Journal article

Auditory prediction errors as individual biomarkers of schizophrenia

JA Taylor, N Matthews, PT Michie, MJ Rosa, MI Garrido

NeuroImage: Clinical | ELSEVIER SCI LTD | Published : 2017

Abstract

Schizophrenia is a complex psychiatric disorder, typically diagnosed through symptomatic evidence collected through patient interview. We aim to develop an objective biologically-based computational tool which aids diagnosis and relies on accessible imaging technologies such as electroencephalography (EEG). To achieve this, we used machine learning techniques and a combination of paradigms designed to elicit prediction errors or Mismatch Negativity (MMN) responses. MMN, an EEG component elicited by unpredictable changes in sequences of auditory stimuli, has previously been shown to be reduced in people with schizophrenia and this is arguably one of the most reproducible neurophysiological ma..

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Grants

Awarded by University of Queensland


Awarded by National Health and Medical Research Council NHMRC


Funding Acknowledgements

This work was funded by a University of Queensland Early Career Researcher Grant (2013002373) and Fellowship (2016000071) to MIG. Data collection was funded by the National Health and Medical Research Council NHMRC: (Project Grant ID 209828) and was supported by the Schizophrenia Research Institute (SRI) and Hunter Medical Research Institute (HMRI). We thank Stanley Catts for helping with recruitment, Anderson Winkler and Anton Lord for discussions on statistical analysis, and Janaina Mourao-Miranda for discussions on machine learning methods.