Disrupted brain gray matter connectome in social anxiety disorder: a novel individualized structural covariance network analysis

Phenotyping approaches grounded in structural network science can offer insights into the neurobiological substrates of psychiatric diseases, but this remains to be clarified at the individual level in social anxiety disorder (SAD). Using a recently developed approach combining probability density estimation and Kullback-Leibler divergence, we constructed single-subject structural covariance networks (SCNs) based on multivariate morphometry (cortical thickness, surface area, curvature, and volume) and quantified their global/nodal network properties using graph-theoretical analysis. We compared network metrics between SAD patients and healthy controls (HC) and analyzed the relationship to clinical characteristics. We also used support vector machine analysis to explore the ability of graph-theoretical metrics to discriminate SAD patients from HC. Globally, SAD patients showed higher global efficiency, shorter characteristic path length, and stronger small-worldness. Locally, SAD patients showed abnormal nodal centrality mainly involving left superior frontal gyrus, right superior parietal lobe, left amygdala, right paracentral gyrus, right lingual, and right pericalcarine cortex. Altered topological metrics were associated with the symptom severity and duration. Graph-based metrics allowed single-subject classification of SAD versus HC with total accuracy of 78.7%. This finding, that the topological organization of SCNs in SAD patients is altered toward more randomized configurations, adds to our understanding of network-level neuropathology in SAD.

Automated summary

Phenotyping approaches grounded in structural network science can provide insights into the underlying neurobiology of psychiatric diseases, particularly at the individual level in social anxiety disorder (SAD). This study used a novel approach to construct single-subject structural covariance networks (SCNs) based on multivariate morphometry and analyzed their global/nodal network properties. The findings revealed altered network organization in SAD patients, with higher global efficiency, shorter characteristic path length, and stronger small-worldness compared to healthy controls. Abnormal nodal centrality was observed in specific brain regions, and these topological metrics were associated with symptom severity and duration. Additionally, graph-based metrics allowed accurate classification of SAD versus healthy controls. These results contribute to our understanding of network-level neuropathology in SAD.