Clinical, Brain, and Multilevel Clustering in Early Psychosis and Affective Stages.

ABSTRACT:

Importance

Approaches are needed to stratify individuals in early psychosis stages beyond positive symptom severity to investigate specificity related to affective and normative variation and to validate solutions with premorbid, longitudinal, and genetic risk measures.

Objective

To use machine learning techniques to cluster, compare, and combine subgroup solutions using clinical and brain structural imaging data from early psychosis and depression stages.

Design, setting, and participants

A multisite, naturalistic, longitudinal cohort study (10 sites in 5 European countries; including major follow-up intervals at 9 and 18 months) with a referred patient sample of those with clinical high risk for psychosis (CHR-P), recent-onset psychosis (ROP), recent-onset depressio

SUBMITTER: Dwyer DB 

PROVIDER: S-EPMC9118078 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature