The COVID-19 pandemic widely and rapidly spread around the world, and it had a profound impact on public mental health (
1–
3). During the COVID-19 crisis, people were exposed to an unprecedented environment of threats and uncertainties. Facing physical and social isolation (as most people were under stay-at-home orders), the uncertainty of infection, and other stressors, people may be especially susceptible to anxiety-related symptoms (
3–
6) and exhibit individualized behavioral and emotional responses in the face of the pandemic-induced changes and restrictions, which were unlikely in daily life before the pandemic. In addition, there may be great individual differences in levels of anxiety related to the pandemic. In these circumstances, it is helpful to explore brain-based predictors of anxiety, which would advance the understanding of the neural basis of anxiety and may have implications for clinical practice.
The functional connectome is like a fingerprint of the brain and shows highly individualized functional connectivity profiles that successfully distinguish individuals with high accuracy (
7). Emerging evidence based on machine-learning approaches suggests that the individualized functional connectome can be used to predict individual differences in cognition (
8), personality (
9), and mental disorder symptoms (
10). However, results from recent studies have been inconsistent on the prediction of the functional connectome on individual anxiety (
11,
12), partly because individual differences in anxiety may not be well measured in the absence of large stressful events and threatening stimuli in daily life. Instead, researchers have tended to provoke anxiety in individuals through experimental manipulation, such as exposure to aversive stimuli (
13). Compared with traditional experimental manipulation, the unprecedented uncertainties and stresses caused by this global health crisis may have higher ecological validity that amplified individual differences in anxiety during the pandemic (
14), which has enabled us to use a considerable number of participants to investigate the neural correlates of both pandemic-related anxiety and daily anxiety, as well as to further identify potential connectome-based neuromarkers. Thus, we hypothesized that the prepandemic functional connectome could predict individuals’ pandemic-related anxiety but may show poor performance for predicting daily anxiety. Moreover, to investigate the continuous effect of the pandemic on mental health and further examine the reliability of the neuromarkers of pandemic-related anxiety, we utilized data from a longitudinal cohort study with brain imaging data and daily anxiety scores collected before the pandemic and pandemic-related anxiety scores collected during the severe and remission periods of the pandemic.
The acute and persistent panic and fear caused by this crisis also resulted in some psychopathology symptoms (
15), and recent national cohort studies have reported that people are more likely to screen positive for mental disorders (e.g., pathological anxiety) during the pandemic (
6,
16). It is possible that the identified connectome-based neural correlates of pandemic-related anxiety have the potential to be used for risk assessment of common mental disorders. To explore this possibility, three independent clinical data sets were applied to investigate whether the neuromarkers of pandemic-related anxiety could be generalized to participants with generalized anxiety disorder, major depression, or schizophrenia.
RESULTS
Behavioral Data
A spatial map of COVID-19 case information and the sample size of the BBP during our pandemic survey are presented in Figure S2 in the
online supplement. The case data were obtained from the National Health Commission of the People’s Republic of China. The number of existing, confirmed COVID-19 cases (N=58,016) peaked on February 17, 2020. The pandemic remained severe during our first online questionnaire collection period from February 22 to 28, 2020, when the number of existing, confirmed cases ranged from 39,919 to 54,965. After this, the peak of the initial COVID-19 outbreak was passed in China, with a gradually decreasing number of confirmed cases. The number of existing, confirmed cases ranged from 557 to 838 during our second online questionnaire collection period from April 24 to May 1, 2020. One-way repeated-measures analysis of variance showed that the STAI-T scores for the BBP increased from baseline to the severe (first pandemic survey) and remission (second pandemic survey) stages of the pandemic (F=24.70, df=2, 970, p<0.001, η
2=0.048;
Figure 2). Bonferroni post hoc tests showed that the STAI-T scores at the severe (p<0.001, 95% CI=0.63, 2.16) and remission (p<0.001, 95% CI=1.49, 3.22) stages of the pandemic were higher than the prepandemic baseline scores and that the STAI-T scores at the remission stage were higher than those at the severe stage (p=0.012, 95% CI=0.16, 1.75), even though the pandemic had greatly improved in China during our second pandemic survey.
