Inter-network Effective Connectivity During an Emotional Working Memory Task in Two Independent Samples of Young...

Area of Science:

• Neuroscience
• Cognitive Science
• Psychiatry

Background:

• Effective connectivity (EC) analysis investigates neural interaction directionality, crucial for understanding cognitive and emotional regulation in depressive and anxiety disorders.
• Previous research highlights the importance of neural network dynamics in mental health conditions.

Purpose of the Study:

• To examine EC within key neural networks during working memory (WM) and emotional regulation (ER) tasks.
• To compare EC patterns in healthy individuals versus those experiencing emotional distress.

Main Methods:

• Dynamic causal modeling (DCM) was employed on two independent samples (n=97, n=94).
• Participants completed an emotional n-back task assessing EC across the central executive network (CEN), default mode network (DMN), salience network (SN), and face processing network.
• Group-level analyses identified EC patterns, with subanalyses comparing individuals with and without depression and anxiety.

Main Results:

• Consistent positive DMN→CEN and DMN→SN EC were observed in both samples, particularly under low and high WM conditions without ER.
• Individuals without depression or anxiety disorders showed a significantly greater number of preserved connections replicated across both samples.

Conclusions:

• Reduced DMN→CEN EC integration during high WM loads with ER is suggested to facilitate cognitive task performance.
• Significantly reduced DMN→CEN and DMN→SN EC in depressive and anxiety disorders may represent a potential neural marker for these conditions.