Social brain network predicts real-world social network in individuals with social anhedonia. (30th November 2021)

• Record Type: Journal Article
• Authors: Zhang, Yi-jing; Cai, Xin-lu; Hu, Hui-xin; Zhang, Rui-ting; Wang, Yi; Lui, Simon S.Y.; Cheung, Eric F.C.; Chan, Raymond C.K.
• Is Part Of: Psychiatry research. Volume 317(2021)
• Publication Date: 2021-11-30
• Subjects: Social brain network -- Social network -- Longitudinal -- Prediction -- Social anhedonia; Psychiatry -- Periodicals; Brain -- Imaging -- Periodicals; Psychiatry -- Periodicals; Diagnostic Imaging -- Periodicals; Psychiatrie -- Périodiques; Cerveau -- Imagerie pour le diagnostic -- Périodiques; 616.890754
• DOI: 10.1016/j.pscychresns.2021.111390
• Languages: English
• ISSNs: 0925-4927

Abstract

Highlights: Functional social brain network predicted social network development. Topological characteristics predicted social network in the group with high levels of social anhedonia. Functional connectivity predicted social network in the group with low levels of social anhedonia. Right orbital inferior frontal gyrus centered at the predictive component. Abstract: Social anhedonia (SA) impairs social functioning in schizophrenia. Previous evidence suggested that certain brain regions predict longitudinal change of real-world social outcomes, yet previous study designs have failed to capture the corresponding functional connectivity among the brain regions involved. This study measured the real-world social network in 22 pairs of individuals with high and low levels of SA, and followed up them for 21 months. We further explored whether resting-state social brain network characteristics could predict the longitudinal variations of real-world social network. Our results showed that social brain network characteristics could predict the change of real-world social networks in both the high SA and low SA groups. However, the results differed between the two groups, i.e., the topological characteristics of the social brain network predicted real-world social network change in the high SA group; whereas the functional connectivity within the social brain network predicted real-world social network change in the low SA group. Principal component analysis and linear regression analysis on the entire sample showed that the functional connectivity component centered at the right orbital inferior frontal gyrus could best predict social network change. Our findings support the notion that social brain network characteristics could predict social network development.