Neural Substrates of Vulnerability to Anxiety in Response to Stressful Life Events: A COVID-19 Study

It has been over a year since countries around the world implemented measures in response to the rapid spread of the coronavirus disease 2019 (COVID-19), ranging from travel bans, school closures, assembly restrictions, and curfews to full lockdowns. Many countries are currently in a second or third lockdown in response to additional “waves” of the virus. The past year has been an unprecedented period of disruption to our lives and livelihoods. The ongoing COVID-19 pandemic has had a tremendous impact on mental health, with the first surveys estimating the experience of anxiety to be at least three to four times as prevalent compared with prepandemic circumstances in the general population, with similar estimates for depression, posttraumatic stress disorder, and alcohol/drug use (1–3). Especially young people face a generation-defining disruption that will have a multifaceted, long-term impact on their lives.

Not surprisingly, anxiety has been among the mental illnesses that have shown the highest rise in response to the pandemic (1–4). Anxiety and worries related to being infected or the infection of family members, overrun health care systems, job losses and loss of income, disruptions in education, and social isolation are major contributors to the overall rise in anxiety in the population, both at subthreshold and clinical levels. These observed increases in symptoms of mental illness in general are likely sustained effects that will outlive the pandemic.

Some individuals may be more vulnerable to the psychological impact of the pandemic than others. Specific risk groups include COVID-19 survivors, health care workers, and quarantined individuals (4, 5). A recent meta-analysis estimated that the probability of having been diagnosed with any psychiatric illness 14–90 days after a COVID-19 diagnosis was 18.1%, significantly higher than for all control health events (4). In addition, other factors may contribute to the vulnerability of experiencing stress and anxiety in response to the pandemic outbreak, such as dispositional traits, perceived social support, higher levels of distress prior to the pandemic outbreak, and prepandemic cognitive and brain functioning.

In this issue of the Journal, He and colleagues (6) investigate whether prepandemic functional brain connectivity during rest can predict increases in anxiety levels in response to the COVID-19 crisis in 589 undergraduate students at the Southwest University in China. The authors used the COVID-19 pandemic as an ecologically valid model of induction of anxiety in response to stressful life events, likely inducing greater increases in anxiety levels compared with commonly used experimental manipulation of anxiety and stress. Trait levels of anxiety were measured prepandemic (at the time of scanning), as well as during (February 22–28, 2020) and just after (April 24 to May 1, 2020) the first lockdown in Wuhan, China. Machine learning was used to evaluate the predictive value of the functional connectome (i.e., pairwise resting-state functional connectivity measures) on “daily anxiety” (i.e., prepandemic anxiety levels at the time of scanning) and “pandemic-related anxiety” (i.e., anxiety levels during and just after the lockdown).

The authors found that self-reported trait levels of anxiety (State-Trait Anxiety Inventory [STAI] trait scores) significantly increased from the prepandemic period to the lockdown stage of the pandemic (first pandemic survey). Importantly, self-reported state and trait levels of anxiety were even higher in the second pandemic survey after the first lockdown was lifted compared with the lockdown stage, showing the sustained psychological impact of COVID-19 in these university students. Pandemic-related anxiety could be predicted by the prepandemic functional connectome (based on STAI-trait scores: r=0.215, p_{permutation test} <0.001, mean absolute error=6.97; based on STAI-state scores: r=0.185, p_{permutation test} <0.001, mean absolute error=7.93). Conversely, the functional connectome showed poor performance for predicting students’ daily anxiety (i.e., trait anxiety scores that were collected at the time of brain scanning; based on STAI-trait scores: r=0.016, p_{permutation test} >0.05, mean absolute error=6.34). This latter observation may be due to limited variation in anxiety scores prepandemic, given that the sample consisted of university students without any diagnosis of mental disorders at baseline. Indeed, larger variation in anxiety scores was observed in the severe and remission pandemic stages compared with the baseline survey (see Figure 2 in the article), showing that the unprecedented uncertainties and stresses caused by this global health crisis may amplify individual differences in anxiety during the pandemic.

