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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

  • Author Footnotes
    1 MC-T and LV-A contributed equally to this work.
    María Cabello-Toscano
    Footnotes
    1 MC-T and LV-A contributed equally to this work.
    Affiliations
    Department of Medicine, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain

    Institut de Recerca Biomèdica August Pi i Sunyer, Barcelona, Spain

    Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la Universitat Autónoma de Barcelona, Barcelona, Spain
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  • Author Footnotes
    1 MC-T and LV-A contributed equally to this work.
    Lídia Vaqué-Alcázar
    Footnotes
    1 MC-T and LV-A contributed equally to this work.
    Affiliations
    Department of Medicine, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain

    Institut de Recerca Biomèdica August Pi i Sunyer, Barcelona, Spain
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  • Gabriele Cattaneo
    Affiliations
    Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la Universitat Autónoma de Barcelona, Barcelona, Spain

    Fundació Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol, Badalona, Spain
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  • Javier Solana-Sánchez
    Affiliations
    Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la Universitat Autónoma de Barcelona, Barcelona, Spain

    Fundació Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol, Badalona, Spain
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  • Ivet Bayes-Marin
    Affiliations
    Department of Medicine, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain
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  • Kilian Abellaneda-Pérez
    Affiliations
    Department of Medicine, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain

    Institut de Recerca Biomèdica August Pi i Sunyer, Barcelona, Spain
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  • Dídac Macià-Bros
    Affiliations
    ISGlobal, Hospital Clínic, Universitat de Barcelona, Barcelona, Spain

    Department of Biomedicine, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain
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  • Lídia Mulet-Pons
    Affiliations
    Department of Medicine, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain

    Institut de Recerca Biomèdica August Pi i Sunyer, Barcelona, Spain
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  • Cristina Portellano-Ortiz
    Affiliations
    Department of Medicine, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain

    Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la Universitat Autónoma de Barcelona, Barcelona, Spain
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  • Miquel Angel Fullana
    Affiliations
    Adult Psychiatry and Psychology Department, Institute of Neurosciences, Hospital Clínic, Barcelona, Spain

    Imaging of Mood- and Anxiety-Related Disorders Group, Institut d’Investigacions Biomèdiques August Pi i Sunyer, Centre for Biomedical Research on Mental Health, Barcelona, Spain
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  • Laura Oleaga
    Affiliations
    Centre de Diagnòstic per la Imatge Clínic, Hospital Clínic de Barcelona, Barcelona, Spain
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  • Sofía González
    Affiliations
    Centre de Diagnòstic per la Imatge Clínic, Hospital Clínic de Barcelona, Barcelona, Spain
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  • Nuria Bargalló
    Affiliations
    Institut de Recerca Biomèdica August Pi i Sunyer, Barcelona, Spain

    Centre for Biomedical Research on Mental Health, Barcelona, Spain

    Centre de Diagnòstic per la Imatge Clínic, Hospital Clínic de Barcelona, Barcelona, Spain
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  • Jose M. Tormos
    Affiliations
    Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la Universitat Autónoma de Barcelona, Barcelona, Spain

    Fundació Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol, Badalona, Spain
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  • Alvaro Pascual-Leone
    Correspondence
    Alvaro Pascual-Leone, M.D., Ph.D.
    Affiliations
    Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la Universitat Autónoma de Barcelona, Barcelona, Spain

    Hinda and Arthur Marcus Institute for Aging Research and Deanna and Sidney Wolk Center for Memory Health, Hebrew SeniorLife, Harvard Medical School, Boston Massachusetts

    Department of Neurology, Harvard Medical School, Boston Massachusetts
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  • David Bartrés-Faz
    Correspondence
    Address correspondence to David Bartrés-Faz, Ph.D.
    Affiliations
    Department of Medicine, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain

    Institut de Recerca Biomèdica August Pi i Sunyer, Barcelona, Spain

    Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la Universitat Autónoma de Barcelona, Barcelona, Spain
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  • Author Footnotes
    1 MC-T and LV-A contributed equally to this work.
Open AccessPublished:August 20, 2022DOI:https://doi.org/10.1016/j.bpsc.2022.08.005

      Abstract

      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.

