Predicting individual improvement in schizophrenia symptom severity at 1-year follow-up: Comparison of connectomic, structural, and clinical predictors

Akhil Kottaram; Leigh A Johnston; Ye Tian; Eleni P Ganella; Liliana Laskaris; Luca Cocchi; Patrick McGorry; Christos Pantelis; Ramamohanarao Kotagiri; Vanessa Cropley; Andrew Zalesky

Journal article

Predicting individual improvement in schizophrenia symptom severity at 1-year follow-up: Comparison of connectomic, structural, and clinical predictors

Akhil Kottaram, Leigh A Johnston, Ye Tian, Eleni P Ganella, Liliana Laskaris, Luca Cocchi, Patrick McGorry, Christos Pantelis, Ramamohanarao Kotagiri, Vanessa Cropley, Andrew Zalesky

Human Brain Mapping | Wiley | Published : 2020

DOI: 10.1002/hbm.25020

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Abstract

In a machine learning setting, this study aims to compare the prognostic utility of connectomic, brain structural, and clinical/demographic predictors of individual change in symptom severity in individuals with schizophrenia. Symptom severity at baseline and 1‐year follow‐up was assessed in 30 individuals with a schizophrenia‐spectrum disorder using the Brief Psychiatric Rating Scale. Structural and functional neuroimaging was acquired in all individuals at baseline. Machine learning classifiers were trained to predict whether individuals improved or worsened with respect to positive, negative, and overall symptom severity. Classifiers were trained using various combinations of predictors, ..

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University of Melbourne Researchers

Leigh Johnston Author

Ye Ella Tian Author

Patrick McGorry Author

Christos Pantelis Author

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Grants

Awarded by NHMRC

Awarded by University of Melbourne Early Career Researcher

Awarded by Australian National Health and Medical Research Council (NHMRC)

Funding Acknowledgements

NHMRC, Grant/Award Numbers: APP1138711, APP1099082, APP1136649, 1105825, 628386, 628880; University of Melbourne Early Career Researcher, Grant/Award Number: 601253; Australian National Health and Medical Research Council (NHMRC), Grant/Award Number: 1065742