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Central and non-central networks, cognition, clinical symptoms, and polygenic risk scores in schizophrenia

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    Rights statement: This is the peer reviewed version of the following article: Alloza, C., Bastin, M. E., Cox, S. R., Gibson, J., Duff, B., Semple, S. I., Whalley, H. C. and Lawrie, S. M. (2017), Central and non-central networks, cognition, clinical symptoms, and polygenic risk scores in schizophrenia. Hum. Brain Mapp. doi:10.1002/hbm.23798, which has been published in final form at . This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving."

    Accepted author manuscript, 676 KB, PDF document

Original languageEnglish
Pages (from-to)5919-5930
JournalHuman Brain Mapping
Issue number12
Early online date7 Sep 2017
Publication statusPublished - Dec 2017


Schizophrenia is a complex disorder that may be the result of aberrant connections between specific brain regions rather than focal brain abnormalities. Here, we investigate the relationships between brain structural connectivity as described by network analysis, intelligence, symptoms, and polygenic risk scores (PGRS) for schizophrenia in a group of patients with schizophrenia and a group of healthy controls. Recently, researchers have shown an interest in the role of high centrality networks in the disorder. However, the importance of non-central networks still remains unclear. Thus, we specifically examined network-averaged fractional anisotropy (mean edge weight) in central and non-central subnetworks. Connections with the highest betweenness centrality within the average network (>75% of centrality values) were selected to represent the central subnetwork. The remaining connections were assigned to the non-central subnetwork. Additionally, we calculated graph theory measures from the average network (connections that occur in at least 2/3 of participants). Density, strength, global efficiency, and clustering coefficient were significantly lower in patients compared with healthy controls for the average network (pFDR  < 0.05). All metrics across networks were significantly associated with intelligence (pFDR  < 0.05). There was a tendency towards significance for a correlation between intelligence and PGRS for schizophrenia (r = -0.508, p = 0.052) that was significantly mediated by central and non-central mean edge weight and every graph metric from the average network. These results are consistent with the hypothesis that intelligence deficits are associated with a genetic risk for schizophrenia, which is mediated via the disruption of distributed brain networks. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc.

    Research areas

  • schizophrenia, diffusion tensor, MRI, connectivity, intelligence, genetics, symptoms

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