Heterogeneity and Classification of Recent Onset Psychosis and Depression: A Multimodal Machine Learning Approach.

ABSTRACT: Diagnostic heterogeneity within and across psychotic and affective disorders challenges accurate treatment selection, particularly in the early stages. Delineation of shared and distinct illness features at the phenotypic and brain levels may inform the development of more precise differential diagnostic tools. We aimed to identify prototypes of depression and psychosis to investigate their heterogeneity, with common, comorbid transdiagnostic symptoms. Analyzing clinical/neurocognitive and grey matter volume (GMV) data from the PRONIA database, we generated prototypic models of recent-onset depression (ROD) vs. recent-onset psychosis (ROP) by training support-vector machines to separate patients with ROD from patients with ROP, who were selected for absent comorbid features (pure groups).

SUBMITTER: Lalousis PA 

PROVIDER: S-EPMC8266654 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature