated with a history of Bipolar disorder (RR = 4.23).

CONCLUSIONS

The current study reported findings in health care professionals similar in magnitude and quality to those reported earlier in the general population although rates of clinical depression, suicidal tendencies, and adherence to conspiracy theories were much lower. However, the general model of factors interplay seems to be the same and this could be of practical utility since many of these factors are modifiable.

The challenges and prospects of brain-based prediction of behaviour

Relating individual brain patterns to behaviour is fundamental in system neuroscience. Recently, the predictive modelling approach has become increasingly popular, largely due to the recent availability of large open datasets and access to computational resources. This means that we can use machine learning models and interindividual differences at the brain level represented by neuroimaging features to predict interindividual differences in behavioural measures. By doing so, we could identify biomarkers and neural correlates in a data-driven fashion. Nevertheless, this budding field of neuroimaging-based predictive modelling is facing issues that may limit its potential applications. Here we review these existing challenges, as well as those that we anticipate as the field develops. We focus on the impacts of these challenges on brain-based predictions. We suggest potential solutions to address the resolvable challenges, while keeping in mind that some general and conceptual limitations may also underlie the predictive modelling approach.

Pre-COVID brain functional connectome features prospectively predict emergence of distress symptoms after onset of the COVID-19 pandemic

BACKGROUND

Persistent psychological distress associated with the coronavirus disease 2019 (COVID-19) pandemic has been well documented. This study aimed to identify pre-COVID brain functional connectome that predicts pandemic-related distress symptoms among young adults.

METHODS

Baseline neuroimaging studies and assessment of general distress using the Depression, Anxiety and Stress Scale were performed with 100 healthy individuals prior to wide recognition of the health risks associated with the emergence of COVID-19. They were recontacted for the Impact of Event Scale-Revised and the Posttraumatic Stress Disorder Checklist in the period of community-level outbreaks, and for follow-up distress evaluation again 1 year later. We employed the network-based statistic approach to identify connectome that predicted the increase of distress based on 136-region-parcellation with assigned network membership. Predictive performance of connectome features and causal relations were examined by cross-validation and mediation analyses.

RESULTS

The connectome features that predicted emergence of distress after COVID contained 70 neural connections. Most within-network connections were located in the default mode network (DMN), and affective network-DMN and dorsal attention network-DMN links largely constituted between-network pairs. The hippocampus emerged as the most critical hub region. Predictive models of the connectome remained robust in cross-validation. Mediation analyses demonstrated that COVID-related posttraumatic stress partially explained the correlation of connectome to the development of general distress.

CONCLUSIONS

Brain functional connectome may fingerprint individuals with vulnerability to psychological distress associated with the COVID pandemic. Individuals with brain neuromarkers may benefit from the corresponding interventions to reduce the risk or severity of distress related to fear of COVID-related challenges.

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.

Adolescent functional network connectivity prospectively predicts adult anxiety symptoms related to perceived COVID-19 economic adversity

BACKGROUND

Stressful events, such as the COVID-19 pandemic, are major contributors to anxiety and depression, but only a subset of individuals develop psychopathology. In a population-based sample (N = 174) with a high representation of marginalized individuals, this study examined adolescent functional network connectivity as a marker of susceptibility to anxiety and depression in the context of adverse experiences.

METHODS

Data-driven network-based subgroups were identified using an unsupervised community detection algorithm within functional neural connectivity. Neuroimaging data collected during emotion processing (age 15) were extracted from a priori regions of interest linked to anxiety and depression. Symptoms were self-reported at ages 15, 17, and 21 (during COVID-19). During COVID-19, participants reported on pandemic-related economic adversity. Differences across subgroup networks were first examined, then subgroup membership and subgroup-adversity interaction were tested to predict change in symptoms over time.

RESULTS

Two subgroups were identified: Subgroup A, characterized by relatively greater neural network variation (i.e., heterogeneity) and density with more connections involving the amygdala, subgenual cingulate, and ventral striatum; and the more homogenous Subgroup B, with more connections involving the insula and dorsal anterior cingulate. Accounting for initial symptoms, subgroup A individuals had greater increases in symptoms across time (β = .138, p = .042), and this result remained after adjusting for additional covariates (β = .194, p = .023). Furthermore, there was a subgroup-adversity interaction: compared with Subgroup B, Subgroup A reported greater anxiety during the pandemic in response to reported economic adversity (β = .307, p = .006), and this remained after accounting for initial symptoms and many covariates (β = .237, p = .021).

