Show simple item record

dc.contributor.authorSun, Yi
dc.contributor.authorZhang, Zhe
dc.contributor.authorKakkos, Ioannis
dc.contributor.authorMatsopoulos, George K
dc.contributor.authorYuan, Jingjia
dc.contributor.authorSuckling, John
dc.contributor.authorXu, Luoyi
dc.contributor.authorCao, Shuxia
dc.contributor.authorChen, Wenjuan
dc.contributor.authorHu, Xingyue
dc.contributor.authorLi, Tao
dc.contributor.authorSim, Kang
dc.contributor.authorQi, Peng
dc.contributor.authorSun, Yu
dc.date.accessioned2022-03-05T00:30:35Z
dc.date.available2022-03-05T00:30:35Z
dc.date.issued2022-06
dc.identifier.issn2168-2194
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/334687
dc.description.abstractThe prediction of schizophrenia-related psychopathologic deficits is exceedingly important in the fields of psychiatry and clinical practice. However, objective association of the brain structure alterations to the illness clinical symptoms is challenging. Although, schizophrenia has been characterized as a brain dysconnectivity syndrome, evidence accounting for neuroanatomical network alterations remain scarce. Moreover, the absence of generalized connectome biomarkers for the assessment of illness progression further perplexes the prediction of long-term symptom severity. In this paper, a combination of individualized prediction models with quantitative graph theoretical analysis was adopted, providing a comprehensive appreciation of the extent to which the brain network properties are affected over time in schizophrenia. Specifically, Connectome-based Prediction Models were employed on Structural Connectivity (SC) features, efficiently capturing individual network-related differences, while identifying the anatomical connectivity disturbances contributing to the prediction of psychopathological deficits. Our results demonstrated distinctions among widespread cortical circuits responsible for different domains of symptoms, indicating the complex neural mechanisms underlying schizophrenia. Furthermore, the generated models were able to significantly predict changes of symptoms using SC features at follow-up, while the preserved SC features suggested an association with improved positive and overall symptoms. Moreover, cross-sectional significant deficits were observed in network efficiency and a progressive aberration of global integration in patients compared to healthy controls, representing a group-consensus pathological map, while supporting the dysconnectivity hypothesis.
dc.format.mediumPrint-Electronic
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.rightsPublisher's own licence
dc.titleInferring the Individual Psychopathologic Deficits With Structural Connectivity in a Longitudinal Cohort of Schizophrenia.
dc.typeArticle
dc.publisher.departmentDepartment of Psychiatry
dc.date.updated2022-03-04T09:59:53Z
prism.endingPage1
prism.issueIdentifier99
prism.publicationDate2022
prism.publicationNameIEEE J Biomed Health Inform
prism.startingPage1
prism.volumePP
dc.identifier.doi10.17863/CAM.82105
dcterms.dateAccepted2021-12-28
rioxxterms.versionofrecord10.1109/JBHI.2021.3139701
rioxxterms.versionAM
dc.contributor.orcidSuckling, John [0000-0002-5098-1527]
dc.identifier.eissn2168-2208
rioxxterms.typeJournal Article/Review
cam.issuedOnline2022-06-03
cam.orpheus.success2022-03-04 - Embargo set during processing via Fast-track
cam.depositDate2022-03-04
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement
rioxxterms.freetoread.startdate2022-01-04


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record