      Keywords

      The COVID-19 pandemic has resulted in an unprecedented impact, with more than 400 million people affected and 6 million deaths worldwide by mid-March 2022 (https://www.worldometers.info/coronavirus/?utm_campaign=homeAdvegas1). From its inception, this pandemic has been highlighted as a health and societal threat, not only owing to the direct negative effects of SARS-CoV-2 infection but also because of the long-term restrictions imposed by governments and authorities attempting to prevent or limit the spread of the virus. General confinements and quarantines, along with other protective measures, closure of businesses, and limitation of social interactions, can be expected to result in multiple psychological sequelae (
      • Pfefferbaum B.
      • North C.S.
      Mental health and the Covid-19 pandemic.
      ). Accordingly, overall rates of around 30% in anxiety and depressive symptoms have been observed, which are higher than the usual incidence rate observed in the general population [e.g., (
      • Luo M.
      • Guo L.
      • Yu M.
      • Jiang W.
      • Wang H.
      The psychological and mental impact of coronavirus disease 2019 (COVID-19) on medical staff and general public – A systematic review and meta-analysis.
      ,
      • Salari N.
      • Hosseinian-Far A.
      • Jalali R.
      • Vaisi-Raygani A.
      • Rasoulpoor S.
      • Mohammadi M.
      • et al.
      Prevalence of stress, anxiety, depression among the general population during the COVID-19 pandemic: A systematic review and meta-analysis.
      )]. Nonetheless, many of the initial studies investigating mental health effects of the COVID-19 pandemic have been cross-sectional and have lacked comparable pre-pandemic baseline data. These methodological constraints limit the interpretation of findings; in fact, other studies challenge the assumption that the effect of the pandemic on mental health can be described as a significant overall negative impact on anxiety and depressive symptoms (
      • van der Velden P.G.
      • Contino C.
      • Das M.
      • van Loon P.
      • Bosmans M.W.G.
      Anxiety and depression symptoms, and lack of emotional support among the general population before and during the COVID-19 pandemic. A prospective national study on prevalence and risk factors.
      ,
      • Fullana M.A.
      • Littarelli S.A.
      Covid-19, anxiety, and anxiety-related disorders.
      ). There also remains inconsistency among studies with preoutbreak data, with some studies reporting significant increases in psychological distress (
      • Pierce M.
      • Hope H.
      • Ford T.
      • Hatch S.
      • Hotopf M.
      • John A.
      • et al.
      Mental health before and during the COVID-19 pandemic: A longitudinal probability sample survey of the UK population.
      ,
      • Twenge J.M.
      • Joiner T.E.
      U.S. Census Bureau-assessed prevalence of anxiety and depressive symptoms in 2019 and during the 2020 COVID-19 pandemic.
      ) and others highlighting general null effects (
      • van der Velden P.G.
      • Contino C.
      • Das M.
      • van Loon P.
      • Bosmans M.W.G.
      Anxiety and depression symptoms, and lack of emotional support among the general population before and during the COVID-19 pandemic. A prospective national study on prevalence and risk factors.
      ). Other research has highlighted the high prevalence of individuals showing resilient outcomes, and in general, the need to consider different, even opposite, trajectories across groups of individuals (
      • Kimhi S.
      • Eshel Y.
      • Marciano H.
      • Adini B.
      • Bonanno G.A.
      Trajectories of depression and anxiety during COVID-19 associations with religion, income, and economic difficulties.
      ,
      • Shevlin M.
      • Butter S.
      • McBride O.
      • Murphy J.
      • Gibson-Miller J.
      • Hartman T.K.
      • et al.
      Refuting the myth of a “tsunami” of mental ill-health in populations affected by COVID-19: Evidence that response to the pandemic is heterogenous, not homogeneous [published online ahead of print Apr 20].
      ).
      Resilience is a broad term that generally refers to the interindividual differences with regard to the ability to resist the impact of an illness or stress (
      • Pascual-Leone A.
      • Bartres-Faz D.
      Human brain resilience: A call to action.
      ). Hence, in the context of this study, resilience can be defined as the lack of anxiety or depression during the COVID-19 pandemic. Psychological variables, such as coping abilities, are defined as behaviors to protect oneself by avoiding psychological harm from bad experiences (
      • Vannini P.
      • Gagliardi G.P.
      • Kuppe M.
      • Dossett M.L.
      • Donovan N.J.
      • Gatchel J.R.
      • et al.
      Stress, resilience, and coping strategies in a sample of community-dwelling older adults during COVID-19 [published correction appears in J Psychiatr Res. 2021;142:167-170].
      ) and have been shown to be strongly associated with resilience to life traumas (
      • Sisto A.
      • Vicinanza F.
      • Campanozzi L.L.
      • Ricci G.
      • Tartaglini D.
      • Tambone V.
      Towards a transversal definition of psychological resilience: A literature review.
      ). Moreover, the role of distinct neurobiological substrates of resilience have been highlighted (
      • Feder A.
      • Fred-Torres S.
      • Southwick S.M.
      • Charney D.S.
      The biology of human resilience: Opportunities for enhancing resilience across the life span.
      ). Both neuroimaging (
      • Bolsinger J.
      • Seifritz E.
      • Kleim B.
      • Manoliu A.
      Neuroimaging correlates of resilience to traumatic events—A comprehensive review.
      ,
      • Holz N.E.
      • Tost H.
      • Meyer-Lindenberg A.
      Resilience and the brain: A key role for regulatory circuits linked to social stress and support.
      ) and neurophysiological (
      • Paban V.
      • Modolo J.
      • Mheich A.
      • Hassan M.
      Psychological resilience correlates with EEG source-space brain network flexibility.
      ) studies in humans have revealed that the integrity/functionality of specific brain networks are associated with different response adaptations to major threatening life events or during experimental investigations (
      • van Oort J.
      • Kohn N.
      • Vrijsen J.N.
      • Collard R.
      • Duyser F.A.
      • Brolsma S.C.A.
      • et al.
      Absence of default mode downregulation in response to a mild psychological stressor marks stress-vulnerability across diverse psychiatric disorders.
      ). Specifically, numerous studies point out anatomical and functional implications on resilience of different frontal (e.g., dorsolateral, orbitofrontal) and limbic (e.g., amygdala, insula, or striatum) areas, midline structures integrated within the default mode network (DMN) (
      • Bolsinger J.
      • Seifritz E.
      • Kleim B.
      • Manoliu A.
      Neuroimaging correlates of resilience to traumatic events—A comprehensive review.
      ), and the cingulate cortex (
      • Kong F.
      • Wang X.
      • Hu S.
      • Liu J.
      Neural correlates of psychological resilience and their relation to life satisfaction in a sample of healthy young adults.
      ,
      • Richter A.
      • Krämer B.
      • Diekhof E.K.
      • Gruber O.
      Resilience to adversity is associated with increased activity and connectivity in the VTA and hippocampus.
      ,
      • Iadipaolo A.S.
      • Marusak H.A.
      • Paulisin S.M.
      • Sala-Hamrick K.
      • Crespo L.M.
      • Elrahal F.
      • et al.
      Distinct neural correlates of trait resilience within core neurocognitive networks in at-risk children and adolescents.
      ). Concurrently, graph theory approaches for the study of brain connectivity enable the description of the dynamics of brain organization (
      • Sporns O.
      Graph theory methods: Applications in brain networks.
      ). More specifically, the effective functioning of the network seems to be supported by maintaining the separation of subnetworks while enabling integration between them. This harmony can be quantified by metrics such as system segregation (SyS), which summarizes the balance between integration within and between networks in a single value (
      • Wig G.S.
      Segregated systems of human brain networks.
      ). SyS variability has been studied specifically in the context of aging, cognition (
      • Chan M.Y.
      • Park D.C.
      • Savalia N.K.
      • Petersen S.E.
      • Wig G.S.
      Decreased segregation of brain systems across the healthy adult lifespan.
      ), and resilience to neurodegenerative disease (
      • Ewers M.
      • Luan Y.
      • Frontzkowski L.
      • Neitzel J.
      • Rubinski A.
      • Dichgans M.
      • et al.
      Segregation of functional networks is associated with cognitive resilience in Alzheimer’s disease.
      ), but it remains poorly explored in the context of mental health resilience.
      Altogether, these lines of evidence suggest that the interaction of an individual’s psychological resources (e.g., coping strategies) with brain functional characteristics should predict individual differences in resilience versus vulnerability to mental health outcomes in the face of a sustained stressful situation (e.g., perceived stress during the COVID-19 pandemic). Therefore, taking advantage of longitudinal data collected starting 2 years pre-pandemic and during the first year of the pandemic on several occasions, we first aimed to investigate whether a general change in anxiety and depression symptoms could be observed in our sample of healthy middle-aged individuals as well as to validate previous findings regarding the influence of principal sociodemographic factors (i.e., age, sex, and education) (
      • Pierce M.
      • Hope H.
      • Ford T.
      • Hatch S.
      • Hotopf M.
      • John A.
      • et al.
      Mental health before and during the COVID-19 pandemic: A longitudinal probability sample survey of the UK population.
      ,
      • Fancourt D.
      • Steptoe A.
      • Bu F.
      Trajectories of anxiety and depressive symptoms during enforced isolation due to COVID-19 in England: A longitudinal observational study.
      ). Second, we aimed to determine whether psychological factors (perceived stress and coping strategies) explained the change in anxiety and depressive symptoms. Finally, as our main goal, we were interested in elucidating whether the connectivity status of brain networks was able to predict, either in an independent manner or by the interaction with the studied psychological factors, the change in psychological distress associated with the pandemic. We hypothesized that we would be able to identify a significant change in psychological distress related to the pandemic and that both sociodemographic and psychological factors would influence this change in anxiety and depression symptoms. We also predicted that basal connectivity status of particular resilience-related networks, such as those involving frontal, limbic, cingulate, or DMN areas, would influence the degree of pandemic-related change in psychological distress experienced by our cohort.

