Journal article

Multi-dimensional predictions of psychotic symptoms via machine learning

Jeremy A Taylor, Kit M Larsen, Marta I Garrido

Human Brain Mapping | WILEY | Published : 2020


The diagnostic criteria for schizophrenia comprise a diverse range of heterogeneous symptoms. As a result, individuals each present a distinct set of symptoms despite having the same overall diagnosis. Whilst previous machine learning studies have primarily focused on dichotomous patient-control classification, we predict the severity of each individual symptom on a continuum. We applied machine learning regression within a multi-modal fusion framework to fMRI and behavioural data acquired during an auditory oddball task in 80 schizophrenia patients. Brain activity was highly predictive of some, but not all symptoms, namely hallucinations, avolition, anhedonia and attention. Critically, each..

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Awarded by Australian Research Council Centre of Excellence for Integrative Brain Function

Awarded by University of Queensland

Awarded by Foundation Research Excellence Award

Funding Acknowledgements

Australian Research Council Centre of Excellence for Integrative Brain Function, Grant/Award Number: CE140100007; University of Queensland, Grant/Award Number: 2016000071; Foundation Research Excellence Award, Grant/Award Number: 2016001844