Journal article

Predicting subclinical psychotic-like experiences on a continuum using machine learning

Jeremy A Taylor, Kit Melissa Larsen, Ilvana Dzafic, Marta I Garrido

NEUROIMAGE | ACADEMIC PRESS INC ELSEVIER SCIENCE | Published : 2021

Abstract

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 m..

View full abstract

Grants

Awarded by Australian Research Council Cen-tre of Excellence for Integrative Brain Function (ARC Centre Grant)


Awarded by University of Queensland Fellowship


Awarded by Foundation Research Excellence Award


Funding Acknowledgements

This work was supported by the Australian Research Council Cen-tre of Excellence for Integrative Brain Function (ARC Centre Grant CE140100007) , a University of Queensland Fellowship (2016000071) and a Foundation Research Excellence Award (2016001844) to Marta I Garrido.