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

• Published in: Schizophrenia research Vol. 214; pp. 18 - 23
• Main Authors: Zarogianni, Eleni, Storkey, Amos J., Borgwardt, Stefan, Smieskova, Renata, Studerus, Erich, Riecher-Rössler, Anita, Lawrie, Stephen M.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01-12-2019
• Subjects: At-risk mental state; Early diagnosis; Magnetic resonance imaging; Psychosis onset; Support vector machine; Psychosis onset; Early diagnosis; Support vector machine; Magnetic resonance imaging; At-risk mental state
• ISSN: 0920-9964; 1573-2509
• DOI: 10.1016/j.schres.2017.08.061

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.
•Individualized prediction of transition to psychosis is possible in individuals with an at-risk mental state.•Replication of previous classification findings where a familial high-risk cohort was used.•Classification performance remains to be tested by pooling together familial and clinical high-risk cohorts.