Institute of Psychology, Chinese Academy of Sciences

A central goal in systems neuroscience is the parcellation of the cerebral cortex into discrete neurobiological "atoms". Resting-state functional magnetic resonance imaging (rs-fMRI) offers the possibility of in vivo human cortical parcellation. Almost all previous parcellations relied on 1 of 2 approaches. The local gradient approach detects abrupt transitions in functional connectivity patterns. These transitions potentially reflect cortical areal boundaries defined by histology or visuotopic fMRI. By contrast, the global similarity approach clusters similar functional connectivity patterns regardless of spatial proximity, resulting in parcels with homogeneous (similar) rs-fMRI signals. Here, we propose a gradient-weighted Markov Random Field (gwMRF) model integrating local gradient and global similarity approaches. Using task-fMRI and rs-fMRI across diverse acquisition protocols, we found gwMRF parcellations to be more homogeneous than 4 previously published parcellations. Furthermore, gwMRF parcellations agreed with the boundaries of certain cortical areas defined using histology and visuotopic fMRI. Some parcels captured subareal (somatotopic and visuotopic) features that likely reflect distinct computational units within known cortical areas. These results suggest that gwMRF parcellations reveal neurobiologically meaningful features of brain organization and are potentially useful for future applications requiring dimensionality reduction of voxel-wise fMRI data. Multiresolution parcellations generated from 1489 participants are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal).

Although it is being successfully implemented for exploration of the genome, discovery science has eluded the functional neuroimaging community. The core challenge remains the development of common paradigms for interrogating the myriad functional systems in the brain without the constraints of a priori hypotheses. Resting-state functional MRI (R-fMRI) constitutes a candidate approach capable of addressing this challenge. Imaging the brain during rest reveals large-amplitude spontaneous low-frequency (<0.1 Hz) fluctuations in the fMRI signal that are temporally correlated across functionally related areas. Referred to as functional connectivity, these correlations yield detailed maps of complex neural systems, collectively constituting an individual's "functional connectome." Reproducibility across datasets and individuals suggests the functional connectome has a common architecture, yet each individual's functional connectome exhibits unique features, with stable, meaningful interindividual differences in connectivity patterns and strengths. Comprehensive mapping of the functional connectome, and its subsequent exploitation to discern genetic influences and brain-behavior relationships, will require multicenter collaborative datasets. Here we initiate this endeavor by gathering R-fMRI data from 1,414 volunteers collected independently at 35 international centers. We demonstrate a universal architecture of positive and negative functional connections, as well as consistent loci of inter-individual variability. Age and sex emerged as significant determinants. These results demonstrate that independent R-fMRI datasets can be aggregated and shared. High-throughput R-fMRI can provide quantitative phenotypes for molecular genetic studies and biomarkers of developmental and pathological processes in the brain. To initiate discovery science of brain function, the 1000 Functional Connectomes Project dataset is freely accessible at www.nitrc.org/projects/fcon_1000/.

COVID-19 (Corona Virus Disease 2019) has significantly resulted in a large number of psychological consequences. The aim of this study is to explore the impacts of COVID-19 on people's mental health, to assist policy makers to develop actionable policies, and help clinical practitioners (e.g., social workers, psychiatrists, and psychologists) provide timely services to affected populations. We sample and analyze the Weibo posts from 17,865 active Weibo users using the approach of Online Ecological Recognition (OER) based on several machine-learning predictive models. We calculated word frequency, scores of emotional indicators (e.g., anxiety, depression, indignation, and Oxford happiness) and cognitive indicators (e.g., social risk judgment and life satisfaction) from the collected data. The sentiment analysis and the paired sample t-test were performed to examine the differences in the same group before and after the declaration of COVID-19 on 20 January, 2020. The results showed that negative emotions (e.g., anxiety, depression and indignation) and sensitivity to social risks increased, while the scores of positive emotions (e.g., Oxford happiness) and life satisfaction decreased. People were concerned more about their health and family, while less about leisure and friends. The results contribute to the knowledge gaps of short-term individual changes in psychological conditions after the outbreak. It may provide references for policy makers to plan and fight against COVID-19 effectively by improving stability of popular feelings and urgently prepare clinical practitioners to deliver corresponding therapy foundations for the risk groups and affected people.