      Methods and Materials

      Study Design and Participants

      Study participants were part of the BBHI (Barcelona Brain Health Initiative; https://bbhi.cat/en/), an ongoing longitudinal cohort study investigating the determinants of brain and mental health in healthy middle-aged and older adults. Recruitment started in 2017, when multiple initiatives (including conferences, radio and television interviews, and social media advertisements) took place to encourage participants to join the study. The BBHI’s main inclusion criteria are the absence of neurological, psychiatric, or unstable medical diagnoses and no cognitive impairment. The BBHI includes periodic cognitive, medical, brain imaging, and biological assessments (
      • Cattaneo G.
      • Bartrés-Faz D.
      • Morris T.P.
      • Sánchez J.S.
      • Macià D.
      • Tarrero C.
      • et al.
      The Barcelona brain health initiative: A cohort study to define and promote determinants of brain health.
      ,
      • Cattaneo G.
      • Bartrés-Faz D.
      • Morris T.P.
      • Solana Sánchez J.
      • Macià D.
      • Tormos J.M.
      • Pascual-Leone A.
      The Barcelona Brain Health Initiative: Cohort description and first follow-up.
      ). This study refers to a BBHI substudy aimed at investigating mental health during the COVID-19 pandemic (
      • Pascual-Leone A.
      • Bartres-Faz D.
      Human brain resilience: A call to action.
      ,
      • Bartrés-Faz D.
      • Macià D.
      • Cattaneo G.
      • Borràs R.
      • Tarrero C.
      • Solana J.
      • et al.
      The paradoxical effect of COVID-19 outbreak on loneliness.
      ).
      Data acquisition included a longitudinal design with measures of anxiety and depression symptoms collected 2 times before the pandemic outbreak (i.e., pre-pandemic) between 2018 and 2020 (average interval, 12.73 ± 2.18 months) and 5 assessments separated on average by 3.04 ± 2.29 months and covering the first year of the COVID-19 pandemic (i.e., from March 2020 to March-April 2021) (Figure 1). The primary outcome measure for this study was symptoms of anxiety and depression as assessed with the Patient Health Questionnaire-4 (PHQ-4) (see Questionnaires). Only participants who had valid PHQ-4 measures obtained at least once pre-pandemic and once during the pandemic were included (see Questionnaires). Furthermore, because our focus was on studying the impact of the COVID-19 pandemic on the healthy population, we excluded all individuals who had scores suggesting a possible meaningful clinical status at any of the pre-pandemic assessments (i.e., PHQ-4 scores equal to or above 6) according to recommended cutoffs (
      • Kroenke K.
      • Spitzer R.L.
      • Williams J.B.
      • Löwe B.
      An ultra-brief screening scale for anxiety and depression: The PHQ–4.
      ) (see BBHI vs whole sample in the Supplement for more information on sample differences). For our main objective, only those participants who had available baseline magnetic resonance imaging (MRI) acquisitions before the outbreak that met the quality check inspection requirements and had normative neuroradiological reports (e.g., no brain tumor suspicions, stroke, or moderate to severe white matter damage) were included. In addition, data from 7 participants were discarded because of outlier values in the functional connectivity (FC) measures (see FC Measures). This led to a study sample of 2023 participants and 10,367 observations and a subsample of 433 MRI-available individuals and 2358 observations (Figure S1). The study was approved by the Unió Catalana d’Hospitals ethics committee (approval references: CEIC 17/06 and CEI 18/07). Written informed consent was obtained from all participants in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki).
      Figure thumbnail gr1
      Figure 1Timeline study design showing baseline (i.e., pre-pandemic) and during-pandemic points of acquisition for the main outcome (i.e., Patient Health Questionnaire-4 [PHQ-4]), magnetic resonance imaging (MRI), and psychological factors (perceived stress measured by the Perceived Stress Scale [PSS] and coping strategies measured by the Brief Resilient Coping Scale [BRCS]). On the left and colored in purple are pre-pandemic data including 1 MRI acquisition and 2 online PHQ-4 measures obtained between 2018 and 2020 (average follow-up: 12.73 ± 2.18 months). The beginning of the pandemic outbreak was defined according to the Spanish Government’s State of Emergency declaration on March 14, 2020. On the right and colored in green, 6 online questionnaires were administered during the first year of the COVID-19 pandemic (until March–April 2021). Each questionnaire was available for answering during the specified data periods shown.

