Individualized prediction of psychosis in subjects with an at-risk mental state

Eleni Zarogianni, Amos J Storkey, Stefan Borgwardt, Renata Smieskova, Erich Studerus, Anita Riecher-Rössler, Stephen M Lawrie

Research output: Contribution to journalArticlepeer-review

Abstract

Early intervention strategies in psychosis would significantly benefit from the identification of reliable prognostic biomarkers. Pattern classification methods have shown the feasibility of an early diagnosis of psychosis onset both in clinical and familial high-risk populations. Here we were interested in replicating our previous classification findings using an independent cohort at clinical high risk for psychosis, drawn from the prospective FePsy (Fruherkennung von Psychosen) study. The same neuroanatomical-based pattern classification pipeline, consisting of a linear Support Vector Machine (SVM) and a Recursive Feature Selection (RFE) achieved 74% accuracy in predicting later onset of psychosis. The discriminative neuroanatomical pattern underlying this finding consisted of many brain areas across all four lobes and the cerebellum. These results provide proof-of-concept that the early diagnosis of psychosis is feasible using neuroanatomical-based pattern recognition.

Original languageEnglish
JournalSchizophrenia Research
Early online date19 Sep 2017
DOIs
Publication statusE-pub ahead of print - 19 Sep 2017

Keywords

  • Journal Article

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