The functional connectome predicts feeling of stress on regular days and during the COVID-19 pandemic

Insomnia and stress: the mediating roles of frontoparietal network

Insomnia is a widespread health problem among adults, and it impairs cognitive control and emotional regulation functions. Stress and insomnia are positively correlated, and their vicious cycle has been widely reported. In this study, we explore the neural biomarkers of insomnia from the perspective of whole-brain functional connectivity and investigate the neural mechanisms underlying the association between stress and insomnia. The current study was conducted on a cross-sectional sample (N = 430). First, we investigated the correlation between perceived stress and insomnia. Second, we applied connectome-based predictive modeling (CPM) to determine the neuromarkers of insomnia. Finally, we explored the neural basis underlying the association between perceived stress and insomnia. A significant positive correlation was found between perceived stress and insomnia in the present research. Results of CPM revealed the following as the neural substrates supporting insomnia: the emotion regulation circuit involving repetitive negative thinking and the cognitive control circuit involving attention control. According to further results from mediation analysis, the frontoparietal network supporting cognitive emotion regulation is an important neural mechanism that maintains the correlation between stress and insomnia. The present study offers a profound insight into the alterations of brain activity related to insomnia, and it further investigates the neural underpinnings of the robust association between stress and insomnia. This study also opens new avenues for neural interventions to alleviate stress-related insomnia.

Static and Temporal Dynamic in Functional Connectivity of Large-scale Brain Networks During Acute Stress Regulate Stress Resilience Differently: The Promotion Role of Trait Resilience

Stress resilience has been largely regarded as a process in which individuals actively cope with and recover from stress. Over the past decade, the emergence of large-scale brain networks has provided a new perspective for the study of the neural mechanisms of stress. However, the role of inter-network functional-connectivity (FC) and its temporal fluctuations in stress resilience is still unclear. To bridge this knowledge gap, seventy-seven participants (age, 17-22 years, 37 women) were recruited for a ScanSTRESS brain imaging study. A static perspective was initially adopted, using changes in FC that obtained from stress vs. control condition during the entire stress induction phase as a static indicator. Further, changes in FC between different stress runs were analyzed as an index of temporal dynamics. Stress resilience was gauged using salivary cortisol levels, while trait resilience was measured via behavioral-activation-system (BAS) sensitivity. Results found that, for the static index, enhanced FC between the salience-network (SN), default-mode-network (DMN) and limbic-network (LBN) during acute stress could negatively signal stress resilience. For the temporal dynamics index, FC among the dorsal-attention-network (DAN), central-executive-network (CEN) and visual-network (VN) decreased significantly during repeated stress induction. Moreover, the decline of FC positively signaled stress resilience, and this relationship only exist in people with high BAS. The current research elucidates the intricate neural underpinnings of stress resilience, offering insights into the adaptive mechanisms underlying effective stress responses.

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.

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 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.

Decoding individual differences in self-prioritization from the resting-state functional connectome

Although the self has traditionally been viewed as a higher-order mental function by most theoretical frameworks, recent research advocates a fundamental self hypothesis, viewing the self as a baseline function of the brain embedded within its spontaneous activities, which dynamically regulates cognitive processing and subsequently guides behavior. Understanding this fundamental self hypothesis can reveal where self-biased behaviors emerge and to what extent brain signals at rest can predict such biased behaviors. To test this hypothesis, we investigated the association between spontaneous neural connectivity and robust self-bias in a perceptual matching task using resting-state functional magnetic resonance imaging (fMRI) in 348 young participants. By decoding whole-brain connectivity patterns, the support vector regression model produced the best predictions of the magnitude of self-bias in behavior, which was evaluated via a nested cross-validation procedure. The out-of-sample generalizability was further authenticated using an external dataset of older adults. The functional connectivity results demonstrated that self-biased behavior was associated with distinct connections between the default mode, cognitive control, and salience networks. Consensus network and computational lesion analyses further revealed contributing regions distributed across six networks, extending to additional nodes, such as the thalamus, whose role in self-related processing remained unclear. These results provide evidence that self-biased behavior derives from spontaneous neural connectivity, supporting the fundamental self hypothesis. Thus, we propose an integrated neural network model of this fundamental self that synthesizes previous theoretical models and portrays the brain mechanisms by which the self emerges at rest internally and regulates responses to the external environment.