      Questionnaires

      The main outcome was anxiety and depression symptoms assessed with the PHQ-4, a valid ultra-brief tool consisting of 4 Likert-type scale items for detecting both anxiety and depressive disorders (
      • Kroenke K.
      • Spitzer R.L.
      • Williams J.B.
      • Löwe B.
      An ultra-brief screening scale for anxiety and depression: The PHQ–4.
      ). Perceived stress was assessed with the Perceived Stress Scale, a 14-item, 5-point Likert-type scale including questions about feelings and thoughts during the past month (
      • Cohen S.
      • Kamarck T.
      • Mermelstein R.
      A global measure of perceived stress.
      ). The Brief Resilient Coping Scale is a 4-item, 5-point Likert-type scale used to estimate the tendency to effectively use coping strategies in flexible, committed ways to actively solve problems despite stressful circumstances (
      • Sinclair V.G.
      • Wallston K.A.
      The development and psychometric evaluation of the Brief Resilient Coping Scale.
      ). For further details regarding the questionnaires, see the Supplement.

      FC Measures

      MRI data were acquired using a 3T Siemens scanner (Magnetom Prisma; Siemens Healthineers) with a 32-channel head coil at the Unitat d’Imatge per Ressonància Magnètica Institut d’Investigacions Biomèdiques August Pi i Sunyer at Hospital Clínic de Barcelona, Barcelona, Spain. Resting-state functional MRI scans were preprocessed, and then we quantified individual resting-state FC within and between resting-state networks as defined in the Schaefer-Yeo atlas of 100 nodes and 7 networks (
      • Yeo B.T.
      • Krienen F.M.
      • Sepulcre J.
      • Sabuncu M.R.
      • Lashkari D.
      • Hollinshead M.
      • et al.
      The organization of the human cerebral cortex estimated by intrinsic functional connectivity.
      ,
      • Schaefer A.
      • Kong R.
      • Gordon E.M.
      • Laumann T.O.
      • Zuo X.N.
      • Holmes A.J.
      • et al.
      Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI.
      ) (available at: https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal for the calculation of the SyS metric (
      • Wig G.S.
      Segregated systems of human brain networks.
      ). Here, SyS values were considered as outliers when they were 3 standard deviations over or under the average (i.e., |z score| < 3, where z score = [x − mean]/SD). As a result, 7 participants were excluded from the final sample.
      See the Supplement for further details regarding the acquisition parameters, preprocessing, and SyS.

      Statistical Analyses

      All statistical analyses were written in R language (version 3.6.2) (
      R Core Team
      R: A Language and Environment for Statistical Computing. MSOR connections.
      ) and run in RStudio (version 1.3.1093) (
      RStudio Team
      RStudio: Integrated Development Environment for R..
      ).
      To investigate the change in anxiety and depression symptoms (i.e., PHQ-4 scores) along all the time points, a linear mixed-effect model was first fitted for the whole sample using the lmer function from the lme4 R package (
      • Prati G.
      • Mancini A.D.
      The psychological impact of COVID-19 pandemic lockdowns: A review and meta-analysis of longitudinal studies and natural experiments.
      ). In this model, fixed and random effect coefficients were estimated for a binary variable indicating whether each observation belonged to pre-pandemic assessments or to assessments made during the pandemic (i.e., COVID-19 period) to quantify pandemic-related PHQ-4 general and individual changes, respectively. The individual effect coefficients were extracted to generate a new variable termed PHQ-4 change (i.e., change in anxiety and depression symptoms) where positive values meant PHQ-4 increases during compared with pre-pandemic observations (i.e., anxiety and depression symptoms worsening). To analyze the associations between sociodemographic variables and PHQ-4 change, a linear regression model was fitted in which PHQ-4 change was the outcome and sex, age, and education were the predictor variables of interest. In addition, an analogous linear mixed-effect model was fitted by including only pre-pandemic observations. Then we fitted 3 linear regression models in which PHQ-4 change was the outcome and the predictors were coping strategies, perceived stress, and their interaction. Finally, we fitted a set of linear regression models to predict PHQ-4 change, in which SyS values from the 7 studied resting-state networks were included as independent variables. In this way, we tested whether there was a direct association between any network SyS and PHQ-4 change and whether SyS measures modulated the effects of the psychological factors (i.e., perceived stress and coping strategies) on the outcome. In addition, 2 analogous models were fitted to test for any association between SyS values and coping strategies or perceived stress, respectively. All these models were adjusted for age, sex, socioeconomic status, employment situation during the pandemic, average pre-pandemic levels of anxiety and depression symptoms, and number of months between the last questionnaire administered before the pandemic and the first questionnaire administered during the pandemic.

