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- Publisher Website: 10.1016/j.pscychresns.2021.111390
- Scopus: eid_2-s2.0-85115032013
- PMID: 34537603
- WOS: WOS:000697711300014
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Article: Social brain network predicts real-world social network in individuals with social anhedonia
Title | Social brain network predicts real-world social network in individuals with social anhedonia |
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Authors | |
Keywords | Social brain network Social network Longitudinal Prediction Social anhedonia |
Issue Date | 2021 |
Publisher | Elsevier Ireland Ltd. The Journal's web site is located at http://www.elsevier.com/locate/psychresns |
Citation | Psychiatry Research: Neuroimaging, 2021, v. 317, article no. 111390 How to Cite? |
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. |
Persistent Identifier | http://hdl.handle.net/10722/304751 |
ISSN | 2023 Impact Factor: 2.1 2023 SCImago Journal Rankings: 0.797 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Zhang, Y | - |
dc.contributor.author | Cai, X | - |
dc.contributor.author | Hu, H | - |
dc.contributor.author | Zhang, R | - |
dc.contributor.author | Wang, Y | - |
dc.contributor.author | Lui, SSY | - |
dc.contributor.author | Cheung, EFC | - |
dc.contributor.author | Chan, RCK | - |
dc.date.accessioned | 2021-10-05T02:34:39Z | - |
dc.date.available | 2021-10-05T02:34:39Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Psychiatry Research: Neuroimaging, 2021, v. 317, article no. 111390 | - |
dc.identifier.issn | 0925-4927 | - |
dc.identifier.uri | http://hdl.handle.net/10722/304751 | - |
dc.description.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. | - |
dc.language | eng | - |
dc.publisher | Elsevier Ireland Ltd. The Journal's web site is located at http://www.elsevier.com/locate/psychresns | - |
dc.relation.ispartof | Psychiatry Research: Neuroimaging | - |
dc.subject | Social brain network | - |
dc.subject | Social network | - |
dc.subject | Longitudinal | - |
dc.subject | Prediction | - |
dc.subject | Social anhedonia | - |
dc.title | Social brain network predicts real-world social network in individuals with social anhedonia | - |
dc.type | Article | - |
dc.identifier.email | Lui, SSY: lsy570@hku.hk | - |
dc.identifier.authority | Lui, SSY=rp02747 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.pscychresns.2021.111390 | - |
dc.identifier.pmid | 34537603 | - |
dc.identifier.scopus | eid_2-s2.0-85115032013 | - |
dc.identifier.hkuros | 325738 | - |
dc.identifier.volume | 317 | - |
dc.identifier.spage | article no. 111390 | - |
dc.identifier.epage | article no. 111390 | - |
dc.identifier.isi | WOS:000697711300014 | - |
dc.publisher.place | Ireland | - |