Learning Latent Profiles via Cognitive Growth Charting in Psychosis: Design and Rationale for the PRECOGNITION Project.

Abstract

Background and hypothesis: Cognitive impairments are a core feature of psychosis that are often evident before illness onset and have substantial impact on both clinical and real-world functional outcomes. Therefore, these are an excellent target for stratification and early detection in order to facilitate early intervention. While many studies have aimed to characterize the effects of cognition at the group level and others have aimed to detect individual differences by referencing subjects against existing norms, these studies have limited generalizability across clinical populations, demographic backgrounds, and instruments and do not fully account for the interindividual heterogeneity inherent in psychosis.

Study design: Here, we outline the rationale, design, and analysis plan for the PRECOGNITION project, which aims to address these challenges.

Study results: This project is a collaboration between partners in 5 European countries. The project will not generate any primary data, but by leveraging existing datasets and combining these with novel analytic methods, it will produce multiple contributions including: (i) translating normative modeling approaches pioneered in brain imaging to psychosis data, to yield "cognitive growth charts" for longitudinal tracking and individual prediction; (ii) developing machine learning models for harmonizing and stratifying cohorts on the basis of these models; and (iii) providing integrated next-generation norms, having broad sociodemographic coverage including different languages and distinct norms for individuals with psychosis and unaffected individuals.

Conclusions: This study will enable precision stratification of psychosis cohorts and furnish predictions for a broad range of functional outcome measures. It will be guided throughout by lived experience experts.