      Results

      Sample Demographics and Psychological Characteristics

      This study included a total sample of 2023 participants (age: 54.32 ± 7.18 years, 65.69% female) and a subsample of 433 individuals with available MRI data (age: 53.02 ± 7.04 years, 46.88% female) from the BBHI cohort (
      • Cattaneo G.
      • Bartrés-Faz D.
      • Morris T.P.
      • Sánchez J.S.
      • Macià D.
      • Tarrero C.
      • et al.
      The Barcelona brain health initiative: A cohort study to define and promote determinants of brain health.
      ,
      • Cattaneo G.
      • Bartrés-Faz D.
      • Morris T.P.
      • Solana Sánchez J.
      • Macià D.
      • Tormos J.M.
      • Pascual-Leone A.
      The Barcelona Brain Health Initiative: Cohort description and first follow-up.
      ). At baseline and as per inclusion criteria, all the subjects presented normal to mild symptomatology (i.e., PHQ-4 > 6 within a range of 0–12) before the pandemic outbreak. Regarding psychological factors of vulnerability (i.e., perceived stress) and those associated with mechanisms of resilience (i.e., coping strategies), both samples mostly presented medium to high coping and low to moderate stress profiles (Table 1).
      Table 1Sample Characteristics
      CharacteristicWhole Sample, N = 2023MRI Subsample, n = 433
      Age, Years54.32 ± 7.1853.02 ± 7.04
      Sex
       Female1329 (65.69%)203 (46.88%)
       Male694 (34.31%)230 (53.12%)
      Educational Level
      Primary educational level corresponds to general basic education or equivalent (8 years approximately), secondary corresponds to baccalaureate or equivalent (up to approximately 12 years), and higher corresponds to university degrees such as diploma, degree, Master, or Ph.D. (over 12 years).
       Primary67 (3.31%)12 (2.77%)
       Secondary436 (21.55%)104 (24.02%)
       Higher1520 (75.14%)317 (73.21%)
      Socioeconomic Status
      Socioeconomic status corresponds to the approximate range of the individuals’ monthly family income (low: <1000; low-middle: 1000–2000; middle-high: 2000–5000; high: >5000; all amounts in euros).
      ,
      Missing data are due to noncompletion of the questionnaires by participants.
       Low51 (2.53%)10 (2.31%)
       Low−middle374 (18.54%)83 (19.17%)
       Middle−high1198 (59.40%)234 (54.04%)
       High394 (19.53%)106 (24.48%)
      Employment During the Pandemic
      An individual was considered as employed when answered so at all time points.
       Employed1123 (55.51%)266 (61.43%)
       Unemployed900 (44.49%)167 (38.57%)
      Anxiety and Depression
      Psychological variables are described here as categories created according to available cutoffs regarding severity of anxiety and depressive symptomatology (i.e., PHQ-4), level of coping strategies (i.e., BRCS), and perceived stress (i.e., PSS). Note that this categorization was done under descriptive purposes, but these variables were used as continuous in this study. In addition, as anxiety and depression were assessed on multiple occasions in both periods (pre-pandemic and during the pandemic), a subject was considered to present moderate to severe symptomatology when scoring within this range at least once.
       Pre-pandemic
      Normal−mild, 0–52023 (100%)433 (100%)
      Moderate−severe, 6–120 (0.00%)0 (0.00%)
       During the pandemic
      Normal−mild, 0–51818 (89.87%)400 (92.38%)
      Moderate−severe, 6–12205 (10.13%)33 (7.62%)
      Coping Strategies
      Missing data are due to noncompletion of the questionnaires by participants.
      ,
      Psychological variables are described here as categories created according to available cutoffs regarding severity of anxiety and depressive symptomatology (i.e., PHQ-4), level of coping strategies (i.e., BRCS), and perceived stress (i.e., PSS). Note that this categorization was done under descriptive purposes, but these variables were used as continuous in this study. In addition, as anxiety and depression were assessed on multiple occasions in both periods (pre-pandemic and during the pandemic), a subject was considered to present moderate to severe symptomatology when scoring within this range at least once.
       Low, 4–13371 (18.70%)60 (13.92%)
       Medium, 14–161106 (55.75%)226 (52.44%)
       High, 17–20507 (25.55%)145 (33.64%)
      Perceived Stress
      Missing data are due to noncompletion of the questionnaires by participants.
      ,
      Psychological variables are described here as categories created according to available cutoffs regarding severity of anxiety and depressive symptomatology (i.e., PHQ-4), level of coping strategies (i.e., BRCS), and perceived stress (i.e., PSS). Note that this categorization was done under descriptive purposes, but these variables were used as continuous in this study. In addition, as anxiety and depression were assessed on multiple occasions in both periods (pre-pandemic and during the pandemic), a subject was considered to present moderate to severe symptomatology when scoring within this range at least once.
       Low, 0–13465 (30.80%)128 (38.65%)
       Moderate, 14–26928 (61.46%)188 (55.29%)
       High, 27–40117 (7.75%)24 (7.06%)
      Continuous variables are described by mean ± SD values, while categorical variables are described by the absolute number of individuals and its corresponding percentage (%) within the sample.
      BRCS, Brief Resilient Coping Scale; MRI, magnetic resonance imaging; PHQ-4, Patient Health Questionnaire-4; PSS, Perceived Stress Scale.
      a Primary educational level corresponds to general basic education or equivalent (8 years approximately), secondary corresponds to baccalaureate or equivalent (up to approximately 12 years), and higher corresponds to university degrees such as diploma, degree, Master, or Ph.D. (over 12 years).
      b Socioeconomic status corresponds to the approximate range of the individuals’ monthly family income (low: <1000; low-middle: 1000–2000; middle-high: 2000–5000; high: >5000; all amounts in euros).
      c Missing data are due to noncompletion of the questionnaires by participants.
      d An individual was considered as employed when answered so at all time points.
      e Psychological variables are described here as categories created according to available cutoffs regarding severity of anxiety and depressive symptomatology (i.e., PHQ-4), level of coping strategies (i.e., BRCS), and perceived stress (i.e., PSS). Note that this categorization was done under descriptive purposes, but these variables were used as continuous in this study. In addition, as anxiety and depression were assessed on multiple occasions in both periods (pre-pandemic and during the pandemic), a subject was considered to present moderate to severe symptomatology when scoring within this range at least once.

