Disease prediction in the at-risk mental state for psychosis using neuroanatomical biomarkers: results from the FePsy study.

ABSTRACT:

Background

Reliable prognostic biomarkers are needed for the early recognition of psychosis. Recently, multivariate machine learning methods have demonstrated the feasibility to predict illness onset in clinically defined at-risk individuals using structural magnetic resonance imaging (MRI) data. However, it remains unclear whether these findings could be replicated in independent populations.

Methods

We evaluated the performance of an MRI-based classification system in predicting disease conversion in at-risk individuals recruited within the prospective FePsy (Früherkennung von Psychosen) study at the University of Basel, Switzerland. Pairwise and multigroup biomarkers were constructed using the MRI data of 22 healthy volunteers, 16/21 at-risk subjects with/without a subseq

SUBMITTER: Koutsouleris N 

PROVIDER: S-EPMC3494048 | biostudies-literature | 2012 Nov

REPOSITORIES: biostudies-literature