Min, S., Jun, S., Ahn, J., Lee, J., Lee, S-K., Park, S.H., & Han, S. Intrinsic functional connectivity in emotion regulation network is altered in emotional laborers [Poster]

 

 

 

 

Abstract

 

Emotional labor is considered as a kind of emotion regulation process, which includes managing one’s emotion and emotional expressions to fulfill other people’s expectations. Although several studies have described the psychological characteristics of emotional laborers, little is known about the neurobiological consequences of emotional labor. In this study, we investigated whether patterns of intrinsic functional connectivity can differentiate between emotional laborers and healthy controls and which networks are the most crucial features that distinguish two groups. Applying multivariate pattern analysis (MVPA) methods to each participant’s whole-brain resting-state functional connectivity matrix, we tested whether a machine learning classifier could successfully distinguish these two group. We found that two groups could be successfully classified on the basis of individuals’ connectivity patterns. To further characterize the network structure of connectivity features used for classification, we calculated degree centrality and weighted betweenness centrality of each node with included edges. This analysis revealed that several distributed nodes, including superior parietal lobule (SPL), inferior parietal lobule (IPL), middle frontal gyrus (MFG), posterior superior temporal sulcus (pSTS), and orbitofrontal cortex (OFC), showed greater degree and betweenness centrality among other nodes. Our results suggest that intrinsic connectivity pattern can be useful in exploring the neurobiological changes associated with persistent stress at workplace.