title: Individual prediction of long-term outcome in adolescents at ultra-high risk for psychosis: Applying machine learning techniques to brain imaging data.
abstract: An important focus of studies of individuals at ultra-high risk (UHR) for psychosis has been to identify biomarkers to predict which individuals will transition to psychosis. However, the majority of individuals will prove to be resilient and go on to experience remission of their symptoms and function well. The aim of this study was to investigate the possibility of using structural MRI measures collected in UHR adolescents at baseline to quantitatively predict their long-term clinical outcome and level of functioning. We included 64 UHR individuals and 62 typically developing adolescents (12-18 years old at recruitment). At six-year follow-up, we determined resilience for 43 UHR individuals. Support Vector Regression analyses were performed to predict long-term functional and clinical outcome from baseline MRI measures on a continuous scale, instead of the more typical binary classification. This led to predictive correlations of baseline MR measures with level of functioning, and negative and disorganization symptoms. The highest correlation (r = 0.42) was found between baseline subcortical volumes and long-term level of functioning. In conclusion, our results show that structural MRI data can be used to quantitatively predict long-term functional and clinical outcome in UHR individuals with medium effect size, suggesting that there may be scope for predicting outcome at the individual level. Moreover, we recommend classifying individual outcome on a continuous scale, enabling the assessment of different functional and clinical scales separately without the need to set a threshold. Hum Brain Mapp 38:704-714, 2017. © 2016 Wiley Periodicals, Inc.
authors.affiliation: Department of Psychiatry, University Medical Center Utrecht, Brain Center Rudolf Magnus, Utrecht, the Netherlands.
authors.forename: Sanne
authors.initials: S
authors.lastname: de Wit
authors.affiliation: Department of Clinical Child and Adolescent Studies, Leiden University, Leiden, the Netherlands.|Leiden Institute for Brain and Cognition, Leiden, the Netherlands.
authors.forename: Tim B
authors.initials: TB
authors.lastname: Ziermans
authors.affiliation: Department of Psychiatry, University Medical Center Utrecht, Brain Center Rudolf Magnus, Utrecht, the Netherlands.
authors.forename: M
authors.initials: M
authors.lastname: Nieuwenhuis
authors.affiliation: Department of Psychiatry, University Medical Center Utrecht, Brain Center Rudolf Magnus, Utrecht, the Netherlands.
authors.forename: Patricia F
authors.initials: PF
authors.lastname: Schothorst
authors.affiliation: Department of Psychiatry, University Medical Center Utrecht, Brain Center Rudolf Magnus, Utrecht, the Netherlands.
authors.forename: Herman
authors.initials: H
authors.lastname: van Engeland
authors.affiliation: Department of Psychiatry, University Medical Center Utrecht, Brain Center Rudolf Magnus, Utrecht, the Netherlands.
authors.forename: René S
authors.initials: RS
authors.lastname: Kahn
authors.affiliation: Department of Psychiatry, University Medical Center Utrecht, Brain Center Rudolf Magnus, Utrecht, the Netherlands.
authors.forename: Sarah
authors.initials: S
authors.lastname: Durston
authors.affiliation: Department of Psychiatry, University Medical Center Utrecht, Brain Center Rudolf Magnus, Utrecht, the Netherlands.
authors.forename: Hugo G
authors.initials: HG
authors.lastname: Schnack
mesh_terms: D000293:Adolescent; D001921:Brain / Q000000981:diagnostic imaging*; D002648:Child; D006801:Humans; D007091:Image Processing, Computer-Assisted; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging; D008297:Male; D009483:Neuropsychological Tests; D011237:Predictive Value of Tests; D011569:Psychiatric Status Rating Scales; D011618:Psychotic Disorders / Q000000981:diagnostic imaging / Q000473:pathology*; D012372:ROC Curve; D012307:Risk Factors
keywords: brain imaging; machine-learning; outcome; prediction; psychosis; ultra-high risk