Resting-state fMRI (RS-fMRI) has been drawing more and more attention in recent years. However, a publicly available, systematically integrated and easy-to-use tool for RS-fMRI data processing is still lacking. We developed a toolkit for the analysis of RS-fMRI data, namely the RESting-state fMRI data analysis Toolkit (REST). REST was developed in MATLAB with graphical user interface (GUI). After data preprocessing with SPM or AFNI, a few analytic methods can be performed in REST, including functional connectivity analysis based on linear correlation, regional homogeneity, amplitude of low frequency fluctuation (ALFF), and fractional ALFF. A few additional functions were implemented in REST, including a DICOM sorter, linear trend removal, bandpass filtering, time course extraction, regression of covariates, image calculator, statistical analysis, and slice viewer (for result visualization, multiple comparison correction, etc.). REST is an open-source package and is freely available at http://www.restfmri.net.

OBJECTIVE: To review the evidence about the prevalence and determinants of non-psychotic common perinatal mental disorders (CPMDs) in World Bank categorized low- and lower-middle-income countries. METHODS: Major databases were searched systematically for English-language publications on the prevalence of non-psychotic CPMDs and on their risk factors and determinants. All study designs were included. FINDINGS: Thirteen papers covering 17 low- and lower-middle-income countries provided findings for pregnant women, and 34, for women who had just given birth. Data on disorders in the antenatal period were available for 9 (8%) countries, and on disorders in the postnatal period, for 17 (15%). Weighted mean prevalence was 15.6% (95% confidence interval, CI: 15.4-15.9) antenatally and 19.8% (19.5-20.0) postnatally. Risk factors were: socioeconomic disadvantage (odds ratio [OR] range: 2.1-13.2); unintended pregnancy (1.6-8.8); being younger (2.1-5.4); being unmarried (3.4-5.8); lacking intimate partner empathy and support (2.0-9.4); having hostile in-laws (2.1-4.4); experiencing intimate partner violence (2.11-6.75); having insufficient emotional and practical support (2.8-6.1); in some settings, giving birth to a female (1.8-2.6), and having a history of mental health problems (5.1-5.6). Protective factors were: having more education (relative risk: 0.5; P = 0.03); having a permanent job (OR: 0.64; 95% CI: 0.4-1.0); being of the ethnic majority (OR: 0.2; 95% CI: 0.1-0.8) and having a kind, trustworthy intimate partner (OR: 0.52; 95% CI: 0.3-0.9). CONCLUSION: CPMDs are more prevalent in low- and lower-middle-income countries, particularly among poorer women with gender-based risks or a psychiatric history.

The network architecture of functional connectivity within the human brain connectome is poorly understood at the voxel level. Here, using resting state functional magnetic resonance imaging data from 1003 healthy adults, we investigate a broad array of network centrality measures to provide novel insights into connectivity within the whole-brain functional network (i.e., the functional connectome). We first assemble and visualize the voxel-wise (4 mm) functional connectome as a functional network. We then demonstrate that each centrality measure captures different aspects of connectivity, highlighting the importance of considering both global and local connectivity properties of the functional connectome. Beyond "detecting functional hubs," we treat centrality as measures of functional connectivity within the brain connectome and demonstrate their reliability and phenotypic correlates (i.e., age and sex). Specifically, our analyses reveal age-related decreases in degree centrality, but not eigenvector centrality, within precuneus and posterior cingulate regions. This implies that while local or (direct) connectivity decreases with age, connections with hub-like regions within the brain remain stable with age at a global level. In sum, these findings demonstrate the nonredundancy of various centrality measures and raise questions regarding their underlying physiological mechanisms that may be relevant to the study of neurodegenerative and psychiatric disorders.

Two messages were presented dichotically and subjects were asked to “shadow” whatever they heard on one ear. Somewhere in the middle the two passages were switched to the opposite ears. Subjects occasionally repeated one or two words, at the break, from the wrong ear, but never transferred to it for longer than this. The higher the transition probabilities in the passage the more likely they were to do this. One explanation might be that the “selective filter” (Broadbent, 1958) acts by selectively raising thresholds for signals from the rejected sources rather than acting as an all-or-none barrier.