Because we lacked baseline STAI-S scores, we used paired-sample t tests, which showed that the STAI-S scores at the second pandemic survey were higher than those at the first pandemic survey (t=2.71, df=485, p=0.007; 95% CI=0.29, 1.81; Cohen’s d=0.112). We collected two open questions about the concerns of COVID-19 infection during the first pandemic and found that individual anxiety scores were positively correlated with self-concern (STAI-T: r=0.140, p<0.001; STAI-S: r=0.156, p<0.001) and family concern (STAI-T: r=0.159, p<0.001; STAI-S: r=0.162, p<0.001).
Prediction of the Individualized Anxiety Score
According to the optimal prediction performance, the prepandemic functional connectome predicted individuals’ STAI-T and STAI-S scores during the severe period of the pandemic using relevance vector regression (STAI-T: r=0.215, p
pt<0.001, mean absolute error=6.97,
Figure 2B; STAI-S: r=0.185, p
pt<0.001, mean absolute error=7.93; see also Figure S3 in the
online supplement). Two other methods showed similar prediction results, as illustrated in Figures S4 and S5 in the
online supplement (elastic net regression: STAI-T: r=0.191, p
pt<0.001, mean absolute error=6.96; STAI-S: r=0.195, p
pt<0.001, mean absolute error=7.84; support vector regression: STAI-T: r=0.215, p
pt<0.001, mean absolute error=6.93; STAI-S: r=0.186, p
pt<0.001, mean absolute error=7.81).
However, the functional connectome showed poor performance for predicting individuals’ daily anxiety, even though anxiety scores were collected immediately after brain scanning. According to the optimal prediction performance, there was no close correlation between the predicted and measured daily anxiety in the BBP data set (STAI-T: r=0.016, ppt>0.05, mean absolute error=6.34; see Figure S3 in the online supplement). The SLIM cohort further confirmed the poor prediction of the functional connectome on daily anxiety (STAI-T: r=0.104, ppt>0.05, mean absolute error=6.57; STAI-S: r=0.048, ppt>0.05, mean absolute error=7.07; see Figure S3 in the online supplement). Similarly, poor predictive performance was also found using elastic net regression and support vector regression (see Figures S4 and S5 in the online supplement).
Consensus Functional Connectivity and External Validation
Given that the functional connectome had a greater advantage in predicting the STAI-T score during the severe period of the COVID-19 pandemic, subsequent analyses focused on the connections at the optimal threshold of feature selection (i.e., p=5 × 10
−4; see Figure S3 in the
online supplement). Only 32 connections occurred in all rounds and were defined as the consensus functional connectivity that also survived correction for multiple comparisons across the whole-brain functional connectivity using the full BBP data set (false discovery rate corrected p<0.05). The correlation coefficients between the consensus functional connectivity and the pandemic-related STAI-T scores ranged from −0.223 to −0.180 after controlling for sex, age, head motion, and baseline STAI-T scores (see Table S3 in the
online supplement).
Figure 3A shows that the consensus functional connectivity primarily included connections between the prefrontal cortex, insula, anterior cingulate cortex, and subcortical nuclei (e.g., thalamus and putamen), as well as the connections between the insula, thalamus, hippocampus, parahippocampal gyrus, and sensorimotor cortex. To generate a simple interpretation of the importance of each node within the consensus network, we used a common measure (i.e., node strength). Node strength is the sum of the weights of links connected to the node (
33). The correlation coefficients between the consensus functional connectivity and anxiety scores were used as the weights of links, and then node strength was computed by summing the absolute value of the correlation coefficients (because all correlation coefficients have negative values; see Table S3 in the
online supplement). Regions with higher node strength were mainly located in the insula, thalamus, prefrontal cortex, anterior cingulate cortex, hippocampus, and parahippocampal gyrus (
Figure 3B).
