Connectome-based modeling reveals a resting-state functional network that mediates the relationship between social rejection and rumination

Li Geng; Qiuyang Feng; Xueyang Wang; Yixin Gao; Lei Hao; Jiang Qiu

doi:10.3389/fpsyg.2023.1264221

Connectome-based modeling reveals a resting-state functional network that mediates the relationship between social rejection and rumination

Front Psychol. 2023 Oct 30:14:1264221. doi: 10.3389/fpsyg.2023.1264221. eCollection 2023.

Authors

Li Geng^#^{1

2}, Qiuyang Feng^#^{1

2}, Xueyang Wang^{1

2}, Yixin Gao^{1

2}, Lei Hao³, Jiang Qiu^{1

2}

Affiliations

¹ Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China.
² Faculty of Psychology, Southwest University, Chongqing, China.
³ College of Teacher Education, Southwest University, Chongqing, China.

^# Contributed equally.

Abstract

Background: Rumination impedes problem solving and is one of the most important factors in the onset and maintenance of multiple psychiatric disorders. The current study aims to investigate the impact of social rejection on rumination and explore the underlying neural mechanisms involved in this process.

Methods: We utilized psychological questionnaire and resting-state brain imaging data from a sample of 560 individuals. The predictive model for rumination scores was constructed using resting-state functional connectivity data through connectome-based predictive modeling. Additionally, a mediation analysis was conducted to investigate the mediating role of the prediction network in the relationship between social rejection and rumination.

Results: A positive correlation between social rejection and rumination was found. We obtained the prediction model of rumination and found that the strongest contributions came from the intra- and internetwork connectivity within the default mode network (DMN), dorsal attention network (DAN), frontoparietal control network (FPCN), and sensorimotor networks (SMN). Analysis of node strength revealed the significance of the supramarginal gyrus (SMG) and angular gyrus (AG) as key nodes in the prediction model. In addition, mediation analysis showed that the strength of the prediction network mediated the relationship between social rejection and rumination.

Conclusion: The findings highlight the crucial role of functional connections among the DMN, DAN, FPCN, and SMN in linking social rejection and rumination, particular in brain regions implicated in social cognition and emotion, namely the SMG and AG regions. These results enhance our understanding of the consequences of social rejection and provide insights for novel intervention strategies targeting rumination.

Keywords: functional connectivity; prediction model; resting-state fMRI; rumination; social rejection.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was supported by the Major Research Plan of National Social Science Foundation of China (21&ZD312).