Do Cognitive Subtypes Exist in People at Clinical High Risk for Psychosis? Results From the EU-GEI Study

• Published in: Schizophrenia bulletin Vol. 51; no. 4; pp. 1019 - 1029
• Main Authors: Gifford, George, Avila, Alessia, Kempton, Matthew J, Fusar-Poli, Paolo, McCutcheon, Robert A, Coutts, Fiona, Tognin, Stefania, Valmaggia, Lucia, de Haan, Lieuwe, van der Gaag, Mark, Nelson, Barnaby, Pantelis, Christos, Riecher-Rössler, Anita, Bressan, Rodrigo, Barrantes-Vidal, Neus, Krebs, Marie-Odile, Glenthøj, Birte, Ruhrmann, Stephan, Sachs, Gabriele, Rutten, Bart P F, van Os, Jim, EU-GEI High Risk Study, McGuire, Philip
• Format: Journal Article
• Language: English
• Published: United States Oxford University Press 01.07.2025
• Subjects: Adolescent; Adult; Cluster Analysis; Cognitive Dysfunction - classification; Cognitive Dysfunction - diagnostic imaging; Cognitive Dysfunction - etiology; Cognitive Dysfunction - physiopathology; Cohort Studies; Female; Gray Matter - diagnostic imaging; Gray Matter - pathology; Humans; Magnetic Resonance Imaging; Male; Prodromal Symptoms; Psychotic Disorders - classification; Psychotic Disorders - complications; Psychotic Disorders - diagnostic imaging; Psychotic Disorders - physiopathology; Regular; Risk; Young Adult; clinical high risk for psychosis; clustering; cognition; unsupervised learning
• ISSN: 0586-7614; 1745-1701; 1745-1707; 1745-1701
• DOI: 10.1093/schbul/sbae133

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. 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. 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. 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.