Given that predictive modeling using machine learning methods is vulnerable to overfitting and, hence, to an overestimation of predictive performance especially in smaller samples (7), a strength of this study is the extensive validation of the model in independent samples. He and colleagues evaluated whether the model based on prepandemic brain functional connectivity measures (discovery sample) would generalize to predict anxiety scores (STAI scores) in undergraduate students from two independent samples. The first independent “validation” sample included prepandemic resting-state functional connectivity measures and STAI anxiety scores assessed during the lockdown (N=149). The second independent sample, Southwest University Longitudinal Imaging Multimodal Project (SLIM), also included prepandemic functional connectivity measures, as well as prepandemic STAI scores (i.e., daily anxiety). The brain connectivity features that were identified to contribute most to the prediction of lockdown STAI trait and state scores in the discovery sample were also correlated with lockdown STAI state anxiety scores (r=−0.179, p=0.034) but not STAI trait anxiety scores (r=−0.045, p>0.05) in the validation sample. In line with the findings in the discovery sample, the brain connectivity features were poor predictors of daily anxiety in the independent SLIM sample (STAI-trait, r=0.104, p_{permutation test} >0.05, mean absolute error=6.57; STAI-state, r=0.048, p_{permutation test} >0.05, mean absolute error=7.07).

To test the clinical relevance of the ability of the prepandemic brain functional connectome to predict pandemic-related anxiety, the authors additionally evaluated whether the identified functional connectome features could distinguish between individuals with a mental disorder and healthy control subjects in independent samples of individuals with generalized anxiety disorder (N=25; healthy comparison, N=18), major depressive disorder (N=282; healthy comparison, N=254), and schizophrenia (N=26; healthy comparison, N=46). Interestingly, models based on the prepandemic functional connectome measures identified in the discovery sample were able to classify individuals with generalized anxiety disorder versus healthy comparison subjects (area under the receiver operating characteristic curve [AUC]=0.720) but not those with major depressive disorder versus healthy comparison subjects (AUC=0.537) or those with schizophrenia versus healthy comparison subjects (AUC=0.596).

The findings by He and colleagues suggest that the functional connections between brain regions measured during rest represent vulnerability markers for experiencing pronounced anxiety in response to stressful life events such as a global pandemic. The brain functional connectivity markers were predictive of self-reported dimensional levels of anxiety, likely including both subthreshold and full-threshold levels, measured up to 8 months after the brain scans, as well as a clinical diagnosis of generalized anxiety disorder, indicating that the identified functional connectome measures may be trait markers of anxiety. This is consistent with studies showing that resting-state functional brain network measures are relatively stable in individuals over time (e.g., Gratton et al. [8]; however, see also Geerligs et al. [9]). If the identified brain functional connectome measures indeed represent preexisting vulnerability markers for anxiety, they could be employed to identify individuals who are at risk for high anxiety in response to real stressful life events in order to provide support in an early stage of, for example, a pandemic. However, given the logistics and costs involved in brain scanning, it would of course not be a practical goal to scan everyone in the general population in order to identify who may experience mental health issues during a global pandemic. Brain scanning could be limited to people with other predisposing risk factors for clinical levels of anxiety (e.g., subthreshold anxiety, history of mental illness). However, for such screening even to be considered, it would be crucial to determine how the predictive performance of functional connectivity measures compares to models based on measures that are easier and cheaper to collect (e.g., clinical symptoms, history of mental illness, personality, other psychological factors).

Even though the findings of this study do not have clear, direct clinical translation, they provide important insights into the neural basis of anxiety in general and neural vulnerability for anxiety in response to stressful life events in specific. By using a consensus approach retaining only those functional connectome features that were selected in each cross-validation fold by feature selection, the authors identified 32 functional connections that contributed most to the prediction of anxiety scores in the discovery sample. These primarily included connections within two networks: connections between the prefrontal cortex, insula, anterior cingulate cortex, and subcortical nuclei and connections between the insula, thalamus, hippocampus, parahippocampal gyrus, and sensorimotor cortex. The insula, thalamus, prefrontal cortex, anterior cingulate cortex, hippocampus, and parahippocampal gyrus showed the highest node strength. This is consistent with meta-analyses showing an important role for functional connectivity of these regions in association with anxiety, including generalized anxiety disorder (10). Perhaps surprisingly, amygdala connectivity was not identified as an important contributor to the predictor of pandemic-related anxiety (10). Interestingly, individuals with major depressive disorder or schizophrenia could not be distinguished from healthy control subjects based on functional connections of these regions even though alterations in resting-state functional connectivity of many of these regions has also been implicated in major depressive disorder and schizophrenia (11, 12) and there is a high rate of comorbidity between major depressive disorder and generalized anxiety disorder (13). This suggests some specificity of the unique combination of connectivity patterns among these regions for dimensionsal measures of anxiety.

In summary, He and colleagues used unique resting-state functional MRI and anxiety data obtained before, during, and after the severe stage of the COVID-19 pandemic in China to show that prepandemic brain functional connectome measures were predictive of self-reported anxiety in response to a stressful life event. Using the pandemic as an ecologically valid model of anxiety induction, their findings enhance our understanding of neural substrates of vulnerability to anxiety.