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
A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPT2 predictions in an individual subject in a clinical setting.
Key wordsPsychosis prediction; Ultra High-Risk of psychosis; machine learning; Support Vector Machines; multimodal neuroimaging; multicentre neuroimaging studies; graph analysis
Psychosis prediction and the ultra high-risk statePsychosis describes a syndrome that includes symptoms such as hallucinations, delusions, disorganised thought, and catatonia Criteria for UHR status include attenuated psychotic symptoms and / or a brief limited intermittent psychotic episode and / or a genetically determined vulnerability, alongside deterioration in social and occupational functioning 7 8 . UHR status comes
A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPT3 with the caveat that those who meet UHR status are selectively those who have come into contact with clinical services. This review uses the term UHR throughout.The UHR population is strikingly heterogeneous in terms of clinical outcomes.Follow-up studies 9 10 suggest that 7 years after clinical presentation, approximately a third of UHR subjects will have developed a psychotic disorder, with most transitions occurring in the first 2 years
11. Most of those who do not develop a psychotic disorder will have persistent attenuated symptoms and / or have developed another mental health disorder, whilst 14% will have recovered (see figure 1).Clinical intervention in the UHR group may reduce the likelihood of the onset of a psychotic disorder
12. However, as most UHR subjects do not develop a psychotic disorder, providing preventative treatment to all of those at risk is clinically inefficient. Identifying biomarkers that could be used to stratify the UHR group according to clinical outcome would enable the selective delivery of preventative interventions to the subgroup that would benefit the most.It is difficult to predict clinical outcomes in an UHR subject on the basis of their clinical features at presentation. Although the clinical assessment at presentation has good diagnostic validity for ruling out a future psychotic disorder (meta analytical sensitivity of UHR assessment = 0.96)
13, it has only a modest ability to rule in a future psychotic disorder (meta-analytical specificity of UHR assessment = 0.47)
13.There is thus a need to find other forms of assessment that can improve the specificity of psychosis prediction. Fusar-Poli et al. (2015) 14 and based on data by Lin et al. (2011) 10 .
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