CONCLUSIONS

A subgrouping algorithm identified young adults who were susceptible to adversity using their personalized functional network profiles derived from a priori brain regions. These results highlight potential prospective neural signatures involving heterogeneous emotion networks that predict individuals at the greatest risk for anxiety when experiencing adverse events.

Individualized prediction of trait self-control from whole-brain functional connectivity

Self-control is a core psychological construct for human beings and it plays a crucial role in the adaptation to society and achievement of success and happiness for individuals. Although progress has been made in behavioral studies examining self-control, its neural mechanisms remain unclear. In this study, we employed a machine-learning approach-relevance vector regression (RVR) to explore the potential predictive power of intrinsic functional connections to trait self-control in a large sample (N = 390). We used resting-state functional MRI (fMRI) to explore whole-brain functional connectivity patterns characteristic of 390 healthy adults and to confirm the effectiveness of RVR in predicting individual trait self-control scores. A set of connections across multiple neural networks that significantly predicted individual differences were identified, including the classic control network (e.g., fronto-parietal network (FPN), salience network (SAL)), the sensorimotor network (Mot), and the medial frontal network (MF). Key nodes that contributed to the predictive model included the dorsolateral prefrontal cortex (dlPFC), middle frontal gyrus (MFG), anterior cingulate and paracingulate gyri, inferior temporal gyrus (ITG) that have been associated with trait self-control. Our findings further assert that self-control is a multidimensional construct rooted in the interactions between multiple neural networks.

Depression and Anxiety During the COVID-19 Pandemic: Epidemiology, Mechanism, and Treatment

The Coronavirus Disease 2019 (COVID-19) pandemic has had an adverse impact on the physical and mental health of the public worldwide. In addition to illness in patients with COVID-19, isolated people and the general population have experienced mental health problems due to social distancing policies, mandatory lockdown, and other psychosocial factors, and the prevalence of depression and anxiety significantly increased during the pandemic. The purpose of this review is to elucidate the epidemiology, contributing factors, and pathogenesis of depression and anxiety. during the pandemic. These findings indicate that physicians and psychiatrists should pay more attention to and identify those with a high risk for mental problems, such as females, younger people, unmarried people, and those with a low educational level. In addition, researchers should focus on identifying the neural and neuroimmune mechanisms involved in depression and anxiety, and assess the intestinal microbiome to identify effective biomarkers. We also provide an overview of various intervention methods, including pharmacological treatment, psychological therapy, and physiotherapy, to provide a reference for different populations to guide the development of optimized intervention methods.

Increased spontaneous activity of the superior frontal gyrus with reduced functional connectivity to visual attention areas and cerebellum in male smokers

Background

Chronic smokers have abnormal spontaneous regional activity and disrupted functional connectivity as revealed by previous neuroimaging studies. Combining different dimensions of resting-state functional indicators may help us learn more about the neuropathological mechanisms of smoking.

Methods

The amplitude of low frequency fluctuations (ALFF) of 86 male smokers and 56 male non-smokers were first calculated. Brain regions that displayed significant differences in ALFF between two groups were selected as seeds for further functional connectivity analysis. Besides, we examined correlations between brain areas with abnormal activity and smoking measurements.

Results

Increased ALFF in left superior frontal gyrus (SFG), left medial superior frontal gyrus (mSFG) and middle frontal gyrus (MFG) as well as decreased ALFF in right calcarine sulcus were observed in smokers compared with non-smokers. In the seed-based functional connectivity analysis, smokers showed attenuated functional connectivity with left SFG in left precuneus, left fusiform gyrus, left lingual gyrus, left cerebellum 4 5 and cerebellum 6 as well as lower functional connectivity with left mSGF in left fusiform gyrus, left lingual gyrus, left parahippocampal gyrus (PHG), left calcarine sulcus, left cerebellum 4 5, cerebellum 6 and cerebellum 8 (GRF corrected, Pvoxel &lt; 0.005, Pcluster&lt;0.05). Furthermore, attenuated functional connectivity with left mSGF in left lingual gyrus and PHG displayed a negative correlation with FTND scores (r = −0.308, p = 0.004; r = −0.326, p = 0.002 Bonferroni corrected).

