Advancing understanding of affect labeling with dynamic causal modeling.

Area of Science:

• Affective neuroscience
• Cognitive neuroscience
• Neuroimaging

Background:

• Incidental emotion regulation is crucial for affective sciences.
• Understanding prefrontal-amygdala circuitry is key for translational research.

Purpose of the Study:

• To investigate prefrontal-to-subcortical influences during affect labeling using fMRI.
• To model the neural network connectivity underlying emotion regulation.

Main Methods:

• Applied Dynamic Causal Modeling (DCM) to fMRI data from 45 healthy subjects.
• Compared 64 models of brain region interactions during facial affect labeling.
• Utilized Bayesian Model Selection and Averaging to identify network connectivity patterns.

Main Results:

• Identified a distinct endogenous network connectivity pattern.
• Right ventrolateral prefrontal cortex (vlPFC) and Broca's area significantly modulated amygdala activity.
• Right vlPFC exerted a stronger dampening influence on the amygdala than Broca's area.

Conclusions:

• The findings support and extend previous research on corticolimbic coupling.
• Dynamic causal modeling provides mechanistic insights into incidental emotion regulation.
• This study elucidates the neural pathways involved in top-down emotional control.