Psychological resilience mediates the protective role of default-mode network functional connectivity against COVID-19 vicarious traumatization

Vicarious traumatization (VT), a negative reaction to witnessing others' trauma, has been experienced by some people during the COVID-19 pandemic, and can lead to mental health problems. This study aimed to identify functional brain markers of COVID-specific VT and explore the psychological mechanism underlying the brain-VT link. One hundred healthy participants underwent resting-state functional magnetic resonance imaging before the pandemic (October 2019-January 2020) and completed VT measurement during the pandemic (February-April 2020). Whole-brain correlation analysis based on global functional connectivity density (FCD) mapping revealed that VT was negatively correlated with FCD in the right inferior temporal gyrus (ITG) (i.e., the lower FCD in ITG, the worse the VT), identified by mapping onto known large-scale networks as part of the default-mode network (DMN). Resting-state functional connectivity (RSFC) analysis using ITG as seed found that VT was predicted by lower functional connectivity between ITG and other DMN regions including left medial prefrontal cortex, left orbitofrontal cortex, right superior frontal gyrus, right inferior parietal lobule and bilateral precuneus (i.e., the lower the ITG-DMN connectivity, the worse the VT). Mediation analyses suggested that psychological resilience served as a mediator in these associations of ITG FCD and ITG-DMN RSFC with VT. Our results provide novel evidence on the brain basis of VT and emphasize psychological resilience as an important link from DMN functional connectivity to COVID-specific-VT. This may facilitate public health interventions by helping identify individuals at risk of stress- and trauma-related psychopathologies.

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.

Default Mode and Frontoparietal Network Dynamics: Associations with Familial Risk for Depression and Stress Sensitivity

Background

Major Depressive Disorder (MDD) is associated with alterations within the default mode (DMN) and frontoparietal (FPN) networks. However, it is unclear whether changes in these networks occur prior to onset in youth at high familial risk for MDD or are a consequence of MDD. Moreover, studies examining premorbid MDD vulnerability markers have focused on static rather than dynamic network properties, which could further elucidate DMN-FPN imbalances linked to MDD risk.

Methods

Eighty-nine unaffected 12-14-year-old adolescents both with (n = 27) and without (n = 62) a maternal history of MDD completed a resting state functional magnetic resonance imaging scan and self-report assessments of depressive symptoms and perceived stress at baseline and every three months across a two-year span. A coactivation pattern (CAP) analysis was conducted to examine functional network dynamic properties, including time spent in each CAP (total number of volumes), CAP persistence (number of consecutive volumes in each CAP), and number of transitions between posterior DMN-FPN and canonical DMN CAPs. Multilevel models estimated whether DMN-FPN dynamic properties predicted future depressive symptoms and stress sensitivity.

Results

High-risk adolescents spent more time and exhibited a longer persistence in a posterior DMN-FPN CAP. DMN-FPN CAP persistence predicted future perceived stress, but only among high-risk adolescents. High-risk adolescents characterized by high DMN-FPN persistence reported greater future perceived stress, whereas those showing low DMN-FPN persistence had reduced perceived stress over time. Unexpectedly, DMN-FPN dynamics did not predict future depressive symptoms.

Conclusions

Altered DMN-FPN CAP properties among high-risk adolescents mirror alterations among individuals with MDD, suggesting that DMN-FPN dynamics may be a risk marker rather than consequence of MDD. Furthermore, longer DMN-FPN CAP persistence increases vulnerability in high-risk adolescents by predicting greater future stress sensitivity, a well-known catalyst for MDD. Replication in a larger sample is warranted.

Hair Cortisone Predicts Lower Stress-induced Salivary Cortisol Response: Resting-state Functional Connectivity Between Salience and Limbic Networks

Previous studies revealed that high long-term hypothalamic-pituitaryadrenal (HPA) axis activity measured by the hair cortisol concentrations predicts lower acute stress cortisol response and reported the influences of hair cortisol on brain activity during acute stress exposure. However, considering that long-term HPA axis activity has a close relationship with the brain's resting-state functional connectivity (RSFC), the current study aimed to explore the role of RSFC between limbic and salience network in this relationship. Seventy-seven healthy participants underwent resting-state imaging scans before performing the acute ScanSTRESS task. Saliva samples were collected to assess the levels of acute stress salivary cortisol. Hair samples were also collected, and the corticosteroid concentration extracted from these samples were used as a biomarker of long-term HPA axis activity. High hair cortisone (HairE) levels predicted lower acute stress cortisol response. Moreover, high HairE levels were significantly correlated with enhanced RSFC between limbic and salience networks, while RSFC was negatively associated with acute stress cortisol response. Importantly, the RSFC between left insula and left parahippocampus mediated the association between HairE and acute cortisol stress response. Taken together, this study uncovers the important role of RSFC between salience and limbic networks in the long-term relationship between HairE and acute cortisol response and contributes to a deeper understanding of the individual differences in acute stress response.

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.

