Identifying a structural brain network for social anxiety: connectome-based predictive modeling and network analyses in a transdiagnostic sample

Abstract Trait social anxiety (SA) is continuous and can manifest across the entire spectrum of mental health, from subclinical levels in healthy controls to burdensome manifestations in major depressive disorder and finally full-blown disorders like social anxiety disorder (SAD). Although it has been proposed that SA is associated with deviations in the structural connectome, there is little research on the SA spectrum and its relation to abnormalities in brain structural networks. In this study, we aim to identify a structural brain network for SA across diagnostic groups using a transdiagnostic sample and connectome-based predictive modeling (CPM) to provide insight on neurostructural underpinnings of the SA spectrum. We collected magnetic resonance imaging (MRI) and clinical data of N = 160 participants across an SA spectrum. Diffusion-weighted and T1-weighted structural images were used to reconstruct each participant’s structural connectome. We applied CPM to identify a structural brain network that predicts SA across diagnostic groups. We further analyzed the structural connectome to investigate hubs and differences in global graph metrics between participants with and without SAD (noSAD n = 96, SAD n = 64). CPM revealed a brain network with 46 edges predicting SA across the SA spectrum with mean r = 0.141 ( CI = [0.12, 0.2], p = 0.003). Hubs were located in the left pericalcarine, inferior-parietal, superior-parietal, right lateral-occipital cortex and pars orbitalis. This study provides first evidence for a shared neurobiological foundation of SA across diagnostic groups and points towards a transdiagnostic relevance of SA and the related brain network.