Pre-COVID brain network topology prospectively predicts social anxiety alterations during the COVID-19 pandemic

Abstract

Background: Social anxiety (SA) is a negative emotional response that can lead to mental health issues, which some have experienced during the coronavirus disease 2019 (COVID-19) pandemic. Little attention has been given to the neurobiological mechanisms underlying inter-individual differences in SA alterations related to COVID-19. This study aims to identify neurofunctional markers of COVID-specific SA development.

Methods: 110 healthy participants underwent resting-state magnetic resonance imaging and behavioral tests before the pandemic (T1, October 2019 to January 2020) and completed follow-up behavioral measurements during the pandemic (T2, February to May 2020). We constructed individual functional networks and used graph theoretical analysis to estimate their global and nodal topological properties, then used Pearson correlation and partial least squares correlations examine their associations with COVID-specific SA alterations.

Results: In terms of global network parameters, SA alterations (T2-T1) were negatively related to pre-pandemic brain small-worldness and normalized clustering coefficient. In terms of nodal network parameters, SA alterations were positively linked to a pronounced degree centrality pattern, encompassing both the high-level cognitive networks (dorsal attention network, cingulo-opercular task control network, default mode network, memory retrieval network, fronto-parietal task control network, and subcortical network) and low-level perceptual networks (sensory/somatomotor network, auditory network, and visual network). These findings were robust after controlling for pre-pandemic general anxiety, other stressful life events, and family socioeconomic status, as well as by treating SA alterations as categorical variables.

Conclusions: The individual functional network associated with SA alterations showed a disrupted topological organization with a more random state, which may shed light on the neurobiological basis of COVID-related SA changes at the network level.

Keywords: COVID-19 pandemic; Functional brain network; Graph theory; Psychoradiology; Resting-state fMRI; Social anxiety.

Fig. 1

Workflow of the study. (A) Timeline of data acquisition. Before the COVID-19 pandemic (T1: October 2019 to January 2020), participants underwent brain MRI scanning and completed baseline behavioral measures. During the most severe pandemic period (T2: February 2020 to April 2020), participants were re-contacted for follow-up behavioral testing. 110 subjects were identified as eligible for the study. (B) Construction of 264 × 264 functional connectivity matrix for each subject. (C) Topological graph theory and statistical analyses. We computed both global and nodal metrics for each individual to describe the characteristics of each weighted network, and for each network metric we used the AUC over a range of network sparsity thresholds (0.02: 0.01: 0.33) in subsequent statistical analyses. We used partial correlation to investigate the association between each global metric and SA alterations (T2-T1), and PLSC to determine the degree centrality pattern of nodes linked to SA alterations. Abbreviations: AUC, area under the curve; COVID-19, coronavirus disease 2019; LSAS, Liebowitz Social Anxiety Scale; MRI, magnetic resonance imaging; PLSC, partial least squares correlation; SA, social anxiety; SRLEC, Self-Rating Life Events Checklist; SSS, Socioeconomic Status Scale; SVD, singular value decomposition; TAI, Trait Anxiety Inventory; C_p, clustering coefficient; E_glob, global efficiency; E_loc, local efficiency; L_p, shortest path length; γ, normalized clustering coefficient; λ, normalized shortest path length; σ, small-worldness.

Fig. 2

Relationships between social anxiety alterations and two global metrics of the brain functional network. The y-axis represents the alterations in social anxiety from T1 to T2; the x-axis represents the residuals of the global metrics' AUCs after controlling for age, sex, and mean framewise displacement during scanning. (A) Higher AUC of the small-worldness parameter (σ) is associated with smaller SA alterations. (B) Higher AUC of the normalized clustering coefficient (γ) is associated with smaller SA alterations. Abbreviations: AUC, area under the curve; SA, social anxiety.

Fig. 3

Partial Least Squares Correlation analysis of the relationship between social anxiety alterations and degree centrality of nodes of the brain functional network. (A) SA alterations are positively linked to a latent variable that comprised a degree centrality pattern of the functional network. (B) 40 regions had absolute values of standardized weighting >2, so made a substantial contribution to the identified degree centrality pattern: for 27 of these regions the contribution was positive (red), and for 13 it was negative (green). Abbreviations: SA, social anxiety; L, left; R, right. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)