Disease Prediction in the At-RiskMental State for Psychosis UsingNeuroanatomical Biomarkers: Results From the FePsy Study

Background: Reliable prognostic biomarkers are needed for the early recognition of psychosis. Recently, multivari-ate machine learning methods have demonstrated the feasi-bility 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 volun-teers, 16/21 at-risk subjects with/without a subsequent dis-ease conversion. Diagnostic performance was measured i