Conclusion

Our findings of increased ALFF in SFG with reduced functional connectivity to visual attention areas and cerebellum subregions may shed new light on the pathophysiology of smoking.

Amygdala connectivity related to subsequent stress responses during the COVID-19 outbreak

Introduction

The amygdala plays an important role in stress responses and stress-related psychiatric disorders. It is possible that amygdala connectivity may be a neurobiological vulnerability marker for stress responses or stress-related psychiatric disorders and will be useful to precisely identify the vulnerable individuals before stress happens. However, little is known about the relationship between amygdala connectivity and subsequent stress responses. The current study investigated whether amygdala connectivity measured before experiencing stress is a predisposing neural feature of subsequent stress responses while individuals face an emergent and unexpected event like the COVID-19 outbreak.

Methods

Data collected before the COVID-19 pandemic from an established fMRI cohort who lived in the pandemic center in China (Hubei) during the COVID-19 outbreak were used to investigate the relationship between amygdala connectivity and stress responses during and after the pandemic in 2020. The amygdala connectivity was measured with resting-state functional connectivity (rsFC) and effective connectivity.

Results

We found the rsFC of the right amygdala with the dorsomedial prefrontal cortex (dmPFC) was negatively correlated with the stress responses at the first survey during the COVID-19 outbreak, and the rsFC between the right amygdala and bilateral superior frontal gyri (partially overlapped with the dmPFC) was correlated with SBSC at the second survey. Dynamic causal modeling suggested that the self-connection of the right amygdala was negatively correlated with stress responses during the pandemic.

Discussion

Our findings expand our understanding about the role of amygdala in stress responses and stress-related psychiatric disorders and suggest that amygdala connectivity is a predisposing neural feature of subsequent stress responses.

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 (β = 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.

Functional Brain Connectivity Prior to the COVID-19 Outbreak Moderates the Effects of Coping and Perceived Stress on Mental Health Changes: A First Year of COVID-19 Pandemic Follow-up Study

BACKGROUND

The COVID-19 pandemic provides a unique opportunity to investigate the psychological impact of a global major adverse situation. Our aim was to examine, in a longitudinal prospective study, the demographic, psychological, and neurobiological factors associated with interindividual differences in resilience to the mental health impact of the pandemic.

METHODS

We included 2023 healthy participants (age: 54.32 ± 7.18 years, 65.69% female) from the Barcelona Brain Health Initiative cohort. A linear mixed model was used to characterize the change in anxiety and depression symptoms based on data collected both pre-pandemic and during the pandemic. During the pandemic, psychological variables assessing individual differences in perceived stress and coping strategies were obtained. In addition, in a subsample (n = 433, age 53.02 ± 7.04 years, 46.88% female) with pre-pandemic resting-state functional magnetic resonance imaging available, the system segregation of networks was calculated. Multivariate linear models were fitted to test associations between COVID-19-related changes in mental health and demographics, psychological features, and brain network status.

RESULTS

The whole sample showed a general increase in anxiety and depressive symptoms after the pandemic onset, and both age and sex were independent predictors. Coping strategies attenuated the impact of perceived stress on mental health. The system segregation of the frontoparietal control and default mode networks were found to modulate the impact of perceived stress on mental health.

CONCLUSIONS

Preventive strategies targeting the promotion of mental health at the individual level during similar adverse events in the future should consider intervening on sociodemographic and psychological factors as well as their interplay with neurobiological substrates.

