Predicting subclinical psychotic-like experiences on a continuum using machine learning
Jeremy A. Taylor,
Kit Melissa Larsen,
Ilvana Dzafic,
Marta I. Garrido
Affiliations
Jeremy A. Taylor
Melbourne School of Psychological Sciences, University of Melbourne, Australia; Queensland Brain Institute, University of Queensland, Australia; Corresponding author at: Melbourne School of Psychological Sciences, University of Melbourne, Redmond Barry Building, Tin Alley, Parkville, Victoria, Australia.
Kit Melissa Larsen
Queensland Brain Institute, University of Queensland, Australia; Australian Research Council Centre of Excellence for Integrative Brain Function; Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark; Child and Adolescent Mental Health Care, Mental Health Services Capital Region Copenhagen, University of Copenhagen, Denmark
Ilvana Dzafic
Melbourne School of Psychological Sciences, University of Melbourne, Australia; Queensland Brain Institute, University of Queensland, Australia; Australian Research Council Centre of Excellence for Integrative Brain Function; Centre for Advanced Imaging, University of Queensland, Australia
Marta I. Garrido
Melbourne School of Psychological Sciences, University of Melbourne, Australia; Queensland Brain Institute, University of Queensland, Australia; Australian Research Council Centre of Excellence for Integrative Brain Function; Centre for Advanced Imaging, University of Queensland, Australia
Previous studies applying machine learning methods to psychosis have primarily been concerned with the binary classification of chronic schizophrenia patients and healthy controls. The aim of this study was to use electroencephalographic (EEG) data and pattern recognition to predict subclinical psychotic-like experiences on a continuum between these two extremes in otherwise healthy people. We applied two different approaches to an auditory oddball regularity learning task obtained from N = 73 participants:A feature extraction and selection routine incorporating behavioural measures, event-related potential components and effective connectivity parameters;Regularisation of spatiotemporal maps of event-related potentials.Using the latter approach, optimal performance was achieved using the response to frequent, predictable sounds. Features within the P50 and P200 time windows had the greatest contribution toward lower Prodromal Questionnaire (PQ) scores and the N100 time window contributed most to higher PQ scores. As a proof-of-concept, these findings demonstrate that EEG data alone are predictive of individual psychotic-like experiences in healthy people. Our findings are in keeping with the mounting evidence for altered sensory responses in schizophrenia, as well as the notion that psychosis may exist on a continuum expanding into the non-clinical population.
