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

Zarogianni, Eleni and Storkey, Amos J. and Borgwardt, Stefan and Smieskova, Renata and Studerus, Erich and Riecher-Rössler, Anita and Lawrie, Stephen M.. (2017) Individualized prediction of psychosis in subjects with an at-risk mental state. Schizophrenia Research, 214. pp. 18-23.

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Official URL: https://edoc.unibas.ch/70685/

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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.
Faculties and Departments:03 Faculty of Medicine > Bereich Psychiatrie (Klinik) > Erwachsenenpsychiatrie UPK > Erwachsenenpsychiatrie (Riecher-Rössler)
03 Faculty of Medicine > Departement Klinische Forschung > Bereich Psychiatrie (Klinik) > Erwachsenenpsychiatrie UPK > Erwachsenenpsychiatrie (Riecher-Rössler)
UniBasel Contributors:Riecher-Rössler, Anita and Studerus, Erich
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:Elsevier
ISSN:0920-9964
e-ISSN:1573-2509
Note:Publication type according to Uni Basel Research Database: Journal article
Language:English
Identification Number:
Last Modified:02 Jan 2020 10:48
Deposited On:29 May 2019 09:53

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