TTLL11 gene is associated with sustained attention performance and brain networks: A genome-wide association study of a healthy Chinese sample

Genetic studies on attention have mainly focused on children with attention-deficit/hyperactivity disorder (ADHD), so little systematic research has been conducted on genetic correlates of attention performance and their potential brain mechanisms among healthy individuals. The current study included a genome-wide association study (GWAS, N = 1145 healthy young adults) aimed to identify genes associated with sustained attention and an imaging genetics study (an independent sample of 483 healthy young adults) to examine any identified genes' influences on brain function. The GWAS found that TTLL11 showed genome-wide significant associations with sustained attention, with rs13298112 as the most significant SNP and the GG homozygotes showing more impulsive but also more focused responses than the A allele carriers. A retrospective examination of previously published ADHD GWAS results confirmed an un-reported, small but statistically significant effect of TTLL11 on ADHD. The imaging genetics study replicated this association and showed that the TTLL11 gene was associated with resting state activity and connectivity of the somatomoter network, and can be predicted by dorsal attention network connectivity. Specifically, the GG homozygotes showed lower brain activity, weaker brain network connectivity, and non-significant brain-attention association compared to the A allele carriers. Expression database showed that expression of this gene is enriched in the brain and that the G allele is associated with lower expression level than the A allele. These results suggest that TTLL11 may play a major role in healthy individuals' attention performance and may also contribute to the etiology of ADHD.

Counseling for Health: How Psychological Distance Influences Continuance Intention towards Mobile Medical Consultation

As mobile healthcare services entered the public sight with high frequency during the COVID-19 pandemic, patients are increasingly recognizing the effectiveness of mobile medical consultation (MMC). Earlier studies have investigated what influences continuance intention (CI) towards MMC, but few studies have scrutinized it from the perspective of patients' psychological distance. We formulated a framework to examine the psychological factors influencing CI towards MMC by integrating the information systems continuance model and psychological distance theory. The framework was validated using the partial least squares structural equation modeling (PLS-SEM) approach and data from 475 MMC users in China. The empirical results revealed that immediacy, telepresence, intimacy, and substitutability were significant predictors of CI, while satisfaction mediated these pathways. Pandemic-induced anxiety positively moderated the effect of immediacy on satisfaction and the effect of satisfaction on CI. Practical implementations for MMC healthcare practitioners, designers, and marketers are drawn.

Translating Big Data to Clinical Outcomes in Anxiety: Potential for Multimodal Integration

PURPOSE OF THE REVIEW

This review describes approaches to research on anxiety that attempt to link neural correlates to treatment response and novel therapies. The review emphasizes pediatric anxiety disorders since most anxiety disorders begin before adulthood.

RECENT FINDINGS

Recent literature illustrates how current treatments for anxiety manifest diverse relations with a range of neural markers. While some studies demonstrate post-treatment normalization of markers in anxious individuals, others find persistence of group differences. For other markers, which show no pretreatment association with anxiety, the markers nevertheless distinguish treatment-responders from non-responders. Heightened error related negativity represents the risk marker discussed in the most depth; however, limitations in measures related to error responding necessitate multimodal and big-data approaches. Single risk markers show limits as correlates of treatment response. Large-scale, multimodal data analyzed with predictive models may illuminate additional risk markers related to anxiety disorder treatment outcomes. Such work may identify novel targets and eventually guide improvements in treatment response/outcomes.

The effect of different degrees of lockdown and self-identified gender on anxiety, depression and suicidality during the COVID-19 pandemic: Data from the international COMET-G study

INTRODUCTION

During the COVID-19 pandemic various degrees of lockdown were applied by countries around the world. It is considered that such measures have an adverse effect on mental health but the relationship of measure intensity with the mental health effect has not been thoroughly studied. Here we report data from the larger COMET-G study pertaining to this question.

MATERIAL AND METHODS

During the COVID-19 pandemic, data were gathered with an online questionnaire from 55,589 participants from 40 countries (64.85% females aged 35.80 ± 13.61; 34.05% males aged 34.90±13.29 and 1.10% other aged 31.64±13.15). Anxiety was measured with the STAI, depression with the CES-D and suicidality with the RASS. Distress and probable depression were identified with the use of a previously developed cut-off and algorithm respectively.

