Inter-Network Effective Connectivity During Emotional Working Memory Task in two independent samples of young adults

Effective connectivity (EC) analysis provides valuable insights into the directionality of neural interactions, which are crucial for understanding the mechanisms underlying cognitive and emotional regulation in depressive and anxiety disorders. In this study, we examined EC within key neural networks during working memory (WM) and emotional regulation (ER) tasks in young adults, both healthy individuals and those seeking help from mental health professionals for emotional distress. Dynamic causal modeling was used to analyze EC in 2 independent samples (n = 97 and n = 94). Participants performed an emotional n-back task to assess EC across the central executive network (CEN), default mode network (DMN), salience network (SN), and face processing network. Group-level parametric empirical Bayes analyses were conducted to examine EC patterns, with subanalyses comparing individuals with and without depression and anxiety. Consistent patterns of positive (posterior probability > .95) DMN→CEN and DMN→SN EC were observed in both samples, predominantly in low and high WM conditions without ER. However, individuals without depressive or anxiety disorders exhibited a significantly greater number of preserved connections that were replicated across both samples. This study highlights the different patterns of DMN→CEN EC in conditions with high and low WM loads with and without ER, suggesting that in higher WM loads with ER, the integration of the DMN with the CEN is reduced to facilitate successful cognitive task performance. The findings also suggest that DMN→CEN and DMN→SN EC are significantly reduced in depressive and anxiety disorders, highlighting this pattern of reduced EC as a potential neural marker of these disorders.