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, McGuire Philip
Department of Psychiatry, University of Oxford, Oxford, UK.
Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
Schizophr Bull. 2025 Jul 7;51(4):1019-1029. 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.
在临床高危精神病个体（CHR-P）中，认知与社会职业功能相关。本研究假设，基于认知数据对CHR-P参与者进行聚类分析，可能会揭示具有临床意义的亚型。
通过多中心欧盟基因-环境相互作用（EU-GEI）高危研究招募了291名CHR-P受试者。我们探讨了认知数据中是否存在潜在的聚类结构。使用k均值聚类和基于密度的带噪声空间聚类算法对认知数据进行聚类。通过比较各聚类之间在功能、精神病症状、转归结果和灰质体积方面的差异，对认知亚型进行验证。采用网络分析进一步研究认知得分与临床症状之间的关系。
在认知数据中未发现潜在的聚类结构。k均值聚类产生了与先前研究中报告的类似的“保留”和“受损”认知聚类。然而，这些聚类与功能、症状学、转归或灰质体积的差异无关。网络分析确定了形成独立关联子网的认知和症状/功能测量指标。
根据认知表现对患者进行分层可能有助于临床护理。然而，在这个CHR-P样本中，我们没有发现认知聚类的证据。我们建议，在从无监督学习研究中推断不同认知亚型的存在时需谨慎。未来对CHR-P样本的研究可以在更广泛的认知领域探索认知亚型的存在情况。