STATISTICAL ANALYSIS

It included the calculation of Relative Risk (RR), Factorial ANOVA and Multiple backwards stepwise linear regression analysis RESULTS: Approximately two-thirds were currently living under significant restrictions due to lockdown. For both males and females the risk to develop clinical depression correlated significantly with each and every level of increasing lockdown degree (RR 1.72 and 1.90 respectively). The combined lockdown and psychiatric history increased RR to 6.88 The overall relationship of lockdown with severity of depression, though significant was small.

CONCLUSIONS

The current study is the first which reports an almost linear relationship between lockdown degree and effect in mental health. Our findings, support previous suggestions concerning the need for a proactive targeted intervention to protect mental health more specifically in vulnerable groups.

Prevalence and influencing factors of anxiety in medical students during the COVID-19 pandemic

Background and objective

The novel coronavirus disease (COVID-19) pandemic has caused a traumatic impact on the whole world in all aspects including physical health, economic condition, and mental health. Psychological problems are commonly neglected for their inconspicuous symptoms. Little is known about the medical students' psychological status during the COVID-19 pandemic. Our study aimed to investigate the prevalence and influencing factors of anxiety among medical students during the COVID-19 pandemic.

Methods

Two thousand and two medical students were investigated in this cross-sectional study. Zung's self-rating anxiety scale was used to evaluate their anxiety symptoms. A total of 1917 questionnaires were collected, and the response rate was 95.8%.

Results

There were 1735 (90.5%) complete and valid questionnaires. The average SAS standard score was 42.8 ± 11.0. The results indicated that the prevalence of anxiety in medical students was 25.9%. Less social support (OR = 1.4, 95%CI 1.2-1.7) is an independent risk factor of anxiety syndrome, while the female (OR = 0.6, 95%CI 0.5-0.8) is less likely to have anxiety syndrome.

Conclusion

The prevalence of anxiety in medical students is higher during the COVID-19 pandemic. Male students and students with less social support are more likely to have anxiety syndrome. Medical educators and students themselves should take serious steps to prevent, recognize and deal with the anxiety prevalence.

COVID-19 vicarious traumatization links functional connectome to general distress

As characterized by repeated exposure of others' trauma, vicarious traumatization is a common negative psychological reaction during the COVID-19 pandemic and plays a crucial role in the development of general mental distress. This study aims to identify functional connectome that encodes individual variations of pandemic-related vicarious traumatization and reveal the underlying brain-vicarious traumatization mechanism in predicting general distress. The eligible subjects were 105 general university students (60 females, aged from 19 to 27 years) undergoing brain MRI scanning and baseline behavioral tests (October 2019 to January 2020), whom were re-contacted for COVID-related vicarious traumatization measurement (February to April 2020) and follow-up general distress evaluation (March to April 2021). We applied a connectome-based predictive modeling (CPM) approach to identify the functional connectome supporting vicarious traumatization based on a 268-region-parcellation assigned to network memberships. The CPM analyses showed that only the negative network model stably predicted individuals' vicarious traumatization scores (q² = -0.18, MSE = 617, r _{[predicted, actual]} = 0.18, p = 0.024), with the contributing functional connectivity primarily distributed in the fronto-parietal, default mode, medial frontal, salience, and motor network. Furthermore, mediation analysis revealed that vicarious traumatization mediated the influence of brain functional connectome on general distress. Importantly, our results were independent of baseline family socioeconomic status, other stressful life events and general mental health as well as age, sex and head motion. Our study is the first to provide evidence for the functional neural markers of vicarious traumatization and reveal an underlying neuropsychological pathway to predict distress symptoms in which brain functional connectome affects general distress via vicarious traumatization.

Monitoring of COVID-19 pandemic-related psychopathology using machine learning

The COVID-19 pandemic is believed to have a major negative impact on global mental health due to the viral disease itself as well as the associated lockdowns, social distancing, isolation, fear, and increased uncertainty. Individuals with preexisting mental illness are likely to be particularly vulnerable to these conditions and may develop outright 'COVID-19-related psychopathology'. Here, we trained a machine learning model on structured and natural text data from electronic health records to identify COVID-19 pandemic-related psychopathology among patients receiving care in the Psychiatric Services of the Central Denmark Region. Subsequently, applying this model, we found that pandemic-related psychopathology covaries with the pandemic pressure over time. These findings may aid psychiatric services in their planning during the ongoing and future pandemics. Furthermore, the results are a testament to the potential of applying machine learning to data from electronic health records.