We conducted preregistered replications of 28 classic and contemporary published findings, with protocols that were peer reviewed in advance, to examine variation in effect magnitudes across samples and settings. Each protocol was administered to approximately half of 125 samples that comprised 15,305 participants from 36 countries and territories. Using the conventional criterion of statistical significance ( p &lt; .05), we found that 15 (54%) of the replications provided evidence of a statistically significant effect in the same direction as the original finding. With a strict significance criterion ( p &lt; .0001), 14 (50%) of the replications still provided such evidence, a reflection of the extremely high-powered design. Seven (25%) of the replications yielded effect sizes larger than the original ones, and 21 (75%) yielded effect sizes smaller than the original ones. The median comparable Cohen’s ds were 0.60 for the original findings and 0.15 for the replications. The effect sizes were small (&lt; 0.20) in 16 of the replications (57%), and 9 effects (32%) were in the direction opposite the direction of the original effect. Across settings, the Q statistic indicated significant heterogeneity in 11 (39%) of the replication effects, and most of those were among the findings with the largest overall effect sizes; only 1 effect that was near zero in the aggregate showed significant heterogeneity according to this measure. Only 1 effect had a tau value greater than .20, an indication of moderate heterogeneity. Eight others had tau values near or slightly above .10, an indication of slight heterogeneity. Moderation tests indicated that very little heterogeneity was attributable to the order in which the tasks were performed or whether the tasks were administered in lab versus online. Exploratory comparisons revealed little heterogeneity between Western, educated, industrialized, rich, and democratic (WEIRD) cultures and less WEIRD cultures (i.e., cultures with relatively high and low WEIRDness scores, respectively). Cumulatively, variability in the observed effect sizes was attributable more to the effect being studied than to the sample or setting in which it was studied.

thesis that consciousness is a process in brain is put forward as a reasonable scientific hypothesis, not to be dismissed on logical grounds alone. conditions under which two sets of observations are treated as observations of same process, rather than as observations of two independent correlated processes, are discussed. It is suggested that we can identify consciousness with a given pattern of brain activity, if we can explain subject's introspective observations by reference to brain processes with which they are correlated. It is argued that problem of providing a physiological explanation of introspective observations is made to seem more difficult than it really is by ‘phenomenological fallacy’, mistaken idea that descriptions of appearances of things are descriptions of actual state of affairs in a mysterious internal environment.

A robust automatic micro-expression recognition system would have broad applications in national safety, police interrogation, and clinical diagnosis. Developing such a system requires high quality databases with sufficient training samples which are currently not available. We reviewed the previously developed micro-expression databases and built an improved one (CASME II), with higher temporal resolution (200 fps) and spatial resolution (about 280×340 pixels on facial area). We elicited participants' facial expressions in a well-controlled laboratory environment and proper illumination (such as removing light flickering). Among nearly 3000 facial movements, 247 micro-expressions were selected for the database with action units (AUs) and emotions labeled. For baseline evaluation, LBP-TOP and SVM were employed respectively for feature extraction and classifier with the leave-one-subject-out cross-validation method. The best performance is 63.41% for 5-class classification.

BACKGROUND: Although guided self-help for depression and anxiety disorders has been examined in many studies, it is not clear whether it is equally effective as face-to-face treatments.MethodWe conducted a meta-analysis of randomized controlled trials in which the effects of guided self-help on depression and anxiety were compared directly with face-to-face psychotherapies for depression and anxiety disorders. A systematic search in bibliographical databases (PubMed, PsycINFO, EMBASE, Cochrane) resulted in 21 studies with 810 participants. RESULTS: The overall effect size indicating the difference between guided self-help and face-to-face psychotherapy at post-test was d=-0.02, in favour of guided self-help. At follow-up (up to 1 year) no significant difference was found either. No significant difference was found between the drop-out rates in the two treatments formats. CONCLUSIONS: It seems safe to conclude that guided self-help and face-to-face treatments can have comparable effects. It is time to start thinking about implementation in routine care.

