TY - JOUR
T1 - Do cognitive subtypes exist in people at clinical high risk for psychosis? Results from the EU-GEI study
AU - EU-GEI High Risk Study
AU - Gifford, George
AU - Avila, Alessia
AU - Kempton, Matthew J.
AU - Fusar-Poli, Paolo
AU - Mccutcheon, Robert A.
AU - Coutts, Fiona
AU - Tognin, Stefania
AU - Valmaggia, Lucia
AU - Haan, Lieuwe de
AU - Gaag, Mark van der
AU - Nelson, Barnaby
AU - Pantelis, Christos
AU - Riecher-Rössler, Anita
AU - Bressan, Rodrigo
AU - Barrantes-Vidal, Neus
AU - Krebs, Marie-Odile
AU - Glenthoj, Birte
AU - Ruhrmann, Stephan
AU - Sachs, Gabriele
AU - Rutten, Bart P. F.
AU - Os, Jim van
AU - McGuire, Philip
PY - 2024/7
Y1 - 2024/7
N2 - 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 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.
AB - 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 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.
KW - Clinical high risk for psychosis
KW - Clustering
KW - Cognition
KW - Unsupervised learning
U2 - 10.1093/schbul/sbae133
DO - 10.1093/schbul/sbae133
M3 - Article
C2 - 39052918
SN - 0586-7614
JO - Schizophrenia Bulletin
JF - Schizophrenia Bulletin
ER -