      Changes in Anxiety and Depressive Symptoms: Age and Sex Effects

      A linear mixed-effect model on the total sample showed that PHQ-4 scores increased during the pandemic compared with pre-pandemic (during the pandemic > pre-pandemic: β = 0.229, t = 7.428, p < .001) (Figure 2). The random effect coefficients estimated for each individual were used to compute the PHQ-4 change variable (Figure S2). PHQ-4 change was negatively associated with age (β = −0.006, t = −4.084, p < .001) (see Figure S3A) but not with educational level (β = 0.034, t = 1.683, p = .092). This model (adjusted R2 [a-R2] = 0.278) also revealed that female individuals had higher PHQ-4 change values than males (β = 0.141, t = 6.622, p < .001) (Figure S3B). In addition, we did not find any interaction between age and sex associated with our outcome (a-R2 = 0.278; age × sex interaction; β = −0.004, t = −1.225, p = .221). Finally, considering only baseline data, we found that female (females > males; β = 0.261, t = 4.299, p < .001) and younger individuals (age: β= −0.158, t = −4.338, p < .001) had higher pre-pandemic PHQ-4 values.
      Figure thumbnail gr2
      Figure 2Average values of the Patient Health Questionnaire-4 (PHQ-4) along time point measurements for the whole sample (N = 2023) showing ratings increases. Shadow areas above and below the average PHQ-4 line (i.e., thick line) represent standard errors. Abscissa axes indicate the timeline of observations in the study, which are grouped within pre-pandemic (i.e., from 2018 to early 2020) and during-pandemic observations (i.e., those from March 2020 to March–April 2021). The green line indicates the beginning of the lock-down (March 14, 2020 in Spain) and separates pre-pandemic and during-pandemic observations. Black vertical dashed lines delimit 2020 and 2021. Finally, in the upper part of the figure, the increase in PHQ-4 values at points during the pandemic compared with pre-pandemic is indicated.
      The results of repeating these analyses for the MRI subsample (n = 433) can be found in the Supplement.

      Changes in Effects of Perceived Stress and Coping Strategies on Anxiety and Depressive Symptoms

      In the total sample, we fitted 3 different linear models. The first model (a-R2 = 0.337) showed a negative association between coping strategies and PHQ-4 change (β = −0.069, t = −14.147, p < .001), and the second model (a-R2 = 0.421) showed a positive association between perceived stress and PHQ-4 change (β = 0.036, t = 19.241, p < .001). Finally, the third model (a-R2 = 0.447) revealed that the change was significantly described by an interaction between perceived stress and coping strategies (β = −0.003, t = −4.370, p < .001) (Figure 3A). In the latter analysis, the direct effect of perceived stress on PHQ-4 was reduced but maintained (β = 0.075, t = 7.085, p < .001), while the direct effect of coping strategies on anxiety and depressive symptoms change disappeared (β = 0.001, t = 0.801, p = .423) (Figure 3B).
      Figure thumbnail gr3
      Figure 3Plots illustrating the associations found between the studied psychological factors (i.e., coping strategies and perceived stress) and psychological distress worsening (i.e., Patient Health Questionnaire-4 [PHQ-4] change). (A) Scatter and lines plot showing the association between PHQ-4 change (vertical axis) and perceived stress (horizontal axis) as modulated by coping strategies. Dots show individual observations of PHQ-4 change and perceived stress for 2 groups with low (in brown; i.e., below median) and high (in green; i.e., above median) coping strategies. Thick lines illustrate estimated slopes for the association between PHQ-4 change and perceived stress for extreme minimum and maximum levels of low (in brown) and high (in green) coping strategies. This difference between slopes was found to be significant as an interaction between coping strategies and perceived stress to predict PHQ-4 change. Shadow areas above and below the slope lines represent standard errors. (B) Schema of the associations between variables of psychological factors and psychological distress worsening.
      The results of repeating these analyses for the MRI subsample (n = 433) were in accordance with those in the total sample (see the Supplement).