The strength of the consensus functional connectivity correlated with individuals’ anxiety scores during the remission period of the pandemic (second pandemic survey) after controlling for sex, age, head motion, and baseline anxiety (STAI-T: r=−0.165, p<0.001; STAI-S: r=−0.212, p<0.001;
Figure 2). Moreover, we extracted the consensus functional connectivity from the validation sample and also found that the strength of the consensus functional connectivity correlated with the pandemic-related STAI-S score (r=−0.179, p=0.034) but not significantly with the STAI-T score (r=−0.045, p>0.05).
Mental Disorder Classification
Finally, we tested the clinical relevance of the consensus functional connectivity of pandemic-related anxiety in generalized anxiety disorder, major depression, and schizophrenia. We extracted the consensus functional connectivity from the three clinical data sets and performed nested 10F-CV using binary logistic regression with L2 regularization. The classifier achieved a significant classification performance that distinguished participants with generalized anxiety disorder and matched healthy control subjects (AUC=0.720, ppt1=0.025, ppt2=0.012; accuracy=68.72%, ppt1=0.025, ppt2=0.014; sensitivity=71.40%, specificity=65.00%). However, the classifier showed poor classification performance for major depression (AUC=0.537, ppt1>0.05, ppt2>0.05; accuracy=53.68%, ppt1>0.05, ppt2>0.05; sensitivity=72.20%, specificity=33.13%) and schizophrenia (AUC=0.596, ppt1>0.05, ppt2>0.05; accuracy=59.10%, ppt1>0.05, ppt2>0.05; sensitivity=32.88%, specificity=73.91%).
Discussion
The unexpected pandemic rapidly spread to the entire world and had a pervasive effect on public mental health, especially generally increased feelings of anxiety. We conducted a prospective cohort study and collected anxiety ratings for a considerable number of participants during the severe and remission periods of the COVID-19 pandemic, and these participants also completed brain scanning and baseline anxiety measures before the pandemic. This study provides evidence that the prepandemic individualized functional connectome predicted pandemic-induced anxiety but showed poor performance for predicting daily anxiety. Moreover, the identified consensus functional connectivity of pandemic-related anxiety could distinguish between participants with generalized anxiety disorder and matched healthy control subjects but was not sensitive to the identification of other mental disorders, such as major depression and schizophrenia. These findings suggest that individual anxiety was highly susceptible to the great stress and uncertainty during this unprecedented period, which contributes to identifying reliable neuromarkers of anxiety and could inform particular psychological intervention and clinical practice.
People experienced increased feelings of anxiety during the pandemic, as shown by comparing individuals’ anxiety scores that were collected before and during the severe and remission periods. Differently from everyday life situations, the uncertainty of infection, social and physical isolation, and other stressors caused by COVID-19 aggravated public anxiety and amplified individual differences during these extraordinary circumstances (
3,
14,
34,
35). Although the number of COVID-19 cases dropped sharply between our two surveys and the pandemic had greatly improved, people’s anxiety levels did not decline but slightly increased, suggesting that the pandemic has sustained adverse effects on mental health and that we need to pay attention to the long-term mental health consequences of the COVID-19 pandemic (
15). The amplified individual differences in anxiety also provide a rare opportunity to construct reliable brain-behavior associations to deepen our knowledge of the neural basis of anxiety.
Connectome-based prediction analyses revealed that the prepandemic functional connectome predicted individuals’ anxiety scores during the severe period of the pandemic, and the identified consensus functional connectivity could further capture significant variance of the anxiety scores that were collected during the remission period and was also validated on an independent sample. However, the functional connectome showed poor performance for predicting daily anxiety, even though the anxiety scores were collected immediately after brain scanning. These findings indicate that the intrinsic functional network is more sensitive as a marker of the severity of anxiety induced by real major stressful events, such as this global health crisis, and provide a reasonable explanation in the current debate that the prediction of functional networks on anxiety is not stable (
11,
12). The latter may be caused by the fact that the lack of large and unexpected stressors in daily life weakens the ability to capture actual individual differences in stress-related emotional consequences, such as anxiety.
