Using neuroimaging to help predict the onset of psychosis

- Machine learning methods allow for prediction of psychosis outcomes in individuals.
- Current multicentre studies seek to recruit sufficient samples for ML prediction.
- Multiple modalities can be incorporated into ML predictive models.
- ML methods can be used to predict continuous outcomes.
- ML can incorporate graph analysis if data is transformed into vector space.

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.