      Changes in Anxiety and Depression Symptoms as a Function of Brain Network Status and Psychological Factors

      Nonsignificant direct associations between mental health change, coping strategies, and perceived stress and any of the SyS values were found (all p values > .05). However, we aimed to test whether SyS variables were able to modulate the perceived stress effect on PHQ-4 change or the modulatory effect of coping strategies (i.e., PHQ-4 change ∼ coping strategies × perceived stress interaction). The first model (a-R2 = 0.536) showed a significant interaction between the frontoparietal control network SyS (FPCN-SyS) (Figure 4A) and perceived stress (β = 0.108, t = 2.446, p = .009) (Figure 4C) and between the DMN-SyS (DMN-SyS) (Figure 4B) and perceived stress (β = −0.096, t = −2.626, p = .015) (Figure 4D) to the described PHQ-4 change. These interactions show that higher FPCN-SyS levels enhanced the positive association between perceived stress and PHQ-4 change. Conversely, higher levels of DMN-SyS attenuated the association between perceived stress and PHQ-4 change (Figure 4E) similar to the modulation by coping strategies, which remained significant in this model (β = −0.003, t = −2.136, p = .033). Because these 2 neural mechanisms (i.e., FPCN-SyS and DMN-SyS) were significant even after accounting for the effects of coping strategies, it appears that these could be independent of each other. Finally, the second model (a-R2 = 0.539) showed a trend toward significance between the limbic network SyS variable and the coping strategies’ effect on the association between perceived stress and PHQ-4 (β = −0.024, t = −1.727, p = .085) in the sense that a higher limbic network SyS could be related to an increased effect of coping strategies as a psychological regulatory mechanism (Figure S4).
      Figure thumbnail gr4
      Figure 4Representation of the modulatory effect of frontoparietal control network (FPCN) and default mode network (DMN) system segregation (SyS) values on the association between perceived stress and Patient Health Questionnaire-4 (PHQ-4) change. (A, B) Graphs representing within- and between-network connectivity taking part in the computation of FPCN and DMN-SyS values, respectively. Nodes in the graph represent studied regions of interest (ROIs) as defined by the Schaefer-Yeo atlas of 100 nodes and 7 networks. The nodes and edges in light orange illustrate ROIs and within-network connectivity of the studied network (i.e., FPCN or DMN), while those in gray refer to outside network ROIs and the connectivity between them and the studied network. These graphs were created with the BrainNet Viewer (http://www.nitrc.org/projects/bnv/). (C, D) Scatter and lines plot showing the association between PHQ-4 change (vertical axis) and perceived stress (horizontal axis), as modulated by values of SyS, from the FPCN in panel (C) and the DMN in panel (D). Dots show individual observations of PHQ-4 change and perceived stress for 2 groups with low (in brown; i.e., below median) and high (in green; i.e., over median) SyS values. Thick lines illustrate estimated slopes for the association between PHQ-4 change and perceived stress for extreme minimum and maximum levels of low (in brown) and high (in green) SyS. This difference between slopes was found significant as an interaction between each particular SyS variable and perceived stress to predict PHQ-4 change. Shadow areas above and below the slope lines represent standard errors. (E) Schema of the associations between perceived stress and psychological distress worsening, as regulated by FPCN-SyS, DMN-SyS, and coping strategies.

