Identifying Individuals at High Risk of Psychosis: Predictive Utility of Support Vector Machine using Structural and Functional MRI Data

Journal article

Gray matter and cortical thickness reductions have been documented in individuals at clinical high-risk for psychosis and may be more pronounced in those who transition to psychosis. However, these findings rely on small samples and are inconsistent across studies. In this review and meta-analysis we aimed to investigate neuroanatomical correlates of clinical high-risk for psychosis and potential predictors of transition, using a novel metaanalytic method (Seed-based d Mapping with Permutation of Subject Images) and cortical mask, combining data from surface-based and voxel-based morphometry studies. Individuals at clinical high-risk for psychosis who later transitioned to psychosis were compared to those who did not and to controls, and included three statistical maps. Overall, individuals at clinical high-risk for psychosis did not differ from controls, however, within the clinical high-risk for psychosis group, transition to psychosis was associated with less cortical gray matter in the right temporal lobe (Hedges' g = −0.377), anterior cingulate and paracingulate (Hedges' g = −0.391). These findings have the potential to help refine prognostic and etiopathological research in early psychos

Authors: Valli, I; Marquand, AF; Mechelli, A; Raffin, M; Allen, P

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Frontiers Media
• Journal: Frontiers in Psychiatry
• Volume: 7
• Pages: 52-52
• Publication date: 2016-04-08
• DOI: 10.3389/fpsyt.2016.00052
• EISSN: 1664-0640
• ISSN: 1664-0640
• Language: English
• Keywords: Machine learning; Computer science; Psychology; Psychosis; Psychiatry; Support vector machine; Medicine; Schizophrenia (object-oriented programming); Artificial intelligence