A dynamic functional network connectivity study on social emotion regulation
Poster No:
818
Submission Type:
Abstract Submission
Authors:
jiazheng Wang1, Bharat Biswal2
Institutions:
1university of electrnic science and technology of China, Chengdu, Sichuan, 2New Jersey Institute of Technology, Newark, NJ
First Author:
Co-Author:
Introduction:
Social emotion regulation (SER) is a critical ability for recovering from negative emotions during social interaction (Butler, 2011; Dixon-Gordon, Bernecker, & Christensen, 2015). For instance, a hug from a friend could warm our heart, particularly when we are feeling down. More importantly, SER is beneficial for individual in establishing well-conditioned social relationships, and contributing to prosocial behavior (Hofmann, 2014). Crucially, disturbance in SER is a vital hallmarks of various disorders, such as depression and borderline personality (Zaki, 2020). The neural substrates of SER emphasize that empathy network, cognitive control work and affective generation network sustain the employment of IER processing (Morawetz, Berboth, & Bode, 2021). However, it remains unclear how the functional connectivity, as well as its temporal variations among these network during the entire SER process. The present study aims to verify the static and dynamic functional connectivity of SER related network during the entire SER processing.
Methods:
The present study utilized an SER task mode-fMRI and a sliding windows approach to examine both the static and dynamic functional network connectivity of SER processing. 55 healthy participants were recruited for the present SER study. The task was utilized a classical SER task, which includes two conditions: SER condition and Watch condition (Xie et al. 2016). During the SER condition, a short video clip by the experimenter was presented to guide the subjects how to down-regulate their negative emotions. The Watch condition needs the subjects experience their negative emotions, as the base-line for IER processing (figure 1). Firstly, the group independent component analysis (GICA) was conducted to identify the independent components (ICs) during the SER processing, and the peak coordinate of each IC was utilized as SER spatial map for further analysis (Klugah-Brown et al., 2019). Then, the static and dynamic functional network connectivity utilized Pearson's correlation based on the ICs in ROI wise. The sliding window length in 30TR and steps of 1TR, and the windows × IC × IC matrix was obtained for dynamic SER processing (Hutchison, Womelsdorf, Gati, Everling, & Menon, 2013). After that, we used the K-mean to classify the dynamic functional connectivity and the betweenness centrality to extract crucial hubs during the SER processing.
Results:
The GICA results yielded 30 distinct ICs, which were subsequently mapped onto SER-related functional networks based on their corresponding peak coordinates. The static functional connectivity results showed strong positive correlations within visual network, empathy network, affective generation network and cognitive control network (Figure 2). The dynamic activity across in SER process exhibited 4 distinct states (Figure 3). The empathy network occurs across all the 4 brain states. Additionally, a 'top-down' pattern is observed between the connectivity of the cognitive control network and the affective generation network during the cognitive control stage and affective response modulation stage.
Conclusions:
By employing sliding window approaches, this study has broadened the scope of emotion regulation research, showcasing distinct functional network connectivity states during the dynamic processing of SER. Four unique brain states were identified, each associated with a domain network profiling the dynamic cognitive processing throughout the SER stage. It's shedding light on the cognitive and neural mechanisms underlying this intricate process.
Emotion, Motivation and Social Neuroscience:
Social Cognition
Social Interaction 1
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making 2
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Emotions
Experimental Design
1|2Indicates the priority used for review
Provide references using author date format
Dixon-Gordon, K. L., Bernecker, S. L., & Christensen, K. (2015). Recent innovations in the field of interpersonal emotion regulation. Current Opinion in Psychology, 3, 36-42. DOI: http://dx.doi.org/doi:10.1016/j.copsyc.2015.02.001
Hofmann, S. G. (2014). Interpersonal emotion regulation model of mood and anxiety disorders. Cognitive therapy and research, 38, 483-492. DOI 10.1007/s10608-014-9620-1
Hutchison, R. M., Womelsdorf, T., Gati, J. S., Everling, S., & Menon, R. S. (2013). Resting‐state networks show dynamic functional connectivity in awake humans and anesthetized macaques. Human brain mapping, 34(9), 2154-2177. DOI: 10.1002/hbm.22058
Klugah-Brown, B., Luo, C., He, H., Jiang, S., Armah, G. K., Wu, Y., . . . Yao, D. (2019). Altered dynamic functional network connectivity in frontal lobe epilepsy. Brain topography, 32, 394-404. DOI: https://doi.org/10.1007/s10548-018-0678-z
Morawetz, C., Berboth, S., & Bode, S. (2021). With a little help from my friends: The effect of social proximity on emotion regulation-related brain activity. Neuroimage, 230, 117817. DOI: 10.1016/j.neuroimage.2021.117817
Xie X, Mulej Bratec S, Schmid G, Meng C, Doll A, Wohlschläger A, Finke K, Förstl H, Zimmer C, Pekrun R, et al. How do you make me feel better? Social cognitive emotion regulation and the default mode network. NeuroImage. 2016:134:270–280
Zaki, J. (2020). Integrating empathy and interpersonal emotion regulation. Annual review of psychology, 71, 517-540. DOI: 10.1146/annurev-psych-010419-050830