Prediction of psychosis using neural oscillations and machine learning in neuroleptic-naïve at-risk patients. (18th May 2016)

• Record Type: Journal Article
• Authors: Ramyead, Avinash; Studerus, Erich; Kometer, Michael; Uttinger, Martina; Gschwandtner, Ute; Fuhr, Peter; Riecher-Rössler, Anita
• Is Part Of: World journal of biological psychiatry. Volume 17:Number 4(2016)
• Page Start: 285
• Page End: 295
• Publication Date: 2016-05-18
• Subjects: Schizophrenia -- psychosis -- machine learning -- EEG -- current source density; Biological psychiatry -- Periodicals; Biological Psychiatry -- Periodicals; 616.89
• DOI: 10.3109/15622975.2015.1083614
• Languages: English
• ISSNs: 1562-2975

Abstract

Abstract: Objectives : 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. Methods : 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. Results : 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. Conclusions : 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.