Early recognition and disease prediction in the at-risk mental states for psychosis using neurocognitive pattern classification.

ABSTRACT:

Background

Neuropsychological deficits predate overt psychosis and overlap with the impairments in the established disease. However, to date, no single neurocognitive measure has shown sufficient power for a prognostic test. Thus, it remains to be determined whether multivariate neurocognitive pattern classification could facilitate the diagnostic identification of different at-risk mental states (ARMS) for psychosis and the individualized prediction of illness transition.

Methods

First, classification of 30 healthy controls (HC) vs 48 ARMS individuals subgrouped into 20 "early," 28 "late" ARMS subjects was performed based on a comprehensive neuropsychological test battery. Second, disease prediction was evaluated by categorizing the neurocognitive baseline data of those ARM

SUBMITTER: Koutsouleris N 

PROVIDER: S-EPMC3494049 | biostudies-literature | 2012 Nov

REPOSITORIES: biostudies-literature