Major depressive disorder (MDD) is common and disabling, but its neuropathophysiology remains unclear. Most studies of functional brain networks in MDD have had limited statistical power and data analysis approaches have varied widely. The REST-meta-MDD Project of resting-state fMRI (R-fMRI) addresses these issues. Twenty-five research groups in China established the REST-meta-MDD Consortium by contributing R-fMRI data from 1,300 patients with MDD and 1,128 normal controls (NCs). Data were preprocessed locally with a standardized protocol before aggregated group analyses. We focused on functional connectivity (FC) within the default mode network (DMN), frequently reported to be increased in MDD. Instead, we found decreased DMN FC when we compared 848 patients with MDD to 794 NCs from 17 sites after data exclusion. We found FC reduction only in recurrent MDD, not in first-episode drug-naïve MDD. Decreased DMN FC was associated with medication usage but not with MDD duration. DMN FC was also positively related to symptom severity but only in recurrent MDD. Exploratory analyses also revealed alterations in FC of visual, sensory-motor, and dorsal attention networks in MDD. We confirmed the key role of DMN in MDD but found reduced rather than increased FC within the DMN. Future studies should test whether decreased DMN FC mediates response to treatment. All R-fMRI indices of data contributed by the REST-meta-MDD consortium are being shared publicly via the R-fMRI Maps Project.

The human visual system contains an array of topographically organized regions. Identifying these regions in individual subjects is a powerful approach to group-level statistical analysis, but this is not always feasible. We addressed this limitation by generating probabilistic maps of visual topographic areas in 2 standardized spaces suitable for use with adult human brains. Using standard fMRI paradigms, we identified 25 topographic maps in a large population of individual subjects (N = 53) and transformed them into either a surface- or volume-based standardized space. Here, we provide a quantitative characterization of the inter-subject variability within and across visual regions, including the likelihood that a given point would be classified as a part of any region (full probability map) and the most probable region for any given point (maximum probability map). By evaluating the topographic organization across the whole of visual cortex, we provide new information about the organization of individual visual field maps and large-scale biases in visual field coverage. Finally, we validate each atlas for use with independent subjects. Overall, the probabilistic atlases quantify the variability of topographic representations in human cortex and provide a useful reference for comparing data across studies that can be transformed into these standard spaces.

BACKGROUND: Depression is a highly prevalent mood disorder that is characterised by persistent low mood, diminished interest, and loss of pleasure. Music therapy may be helpful in modulating moods and emotions. An update of the 2008 Cochrane review was needed to improve knowledge on effects of music therapy for depression. OBJECTIVES: 1. To assess effects of music therapy for depression in people of any age compared with treatment as usual (TAU) and psychological, pharmacological, and/or other therapies.2. To compare effects of different forms of music therapy for people of any age with a diagnosis of depression. SEARCH METHODS: We searched the following databases: the Cochrane Common Mental Disorders Controlled Trials Register (CCMD-CTR; from inception to 6 May 2016); the Cochrane Central Register of Controlled Trials (CENTRAL; to 17 June 2016); Thomson Reuters/Web of Science (to 21 June 2016); Ebsco/PsycInfo, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Embase, and PubMed (to 5 July 2016); the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP), ClinicalTrials.gov, the National Guideline Clearing House, and OpenGrey (to 6 September 2016); and the Digital Access to Research Theses (DART)-Europe E-theses Portal, Open Access Theses and Dissertations, and ProQuest Dissertations and Theses Database (to 7 September 2016). We checked reference lists of retrieved articles and relevant systematic reviews and contacted trialists and subject experts for additional information when needed. We updated this search in August 2017 and placed potentially relevant studies in the "Awaiting classification" section; we will incorporate these into the next version of this review as appropriate. SELECTION CRITERIA: All randomised controlled trials (RCTs) and controlled clinical trials (CCTs) comparing music therapy versus treatment as usual (TAU), psychological therapies, pharmacological therapies, other therapies, or different forms of music therapy for reducing depression. DATA COLLECTION AND ANALYSIS: statistic. MAIN RESULTS: We included in this review nine studies involving a total of 421 participants, 411 of whom were included in the meta-analysis examining short-term effects of music therapy for depression. Concerning primary outcomes, we found moderate-quality evidence of large effects favouring music therapy and TAU over TAU alone for both clinician-rated depressive symptoms (SMD -0.98, 95% CI -1.69 to -0.27, 3 RCTs, 1 CCT, n = 219) and patient-reported depressive symptoms (SMD -0.85, 95% CI -1.37 to -0.34, 3 RCTs, 1 CCT, n = 142). Music therapy was not associated with more or fewer adverse events than TAU. Regarding secondary outcomes, music therapy plus TAU was superior to TAU alone for anxiety and functioning. Music therapy and TAU was not more effective than TAU alone for improved quality of life (SMD 0.32, 95% CI -0.17 to 0.80, P = 0.20, n = 67, low-quality evidence). We found no significant discrepancies in the numbers of participants who left the study early (OR 0.49, 95% CI 0.14 to 1.70, P = 0.26, 5 RCTs, 1 CCT, n = 293, moderate-quality evidence). Findings of the present meta-analysis indicate that music therapy added to TAU provides short-term beneficial effects for people with depression if compared to TAU alone. Additionally, we are uncertain about the effects of music therapy versus psychological therapies on clinician-rated depression (SMD -0.78, 95% CI -2.36 to 0.81, 1 RCT, n = 11, very low-quality evidence), patient-reported depressive symptoms (SMD -1.28, 95% CI -3.75 to 1.02, 4 RCTs, n = 131, low-quality evidence), quality of life (SMD -1.31, 95% CI - 0.36 to 2.99, 1 RCT, n = 11, very low-quality evidence), and leaving the study early (OR 0.17, 95% CI 0.02 to 1.49, 4 RCTs, n = 157, moderate-quality evidence). We found no eligible evidence addressing adverse events, functioning, and anxiety. We do not know whether one form of music therapy is better than another for clinician-rated depressive symptoms (SMD -0.52, 95% CI -1.87 to 0.83, 1 RCT, n = 9, very low-quality evidence), patient-reported depressive symptoms (SMD -0.01, 95% CI -1.33 to 1.30, 1 RCT, n = 9, very low-quality evidence), quality of life (SMD -0.24, 95% CI -1.57 to 1.08, 1 RCT, n = 9, very low-quality evidence), or leaving the study early (OR 0.27, 95% CI 0.01 to 8.46, 1 RCT, n = 10). We found no eligible evidence addressing adverse events, functioning, or anxiety. AUTHORS' CONCLUSIONS: Findings of the present meta-analysis indicate that music therapy provides short-term beneficial effects for people with depression. Music therapy added to treatment as usual (TAU) seems to improve depressive symptoms compared with TAU alone. Additionally, music therapy plus TAU is not associated with more or fewer adverse events than TAU alone. Music therapy also shows efficacy in decreasing anxiety levels and improving functioning of depressed individuals.Future trials based on adequate design and larger samples of children and adolescents are needed to consolidate our findings. Researchers should consider investigating mechanisms of music therapy for depression. It is important to clearly describe music therapy, TAU, the comparator condition, and the profession of the person who delivers the intervention, for reproducibility and comparison purposes.