      Discussion

      This study found a general increase in anxiety and depressive symptoms during the COVID-19 pandemic in a healthy middle-aged population where age and sex were found as independent predictors. We identified that coping strategies attenuated the impact of perceived stress on mental health. Finally, to our knowledge, this is the first study to identify the modulation of the impact of perceived stress on anxious-depressive responses through baseline FPCN and DMN network connectivity balance.
      Our findings revealed a measurable COVID-19 impact on mental health among healthy middle-aged individuals, arguing against a complete lack of a general effects (
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      Our results also highlight the role of the DMN in attenuating the impact of perceived stress on change in anxiety and depressive symptoms. Abnormal DMN functionality (along with FPCN and salience network dysfunctions) is characteristic of anxiety and depression disorders (
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      Taken together, these findings highlight the need to consider the study of resilience using a person-centered approach wherein relevant contributing factors (psychological, lifestyles, sociocultural, and neurodevelopmental aspects) should ultimately be integrated and where effects of neurobiological markers should be interpreted within this context (
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      Measuring and manipulating brain connectivity with resting state functional connectivity magnetic resonance imaging (fcMRI) and transcranial magnetic stimulation (TMS).
      ,
      • Pascual-Leone A.
      • Freitas C.
      • Oberman L.
      • Horvath J.C.
      • Halko M.
      • Eldaief M.
      • et al.
      Characterizing brain cortical plasticity and network dynamics across the age-span in health and disease with TMS-EEG and TMS-fMRI.
      ,
      • Shafi M.M.
      • Westover M.B.
      • Fox M.D.
      • Pascual-Leone A.
      Exploration and modulation of brain network interactions with noninvasive brain stimulation in combination with neuroimaging.
      ,
      • Shafi M.M.
      • Brandon Westover M.
      • Oberman L.
      • Cash S.S.
      • Pascual-Leone A.
      Modulation of EEG functional connectivity networks in subjects undergoing repetitive transcranial magnetic stimulation.
      ,
      • Abellaneda-Pérez K.
      • Vaqué-Alcázar L.
      • Perellón-Alfonso R.
      • Solé-Padullés C.
      • Bargalló N.
      • Salvador R.
      • et al.
      Multifocal transcranial direct current stimulation modulates resting-state functional connectivity in older adults depending on the induced current density.
      ). Furthermore, the brain responses evoked by stimulation may hold predictive value regarding clinical and behavioral outcomes (
      • Abellaneda-Pérez K.
      • Vaqué-Alcázar L.
      • Solé-Padullés C.
      • Bartrés-Faz D.
      Combining non-invasive brain stimulation with functional magnetic resonance imaging to investigate the neural substrates of cognitive aging.
      ). Hence, such experimentally controlled approaches could be integrated with other factors to predict an individual’s risk of experiencing negative mental health impact in the event of unexpected and sustained stressors (
      • Pascual-Leone A.
      • Bartres-Faz D.
      Human brain resilience: A call to action.
      ).
      Our study is not without limitations. First, we used the PHQ-4 as the primary outcome measure to maximize the fact that we had assessments across all the time points (pre-pandemic and during the pandemic) for this variable, but we acknowledge that it may entail constraints in terms of the sensitivity and specificity of the mental health symptoms assessed. Second, the included sample exhibits particular characteristics, in part because of the recruitment method used, notably the fact that the sample is composed of individuals with high interest in their own brain health, with an underrepresentation of low mental health rates and with a high educational level. Hence, even though the lack of effects for education in our study aligns with previous reports (
      • Vannini P.
      • Gagliardi G.P.
      • Kuppe M.
      • Dossett M.L.
      • Donovan N.J.
      • Gatchel J.R.
      • et al.
      Stress, resilience, and coping strategies in a sample of community-dwelling older adults during COVID-19 [published correction appears in J Psychiatr Res. 2021;142:167-170].
      ), findings might have differed if the sample had included a greater representation of individuals with no or fewer educational qualifications (
      • Fancourt D.
      • Steptoe A.
      • Bu F.
      Trajectories of anxiety and depressive symptoms during enforced isolation due to COVID-19 in England: A longitudinal observational study.
      ). Third, many other variables including individual dispositional factors, health- and family-related issues, and environmental and cultural aspects possibly affecting the investigated outcome were not considered here [see Discussion in (
      • Bongelli R.
      • Canestrari C.
      • Fermani A.
      • Muzi M.
      • Riccioni I.
      • Bertolazzi A.
      • Burro R.
      Associations between personality traits, intolerance of uncertainty, coping strategies, and stress in Italian frontline and non-frontline HCWs during the Covid-19 pandemic—A multi-group path-analysis.
      )]. In this regard, the availability of pre-pandemic information regarding perceived stress would have been useful to better characterize the COVID-19–related impact on this variable of interest. Particularly, information about ethnicity and race was not included in our analyses because we did not collect information about ethnicity and because our population was homogeneous, mostly considering themselves Caucasian or White (i.e., 94.39%). It should also be noted that owing to our inclusion criteria, our results might not generalize to samples of patients or those individuals exhibiting higher pre-pandemic anxiety and depression scores. Finally, the analytical approach was neither specifically designed to formally test for changes across temporal pandemic stages nor designed to investigate group trajectories potentially contributing to longitudinal individual differences (
      • Gambin M.
      • Oleksy T.
      • Sękowski M.
      • Wnuk A.
      • Woźniak-Prus M.
      • Kmita G.
      • et al.
      Pandemic trajectories of depressive and anxiety symptoms and their predictors: Five-wave study during the COVID-19 pandemic in Poland [published online ahead of print Dec 20].
      ,
      • Chen S.
      • Bi K.
      • Sun P.
      • Bonanno G.A.
      Psychopathology and resilience following strict COVID-19 lockdowns in Hubei, China: Examining person- and context-level predictors for longitudinal trajectories.
      ), which will be the matter of future investigations.
      In conclusion, leveraging data from a longitudinal prospective study including a large sample of healthy middle-aged individuals and multiple data points spanning from 2 years prior to the SARS-CoV-2 outbreak until the end of the first year of the pandemic, we have been able to elucidate how basic sociodemographic measures, psychological factors, and neurobiological characteristics relate to a general measure of mental health impact. FPCN and DMN segregation/integration status was found to modulate the influence of psychological factors, acting through distinct pathways, and conferring interindividual differences in vulnerability versus resilience regarding the change in psychological distress associated with the COVID-19 pandemic.

      Acknowledgments and Disclosures

      This work was supported by a grant from the Agència de Gestió d’Ajuts Universitaris I de Recerca (AGAUR) PANDÈMIES 2020 (Grant No. 2020PANDE00043 [to DB-F]) and a grant from La Marató de TV3 MARATÓ 2020 COVID-19 (Grant No. 202129-31). It was supported in part by the Spanish Ministry of Science, Innovation and Universities (Grant No. RTI2018-095181-B-C21 [to DB-F]) and an Institut Català de Recerca i Estudis Avançats (ICREA) Academia 2019 grant award (to DB-F). This research received partial funding from La Caixa Foundation (Grant No. LCF/PR/PR16/11110004) and from the Institut Guttmann and Fundació Abertis. LV-A was supported by a Margarita Salas postdoctoral fellowship. LM-P was supported by a fellowship associated with the Ministerio de Ciencia e Innovación/Fondo Europeo de Desarrollo Regional RTI2018-095181-B-C21 grant (Grant No. PRE2019-089449 [to LV-A]). KA-P and IB-M were supported by 2 postdoctoral fellowships related to PANDÈMIES 2020 (AGAUR) (Grant No. 2020PANDE00043 [to DB-F]). JMT was partly supported by AGAUR (Grant No. 2018 PROD 00172), Fundació Joan Ribas Araquistain, and La Marató de TV3 Fundation (Grant No. 201735.10). This research was also supported by the Government of Catalonia (Grant No. 2017SGR748).
      We are indebted to the Magnetic Resonance Imaging Core Facility. In addition, we acknowledge support from the Spanish Ministry of Science and Innovation and State Research Agency through the Centro de Excelencia Severo Ochoa 2019-2023 Program (Grant No. CEX2018-000806-S) and support from the Generalitat de Catalunya through the CERCA Program.
      AP-L is listed as an inventor on several issued and pending patents on the real-time integration of noninvasive brain stimulation with electroencephalography and MRI. He is cofounder of Linus Health and TI Solutions. AG and serves on the scientific advisory boards for Starlab Neuroscience, Magstim Inc., Nexstim, and MedRhythms. All other authors report no biomedical financial interests or potential conflicts of interest.

      Supplementary Material

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