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Do cognitive subtypes exist in people at clinical high risk for psychosis? Results from the EU-GEI study

2024-07Journal article Open access
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dc.contributor.authorEU-GEI High Risk Study
dc.contributor.authorGifford, George
dc.contributor.authorAvila, Alessia
dc.contributor.authorKempton, Matthew J.
dc.contributor.authorFusar-Poli, Paolo
dc.contributor.authorMccutcheon, Robert A.
dc.contributor.authorCoutts, Fiona
dc.contributor.authorTognin, Stefania
dc.contributor.authorValmaggia, Lucia
dc.contributor.authorHaan, Lieuwe de
dc.contributor.authorGaag, Mark van der
dc.contributor.authorNelson, Barnaby
dc.contributor.authorPantelis, Christos
dc.contributor.authorRiecher-Rössler, Anita
dc.contributor.authorBressan, Rodrigo
dc.contributor.authorBarrantes-Vidal, Neus
dc.contributor.authorKrebs, Marie-Odile
dc.contributor.authorGlenthoj, Birte
dc.contributor.authorRuhrmann, Stephan
dc.contributor.authorSachs, Gabriele
dc.contributor.authorRutten, Bart P. F.
dc.contributor.authorOs, Jim van
dc.contributor.authorMcGuire, Philip
dc.date.accessioned2024-08-06T08:38:42Z
dc.date.available2024-08-06T08:38:42Z
dc.date.issued2024-07
dc.description.abstractBackground and Hypothesis: Cognition has been associated with socio-occupational functioning in individuals at Clinical High Risk for Psychosis (CHR-P). The present study hypothesized that clustering CHR-P participants based on cognitive data could reveal clinically meaningful subtypes. Study Design: A cohort of 291 CHR-P subjects was recruited through the multicentre EU-GEI high-risk study. We explored whether an underlying cluster structure was present in the cognition data. Clustering of cognition data was performed using k-means clustering and density-based spatial clustering of applications with noise. Cognitive subtypes were validated by comparing differences in functioning, psychosis symptoms, transition outcome, and grey matter volume between clusters. Network analysis was used to further examine relationships between cognition scores and clinical symptoms. Study Results: No underlying cluster structure was found in the cognitive data. K-means clustering produced “spared” and “impaired” cognition clusters similar to those reported in previous studies. However, these clusters were not associated with differences in functioning, symptomatology, outcome, or grey matter volume. Network analysis identifed cognition and symptoms/functioning measures that formed separate subnetworks of associations. Conclusions: Stratifying patients according to cognitive performance has the potential to inform clinical care. However, we did not fnd evidence of cognitive clusters in this CHR-P sample. We suggest that care needs to be taken in inferring the existence of distinct cognitive subtypes from unsupervised learning studies. Future research in CHR-P samples could explore the existence of cognitive subtypes across a wider range of cognitive domains.pt_PT
dc.description.versioninfo:eu-repo/semantics/acceptedVersionpt_PT
dc.identifier.doi10.1093/schbul/sbae133pt_PT
dc.identifier.issn0586-7614
dc.identifier.pmid39052918
dc.identifier.urihttp://hdl.handle.net/10400.14/46062
dc.identifier.wos001275777500001
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectClinical high risk for psychosispt_PT
dc.subjectClusteringpt_PT
dc.subjectCognitionpt_PT
dc.subjectUnsupervised learningpt_PT
dc.titleDo cognitive subtypes exist in people at clinical high risk for psychosis? Results from the EU-GEI studypt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.titleSchizophrenia Bulletinpt_PT
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT

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