Resting-state functional magnetic resonance imaging (rs-fMRI) offers the opportunity to delineate individual-specific brain networks. A major question is whether individual-specific network topography (i.e., location and spatial arrangement) is behaviorally relevant. Here, we propose a multi-session hierarchical Bayesian model (MS-HBM) for estimating individual-specific cortical networks and investigate whether individual-specific network topography can predict human behavior. The multiple layers of the MS-HBM explicitly differentiate intra-subject (within-subject) from inter-subject (between-subject) network variability. By ignoring intra-subject variability, previous network mappings might confuse intra-subject variability for inter-subject differences. Compared with other approaches, MS-HBM parcellations generalized better to new rs-fMRI and task-fMRI data from the same subjects. More specifically, MS-HBM parcellations estimated from a single rs-fMRI session (10 min) showed comparable generalizability as parcellations estimated by 2 state-of-the-art methods using 5 sessions (50 min). We also showed that behavioral phenotypes across cognition, personality, and emotion could be predicted by individual-specific network topography with modest accuracy, comparable to previous reports predicting phenotypes based on connectivity strength. Network topography estimated by MS-HBM was more effective for behavioral prediction than network size, as well as network topography estimated by other parcellation approaches. Thus, similar to connectivity strength, individual-specific network topography might also serve as a fingerprint of human behavior.

