健常な大学生における社会的不安の機能的接続性を明らかにするコネクトームベース予測モデリング

Social anxiety refers to excessive fear of social situations and is then accompanied by social avoidance behaviours. While the neural mechanisms of social anxiety disorder in clinical populations have been widely investigated, the functional connectivity underlying social anxiety in the nonclinical population remains poorly understood. The present study addressed this gap by employing connectome-based predictive modelling (CPM) to identify resting-state functional connectivity associated with social anxiety in healthy college students. Our findings revealed a social anxiety connectome that contributed to predicting individuals' social anxiety, which mainly includes the connections within the default mode network (DMN) (i.e., positive network) and those between the frontal parietal network (FPN) and visual network (i.e., negative network). Importantly, the robustness and specificity of this connectome were validated by using different brain atlases and cross-validation schemes and controlling for the influence of general anxiety. Moreover, two sub-dimensions of social anxiety, i.e., social distress and social avoidance, showed distinct neural correlates, with social distress correlated with the positive network and social avoidance with the negative network. Together, these findings provide novel insights into the neural basis of social anxiety in nonclinical populations, highlighting specific functional connectivity associated with different facets of social anxiety.