Functional connectivity of amygdala subregions predicts vulnerability to depression following the COVID-19 pandemic

BACKGROUND

The amygdala is vital in processing psychological stress and predicting vulnerability or resilience to stress-related disorders. This study aimed to build the link between functional magnetic resonance imaging data obtained before the stress event and the subsequent stress-related depressive symptoms.

METHODS

Neuroimaging data obtained before the coronavirus disease 2019 pandemic from 39 patients with major depressive disorder (MDD) and 61 health controls (HCs) were used in this study. The participants were divided retrospectively into four groups in accordance with the severity of depressive symptoms during the pandemic: remitted patients, non-remitted patients, depressed HCs (HCd) and non-depressed HCs (HCnd). Seed-based resting-state functional connectivity (rsFC) analyses of the amygdala and its subregions, including the centromedial (CM), the basolateral and the superficial (SF), were performed.

RESULTS

Vulnerability to depression was suggested by decreased rsFC between the left CM amygdala and the bilateral lingual gyrus in the HCd group compared with the HCnd group, and decreased rsFC of the left CM or right SF amygdala with the precuneus and the postcentral gyrus in the HCd group compared with patients with MDD. No evidence supported the rsFC of the amygdala or its subregions as a biomarker for the resilience of patients with MDD to stress under antidepressant treatment.

LIMITATIONS

Smaller sample size and no longitudinal neuroimaging data.

CONCLUSIONS

Our findings suggested that the rsFC of amygdala subregions may represent a neurobiological marker of vulnerability to depression following stress.

OUP accepted manuscript

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.

Results of the COVID-19 mental health international for the general population (COMET-G) study

INTRODUCTION

There are few published empirical data on the effects of COVID-19 on mental health, and until now, there is no large international study.

MATERIAL AND METHODS

During the COVID-19 pandemic, an online questionnaire gathered data from 55,589 participants from 40 countries (64.85% females aged 35.80 ± 13.61; 34.05% males aged 34.90±13.29 and 1.10% other aged 31.64±13.15). Distress and probable depression were identified with the use of a previously developed cut-off and algorithm respectively.

STATISTICAL ANALYSIS

Descriptive statistics were calculated. Chi-square tests, multiple forward stepwise linear regression analyses and Factorial Analysis of Variance (ANOVA) tested relations among variables.

RESULTS

Probable depression was detected in 17.80% and distress in 16.71%. A significant percentage reported a deterioration in mental state, family dynamics and everyday lifestyle. Persons with a history of mental disorders had higher rates of current depression (31.82% vs. 13.07%). At least half of participants were accepting (at least to a moderate degree) a non-bizarre conspiracy. The highest Relative Risk (RR) to develop depression was associated with history of Bipolar disorder and self-harm/attempts (RR = 5.88). Suicidality was not increased in persons without a history of any mental disorder. Based on these results a model was developed.

CONCLUSIONS

The final model revealed multiple vulnerabilities and an interplay leading from simple anxiety to probable depression and suicidality through distress. This could be of practical utility since many of these factors are modifiable. Future research and interventions should specifically focus on them.

Stronger functional network connectivity and social support buffer against negative affect during the COVID-19 outbreak and after the pandemic peak

Health and financial uncertainties, as well as enforced social distancing, during the COVID-19 pandemic have adversely affected the mental health of people. These impacts are expected to continue even after the pandemic, particularly for those who lack support from family and friends. The salience network (SN), default mode network (DMN), and frontoparietal network (FPN) function in an interconnected manner to support information processing and emotional regulation processes in stressful contexts. In this study, we examined whether functional connectivity of the SN, DMN, and FPN, measured using resting-state functional magnetic resonance imaging before the pandemic, is a neurobiological marker of negative affect (NA) during the COVID-19 pandemic and after its peak in a large sample (N = 496, 360 females); the moderating role of social support in the brain-NA association was also investigated. We found that participants reported an increase in NA during the pandemic compared to before the pandemic, and the NA did not decrease, even after the peak period. People with higher connectivity within the SN and between the SN and the other two networks reported less NA during and after the COVID-19 outbreak peak, and the buffer effect was stronger if their social support was greater. These findings suggest that the functional networks that are responsible for affective processing and executive functioning, as well as the social support from family and friends, play an important role in protecting against NA under stressful and uncontrollable situations.

