title: Prediction of psychosis using neural oscillations and machine learning in neuroleptic-naïve at-risk patients.
abstract: This study investigates whether abnormal neural oscillations, which have been shown to precede the onset of frank psychosis, could be used towards the individualised prediction of psychosis in clinical high-risk patients.
  
  
  
  We assessed the individualised prediction of psychosis by detecting specific patterns of beta and gamma oscillations using machine-learning algorithms. Prediction models were trained and tested on 53 neuroleptic-naïve patients with a clinical high-risk for psychosis. Of these, 18 later transitioned to psychosis. All patients were followed up for at least 3 years. For an honest estimation of the generalisation capacity, the predictive performance of the models was assessed in unseen test cases using repeated nested cross-validation.
  
  
  
  Transition to psychosis could be predicted from current-source density (CSD; area under the curve [AUC] = 0.77), but not from lagged phase synchronicity data (LPS; AUC = 0.56). Combining both modalities did not improve the predictive accuracy (AUC = 0.78). The left superior temporal gyrus, the left inferior parietal lobule and the precuneus most strongly contributed to the prediction of psychosis.
  
  
  
  Our results suggest that CSD measurements extracted from clinical resting state EEG can help to improve the prediction of psychosis on a single-subject level.
authors.affiliation: a University of Basel Psychiatric Clinics, Center for Gender Research and Early Detection , Basel , Switzerland.
authors.forename: Avinash
authors.initials: A
authors.lastname: Ramyead
authors.affiliation: a University of Basel Psychiatric Clinics, Center for Gender Research and Early Detection , Basel , Switzerland.
authors.forename: Erich
authors.initials: E
authors.lastname: Studerus
authors.affiliation: b Neuropsychopharmacology and Brain Imaging Research Unit, University Hospital of Psychiatry , Zurich , Switzerland.
authors.forename: Michael
authors.initials: M
authors.lastname: Kometer
authors.affiliation: a University of Basel Psychiatric Clinics, Center for Gender Research and Early Detection , Basel , Switzerland.
authors.forename: Martina
authors.initials: M
authors.lastname: Uttinger
authors.affiliation: c Department of Neurology , University Hospital Basel , Basel , Switzerland.
authors.forename: Ute
authors.initials: U
authors.lastname: Gschwandtner
authors.affiliation: c Department of Neurology , University Hospital Basel , Basel , Switzerland.
authors.forename: Peter
authors.initials: P
authors.lastname: Fuhr
authors.affiliation: a University of Basel Psychiatric Clinics, Center for Gender Research and Early Detection , Basel , Switzerland.
authors.forename: Anita
authors.initials: A
authors.lastname: Riecher-Rössler
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D004569:Electroencephalography; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male; D015999:Multivariate Analysis; D011569:Psychiatric Status Rating Scales; D011618:Psychotic Disorders / Q000175:diagnosis*; D012372:ROC Curve; D012559:Schizophrenia / Q000175:diagnosis*; D012565:Schizophrenic Psychology; D013702:Temporal Lobe / Q000503:physiopathology*; D055815:Young Adult
keywords: EEG; Schizophrenia; current source density; machine learning; psychosis