Assessing functional connectivity differences and work-related fatigue in surviving COVID-negative patients

The Coronavirus Disease 2019 (COVID-19) has affected all aspects of life around the world. Neuroimaging evidence suggests the novel coronavirus can attack the central nervous system (CNS), causing cerebro-vascular abnormalities in the brain. This can lead to focal changes in cerebral blood flow and metabolic oxygen consumption rate in the brain. However, the extent and spatial locations of brain alterations in COVID-19 survivors are largely unknown. In this study, we have assessed brain functional connectivity (FC) using resting-state functional MRI (RS-fMRI) in 38 (25 males) COVID patients two weeks after hospital discharge, when PCR negative and 31 (24 males) healthy subjects. FC was estimated using independent component analysis (ICA) and dual regression. When compared to the healthy group, the COVID group demonstrated significantly enhanced FC in the basal ganglia and precuneus networks (family wise error (fwe) corrected, pfwe < 0.05), while, on the other hand, reduced FC in the language network (pfwe < 0.05). The COVID group also experienced higher fatigue levels during work, compared to the healthy group (p < 0.001). Moreover, within the precuneus network, we noticed a significant negative correlation between FC and fatigue scores across groups (Spearman's ρ = -0.47, p = 0.001, r2 = 0.22). Interestingly, this relationship was found to be significantly stronger among COVID survivors within the left parietal lobe, which is known to be structurally and functionally associated with fatigue in other neurological disorders.

Individualized morphometric similarity predicts body mass index and food approach behavior in school-age children

Childhood obesity is associated with alterations in brain structure. Previous studies generally used a single structural index to characterize the relationship between body mass index(BMI) and brain structure, which could not describe the alterations of structural covariance between brain regions. To cover this research gap, this study utilized two independent datasets with brain structure profiles and BMI of 155 school-aged children. Connectome-based predictive modeling(CPM) was used to explore whether children’s BMI is reliably predictable by the novel individualized morphometric similarity network(MSN). We revealed the MSN can predict the BMI in school-age children with good generalizability to unseen dataset. Moreover, these revealed significant brain structure covariant networks can further predict children’s food approach behavior. The positive predictive networks mainly incorporated connections between the frontoparietal network(FPN) and the visual network(VN), between the FPN and the limbic network(LN), between the default mode network(DMN) and the LN. The negative predictive network primarily incorporated connections between the FPN and DMN. These results suggested that the incomplete integration of the high-order brain networks and the decreased dedifferentiation of the high-order networks to the primary reward networks can be considered as a core structural basis of the imbalance between inhibitory control and reward processing in childhood obesity.

Effects of hyperbaric oxygen therapy on functional and structural connectivity in post-COVID-19 condition patients: A randomized, sham-controlled trial

INTRODUCTION

Post-COVID-19 condition refers to a range of persisting physical, neurocognitive, and neuropsychological symptoms after SARS-CoV-2 infection. Abnormalities in brain connectivity were found in recovered patients compared to non-infected controls. This study aims to evaluate the effect of hyperbaric oxygen therapy (HBOT) on brain connectivity in post-COVID-19 patients.

METHODS

In this randomized, sham-controlled, double-blind trial, 73 patients were randomized to receive 40 daily sessions of HBOT (n = 37) or sham treatment (n = 36). We examined pre- and post-treatment resting-state brain functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) scans to evaluate functional and structural connectivity changes, which were correlated to cognitive and psychological distress measures.

RESULTS

The ROI-to-ROI analysis revealed decreased internetwork connectivity in the HBOT group which was negatively correlated to improvements in attention and executive function scores (p < 0.001). Significant group-by-time interactions were demonstrated in the right hippocampal resting state function connectivity (rsFC) in the medial prefrontal cortex (PFWE = 0.002). Seed-to-voxel analysis also revealed a negative correlation in the brief symptom inventory (BSI-18) score and in the rsFC between the amygdala seed, the angular gyrus, and the primary sensory motor area (PFWE = 0.012, 0.002). Positive correlations were found between the BSI-18 score and the left insular cortex seed and FPN (angular gyrus) (PFWE < 0.0001). Tractography based structural connectivity analysis showed a significant group-by-time interaction in the fractional anisotropy (FA) of left amygdala tracts (F = 7.81, P = 0.007). The efficacy measure had significant group-by-time interactions (F = 5.98, p = 0.017) in the amygdala circuit.

CONCLUSIONS

This study indicates that HBOT improves disruptions in white matter tracts and alters the functional connectivity organization of neural pathways attributed to cognitive and emotional recovery in post-COVID-19 patients. This study also highlights the potential of structural and functional connectivity analysis as a promising treatment response monitoring tool.

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.

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.