The bright side and dark side of daydreaming predict creativity together through brain functional connectivity

Daydreaming and creativity have similar cognitive processes and neural basis. However, few empirical studies have examined the relationship between daydreaming and creativity using cognitive neuroscience methods. The present study explored the relationship between different types of daydreaming and creativity and their common neural basis. The behavioral results revealed that positive constructive daydreaming is positively related to creativity, while poor attentional control is negatively related to it. Machine learning framework was adopted to examine the predictive effect of daydreaming-related brain functional connectivity (FC) on creativity. The results demonstrated that task FCs related to positive constructive daydreaming and task FCs related to poor attentional control both predicted an individual's creativity score successfully. In addition, task FCs combining the positive constructive daydreaming and poor attentional control also had significant predictive effect on creativity score. Furthermore, predictive analysis based on resting-state FCs showed similar patterns. Both of the subscale-related FCs and combined FCs had significant predictive effect on creativity score. Further analysis showed the task and the resting-state FCs both mainly located in the default mode network, central executive network, salience network, and attention network. These results showed that daydreaming was closely related to creativity, as they shared common FC basis.

Chlorinated disinfection byproducts of diazepam perturb cell metabolism and induce behavioral toxicity in zebrafish larvae

Numerous byproducts resulting from chlorinated disinfection are constantly being generated during water treatment processes. The potential risks of these new emerging pollutions remain largely unknown. Here, we determined the risks of chlorinated disinfection byproducts of diazepam (DZP) in the cellular and zebrafish exposure experiments. The cytotoxicity of disinfection byproducts (MACB and MBCC) was greater than DZP in macrophage raw 264.7 cells at 10 mg/L. We further found that the effects of MBCC on the metabolism of glycine, serine, threonine and riboflavin were far greater than DZP by the targeted metabolomics methods. Moreover, MBCC significantly decreased the peak amplitude of neuronal action potential in primary embryonic rat (Spragu-Dawley SD) hippocampal neurons. We finally determined behavioral toxicity of DZP and byproducts in zebrafish larvae. MBCC significantly decreased the maximal swim-activity and peak duration of zebrafish after 72 h exposure. Altogether, these findings indicate the MBCC pose serious pressures on public health.

Post-traumatic stress symptoms in COVID-19 survivors: a self-report and brain imaging follow-up study

Previous coronavirus pandemics were associated elevated post-traumatic stress symptoms (PTSS), but the self-report and neurological basis of PTSS in patients who survived coronavirus disease 2019 (COVID-19) are largely unknown. We conducted a two-session study to record PTSS in the COVID-19 survivors discharged from hospitals for a short (i.e., about 3 months, Session 1) to a medium period (i.e., about 6 months, Session 2), as well as brain imaging data in Session 2. The control groups were non-COVID-19 locals. Session 1 was completed for 126 COVID-19 survivors and 126 controls. Session 2 was completed for 47 COVID-19 survivors and 43 controls. The total score of post-traumatic stress disorder (PTSD) checklist for DSM-5 (PCL-5) score was significantly higher in COVID-19 survivors compared with controls in both sessions. The PCL-5 score in COVID-19 survivors was positively correlated with the duration after discharge (r = 0.27, p = 0.003 for Session 1), and increased by 20% from Session 1 to Session 2 for the survivors who participated both sessions. The increase was positively correlated with individual’s test-retest duration (r = 0.46, p = 0.03). Brain structural volume and functional activity in bilateral hippocampus and amygdala were significantly larger in COVID-19 survivors compared with controls. However, the volumes of the left hippocampus and amygdala were negatively correlated with the PCL-5 score for the COVID-19 survivors. Our study suggests that COVID-19 survivors might face possible PTSS deteriorations, and highlights the importance of monitoring mental wellness of COVID-19 survivors.