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Do Cognitive Subtypes Exist in People at Clinical High Risk for Psychosis? : Results From the EU-GEI Study
Gifford, George (University of Oxford)
Avila, Alessia (Universidade de Lisboa)
Kempton, Matthew J. (King's College London)
Fusar-Poli, Paolo (King's College London)
McCutcheon, Robert A. (University of Oxford)
Coutts, Fiona (King's College London)
Tognin, Stefania (King's College London)
Valmaggia, Lucia (King's College London)
de Haan, Lieuwe (Academic Psychiatric Centre (Amsterdam, Països Baixos))
van der Gaag, Mark (Vrije Universiteit Amsterdam)
Nelson, Barnaby (Orygen)
Pantelis, Christos (University of Melbourne)
Bressan, Rodrigo Affonseca (Universidade Federal de São Paulo)
Barrantes-Vidal, Neus (Universitat Autònoma de Barcelona. Departament de Psicologia Clínica i de la Salut)
Krebs, Marie-Odile (University Paris Descartes)
Glenthøj, Birte (University of Copenhagen)
Ruhrmann, Stephan (University of Cologne)
Sachs, Gabriele (Medical University of Vienna)
Rutten, Bart P. F. (Maastricht University Medical Centre (Maastricht, Països Baixos))
van Os, Jim (Maastricht University Medical Centre (Maastricht, Països Baixos))
McGuire, Philip (University of Oxford)

Fecha: 2024
Descripción: 11 pàg.
Resumen: Background 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 identified 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 find 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.
Ayudas: Agencia Estatal de Investigación PSI2017-87512-C2-1-R
Derechos: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original. Creative Commons
Lengua: Anglès
Documento: Article ; recerca ; Versió publicada
Materia: Clinical high risk for psychosis ; Cognition ; Unsupervised learning ; Clustering ; Humans ; Psychotic Disorders/physiopathology ; Male ; Adult ; Female ; Young Adult ; Cognitive Dysfunction/physiopathology ; Adolescent ; Risk ; Prodromal Symptoms ; Cohort Studies ; Gray Matter/pathology ; Cluster Analysis ; Magnetic Resonance Imaging
Publicado en: Schizophrenia bulletin, Vol. 51, Núm. 4 (2024) , p. 1019-1029, ISSN 1745-1701

DOI: 10.1093/schbul/sbae133
PMID: 39052918


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