OBJECTIVE: Several hundred studies have shown that depression is associated with an elevated risk of dying at follow-up. It is not clear, however, whether the mechanisms for this association are disease specific, leading to higher mortality in specific patient groups, or generic, resulting in comparable mortality rates in all patient groups as well as in community samples. The authors conducted a comprehensive meta-analysis of prospective studies of community as well as patient samples associating depression at baseline with excess mortality at follow-up. METHOD: The authors conducted systematic searches of PubMed, PsycINFO, and Embase. Studies were included if depression was measured with a standardized instrument and mortality was reported for both depressed and nondepressed participants at follow-up. RESULTS: A total of 293 studies including 1,813,733 participants (135,007 depressed and 1,678,726 nondepressed) from 35 countries were included. The overall unadjusted relative risk of mortality in depressed relative to nondepressed participants was 1.64 (95% CI=1.56-1.76), with high heterogeneity (I2=83, 95% CI=80-84). After adjustment for publication bias, the overall relative risk was reduced to 1.52 (95% CI=1.45-1.59). No strong indications were found that the pooled relative risk was different across the relatively healthy community samples and specific patient samples with heart disease, cancer, kidney disease, or other disease, except for a significantly higher risk in patients with chronic obstructive pulmonary disease (p<0.05). Also, the relative risk was lower when the follow-up period was longer and when the quality of the study was higher. CONCLUSIONS: The authors could confirm the presence of a significant association between depression and excess mortality, although this association may have been overestimated because of publication bias and low study quality. Few indications were found that this association is stronger in community or specific patient samples.

Rumination is strongly and consistently correlated with depression. Although multiple studies have explored the neural correlates of rumination, findings have been inconsistent and the mechanisms underlying rumination remain elusive. Functional brain imaging studies have identified areas in the default mode network (DMN) that appear to be critically involved in ruminative processes. However, a meta-analysis to synthesize the findings of brain regions underlying rumination is currently lacking. Here, we conducted a meta-analysis consisting of experimental tasks that investigate rumination by using Signed Differential Mapping of 14 fMRI studies comprising 286 healthy participants. Furthermore, rather than treat the DMN as a unitary network, we examined the contribution of three DMN subsystems to rumination. Results confirm the suspected association between rumination and DMN activation, specifically implicating the DMN core regions and the dorsal medial prefrontal cortex subsystem. Based on these findings, we suggest a hypothesis of how DMN regions support rumination and present the implications of this model for treating major depressive disorder characterized by rumination.

This research examined the relations of social media addiction to college students' mental health and academic performance, investigated the role of self-esteem as a mediator for the relations, and further tested the effectiveness of an intervention in reducing social media addiction and its potential adverse outcomes. In Study 1, we used a survey method with a sample of college students (N = 232) and found that social media addiction was negatively associated with the students' mental health and academic performance and that the relation between social media addiction and mental health was mediated by self-esteem. In Study 2, we developed and tested a two-stage self-help intervention program. We recruited a sample of college students (N = 38) who met criteria for social media addiction to receive the intervention. Results showed that the intervention was effective in reducing the students’ social media addiction and improving their mental health and academic efficiency. The current studies yielded original findings that contribute to the empirical database on social media addiction and that have important theoretical and practical implications.

Journal Article SELECTIVE ATTENTION IN MAN Get access ANNE. M. TREISMAN, D.Phil. ANNE. M. TREISMAN, D.Phil. Institute of Experimental Psychology, University of Oxford Search for other works by this author on: Oxford Academic PubMed Google Scholar British Medical Bulletin, Volume 20, Issue 1, January 1964, Pages 12–16, https://doi.org/10.1093/oxfordjournals.bmb.a070274 Published: 01 January 1964

BACKGROUND: A pandemic is a very stressful event, especially for highly vulnerable people (e.g., older adults). The purpose of the current study was to investigate the main and interactive relationships of social support and resilience on individual mental health during the COVID-19 pandemic across three age groups: emerging adults, adults, and older adults. METHODS: A survey was conducted with 23,192 participants aged 18-85. Respondents completed a questionnaire, including items on the COVID-19-related support they perceived from different sources, the abbreviated version of the Connor-Davidson Resilience Scale, and the Mental Health Inventory. RESULTS: Latent profile analysis identified five profiles of social support, and the patterns of potential profiles were similar in all groups. However, category distribution in the five profiles was significantly different among the age groups. Furthermore, analysis using the BCH command showed significant differences in mental health among these profiles. Lastly, interactive analyses indicated resilience had a positive relationship with mental health, and social support served as a buffer against the negative impact of low resilience on mental health. CONCLUSIONS: This study provides quantitative evidence for socioemotional selectivity theory (SST) and enables several practical implications for helping different age groups protecting mental health during pandemic.