An empirical comparison of different approaches for combining multimodal neuroimaging data with support vector machine

Journal article

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

Authors: Pettersson-Yeo, W; Benetti, S; Marquand, AF; Joules, R; Catani, M

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Frontiers Media
• Journal: Frontiers in Neuroscience
• Volume: 8
• Pages: 189-189
• Publication date: 2014-07-15
• DOI: 10.3389/fnins.2014.00189
• EISSN: 1662-453X
• ISSN: 1662-4548
• Language: English
• Keywords: Artificial intelligence; Psychology; Support vector machine; Salience (neuroscience); Inference; Pattern recognition (psychology); Neuroimaging; Computer science; Modality (human–computer interaction); Machine learning