Using neuroimaging to help predict the onset of psychosis

George Gifford; Nicolas Crossley; Paolo Fusar-Poli; Hugo G. Schnack; René S. Kahn; Nikolaos Koutsouleris; Tyrone D. Cannon; Philip McGuire

doi:10.1016/j.neuroimage.2016.03.075

Using neuroimaging to help predict the onset of psychosis

George Gifford, Nicolas Crossley, Paolo Fusar-Poli, Hugo G. Schnack, René S. Kahn, Nikolaos Koutsouleris, Tyrone D. Cannon, Philip McGuire

Research output: Contribution to journal › Article › peer-review

51 Scopus citations

Abstract

The aim of this review is to assess the potential for neuroimaging measures to facilitate prediction of the onset of psychosis. Research in this field has mainly involved people at ‘ultra-high risk’ (UHR) of psychosis, who have a very high risk of developing a psychotic disorder within a few years of presentation to mental health services. The review details the key findings and developments in this area to date and examines the methodological and logistical challenges associated with making predictions in an individual subject in a clinical setting.

Original language	English
Pages (from-to)	209-217
Number of pages	9
Journal	NeuroImage
Volume	145
DOIs	https://doi.org/10.1016/j.neuroimage.2016.03.075
State	Published - 15 Jan 2017
Externally published	Yes

Keywords

Graph analysis
Machine learning
Multicentre neuroimaging studies
Multimodal neuroimaging
Psychosis prediction
Support vector machines
Ultra high-risk of psychosis

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10.1016/j.neuroimage.2016.03.075

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AU - Fusar-Poli, Paolo

AU - Schnack, Hugo G.

AU - Kahn, René S.

AU - Koutsouleris, Nikolaos

AU - Cannon, Tyrone D.

AU - McGuire, Philip

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KW - Machine learning

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KW - Multimodal neuroimaging

KW - Psychosis prediction

KW - Support vector machines

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Using neuroimaging to help predict the onset of psychosis

Abstract

Keywords

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