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Article: Altered topographical organization of grey matter structural network in early-onset schizophrenia

TitleAltered topographical organization of grey matter structural network in early-onset schizophrenia
Authors
KeywordsEarly-onset schizophrenia
Graph theory
Grey matter
Structural network
Issue Date2021
Citation
Psychiatry Research Neuroimaging, 2021, v. 316, article no. 111344 How to Cite?
AbstractSchizophrenia is characterized by both disrupted neurodevelopmental processes and abnormal brain connectivity. However, few studies have examined the atypical features of brain network topography associated with schizophrenia during childhood and adolescence. We used graph theory to compare the grey matter structural networks of individuals (aged 10-15 years) with early-onset schizophrenia (EOS) (n = 25) and a typically-developing (TD) comparison group (n = 31). Compared with the TD group, EOS patients showed significantly increased clustering and local efficiency across a range of network densities (0.3 – 0.4). The network of EOS patients also had more modules (6 modules in EOS vs. 3 modules in controls), indicating a more segregated network at the cost of functional integration. Although our results were preliminary and failed to survive corrections for multiple comparisons, EOS patients might be characterized by altered nodal centrality in several higher-order associative regions including the prefrontal cortex, the hippocampus and the cerebellum. The EOS structural network also lacked the typical left-hemispheric-dominant hub distribution compared with the TD group. These findings suggest that brain structural network was not only globally but also regionally altered in EOS patients.
Persistent Identifierhttp://hdl.handle.net/10722/367543
ISSN
2023 Impact Factor: 2.1
2023 SCImago Journal Rankings: 0.797

 

DC FieldValueLanguage
dc.contributor.authorZhou, Han yu-
dc.contributor.authorShi, Li juan-
dc.contributor.authorShen, Yan mei-
dc.contributor.authorFang, Yu min-
dc.contributor.authorHe, Yu qiong-
dc.contributor.authorLi, Hua bing-
dc.contributor.authorLuo, Xue rong-
dc.contributor.authorCheung, Eric F.C.-
dc.contributor.authorChan, Raymond C.K.-
dc.date.accessioned2025-12-19T07:57:33Z-
dc.date.available2025-12-19T07:57:33Z-
dc.date.issued2021-
dc.identifier.citationPsychiatry Research Neuroimaging, 2021, v. 316, article no. 111344-
dc.identifier.issn0925-4927-
dc.identifier.urihttp://hdl.handle.net/10722/367543-
dc.description.abstractSchizophrenia is characterized by both disrupted neurodevelopmental processes and abnormal brain connectivity. However, few studies have examined the atypical features of brain network topography associated with schizophrenia during childhood and adolescence. We used graph theory to compare the grey matter structural networks of individuals (aged 10-15 years) with early-onset schizophrenia (EOS) (n = 25) and a typically-developing (TD) comparison group (n = 31). Compared with the TD group, EOS patients showed significantly increased clustering and local efficiency across a range of network densities (0.3 – 0.4). The network of EOS patients also had more modules (6 modules in EOS vs. 3 modules in controls), indicating a more segregated network at the cost of functional integration. Although our results were preliminary and failed to survive corrections for multiple comparisons, EOS patients might be characterized by altered nodal centrality in several higher-order associative regions including the prefrontal cortex, the hippocampus and the cerebellum. The EOS structural network also lacked the typical left-hemispheric-dominant hub distribution compared with the TD group. These findings suggest that brain structural network was not only globally but also regionally altered in EOS patients.-
dc.languageeng-
dc.relation.ispartofPsychiatry Research Neuroimaging-
dc.subjectEarly-onset schizophrenia-
dc.subjectGraph theory-
dc.subjectGrey matter-
dc.subjectStructural network-
dc.titleAltered topographical organization of grey matter structural network in early-onset schizophrenia-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.pscychresns.2021.111344-
dc.identifier.pmid34358964-
dc.identifier.scopuseid_2-s2.0-85111682616-
dc.identifier.volume316-
dc.identifier.spagearticle no. 111344-
dc.identifier.epagearticle no. 111344-
dc.identifier.eissn1872-7506-

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