We further demonstrated that the connectome-based neuromarkers of pandemic-related anxiety distinguished between participants with generalized anxiety disorder and healthy control subjects. The specific clinical extension expands recent behavioral findings that the population prevalence of clinically significant levels of anxiety greatly increased during the pandemic (
6), revealing a link between the neural correlates of pandemic-related anxiety and pathological anxiety, and suggests that the consensus functional connectivity is likely to be a core component of the neural basis of pathological anxiety. However, the consensus functional connectivity was not sensitive to the identification of other mental disorders, such as major depression and schizophrenia. Although previous studies have revealed that there are common and unique neural circuits across the spectrum of depression and anxiety disorders (
36,
37), the clinical extension from the neural correlates of pandemic-related anxiety identified in people without psychiatric illness to those with major depression may be limited because of the different types of psychopathology symptoms in anxiety and depression and also because of sample heterogeneity. Also, the identified neural correlates of pandemic-related anxiety may not involve the connections from the default mode network that are found in the neural circuits of major depression (
38). The classification analysis confirms the potential clinical relevance of the neuromarkers of pandemic-related anxiety and also highlights the higher susceptibility to mental distress (e.g., pathological anxiety) during the pandemic. The anxiety-specific neuromarkers may provide a special contribution to novel clinical practice for stress and anxiety reduction, such as defining connectome-based targets for neuromodulation (
39,
40).
The consensus functional connectivity mainly comprised connections across multiple areas and primarily included two distributed neural circuits. The first neural circuit included the connections between the prefrontal cortex, insula, anterior cingulate cortex, and subcortical nuclei (e.g., thalamus and putamen). The weaker connectivity between the prefrontal cortex and the thalamus, as well as that between the prefrontal cortex, anterior cingulate cortex, and insula, may reflect lower effects of top-down executive control mechanisms (
41) that thereby are unable to successfully inhibit or regulate somatic and autonomic bodily states (
42,
43) and the hyperactivity caused by detecting and filtering salient stressful events (
44), which may account for increased signs of pandemic-related anxiety. The second neural circuit also included weaker connectivity involving the insula and the thalamus but additionally involving the hippocampus, parahippocampal gyrus, and sensorimotor cortex associated with later higher pandemic-related anxiety. This suggests that the interaction between somatic arousal, interoceptive awareness, and adverse event memory may also be predisposing factors for anxiety in stress-provoking situations, such as the COVID-19 pandemic. Additionally, recent findings have revealed that cognitive-behavioral therapy modulated the activity of some core regions (e.g., the prefrontal cortex and anterior cingulate cortex) in our identified neural circuit during panic-related semantic processing in patients with panic disorder (
45), as well as during the processing of cognitive reappraisal of negative self-beliefs in patients with social anxiety disorder (
46). This evidence emphasizes that individuals with high pandemic-related anxiety may benefit from evidence-based therapeutic approaches that promote stress reduction and from cognitive-behavioral strategies that reduce anxiety.
There are several limitations to this study. Anxiety scores were collected from undergraduate students and may not be representative of the general public. Further work will be useful to assess the predictive validity in healthy individuals in the general population, as well as in specific groups such as frontline health care workers, who are fully exposed to anxiety-provoking situations such as COVID-19 and may experience more mental distress (
47). Although we used strict inclusion criteria to ensure that participants from the three undergraduate student data sets had no psychiatric illness or major physical health problems before brain scanning and that no one reported having been diagnosed with COVID-19, the lack of diagnostics during the pandemic may have some potential impact on the inference of our predictive results. In addition, the heterogeneity of the three clinical data sets (e.g., different scanning sites and unmatched demographic information) may be regarded as a limitation to understanding our results for clinical extension. Additionally, people have shown varying degrees of changes in psychopathology symptoms from before the pandemic to during the pandemic (
6,
16). Also, many symptoms will subside for some people but persist for others as the pandemic wanes in the coming months (
35). The examination of which brain features determine individual anxiety changes from baseline to when the outbreak ended is another promising direction for future studies using long-term follow-up.