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

Journal: Human brain mapping
PMID: 32469448

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, including regional cortical thickness and gray matter volume, static and dynamic resting-state connectivity, and/or baseline clinical and demographic variables. Relative change in overall symptom severity between baseline and 1-year follow-up varied markedly among individuals (interquartile range: 55%). Dynamic resting-state connectivity measured within the default-mode network was the most accurate single predictor of change in positive (accuracy: 87%), negative (83%), and overall symptom severity (77%) at follow-up. Incorporating predictors based on regional cortical thickness, gray matter volume, and baseline clinical variables did not markedly improve prediction accuracy and the prognostic utility of these predictors in isolation was moderate (<70%). Worsening negative symptoms at 1-year follow-up were predicted by hyper-connectivity and hypo-dynamism within the default-mode network at baseline assessment, while hypo-connectivity and hyper-dynamism predicted worsening positive symptoms. Given the modest sample size investigated, we recommend giving precedence to the relative ranking of the predictors investigated in this study, rather than the prediction accuracy estimates.

Authors

  • Akhil Kottaram
    Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia.
  • Leigh A Johnston
    Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia.
  • Ye Tian
    State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics and Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen, 361102, China.
  • Eleni P Ganella
    Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Victoria, Australia.
  • Liliana Laskaris
    Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Victoria, Australia.
  • Luca Cocchi
    Clinical Brain Networks Group, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
  • Patrick McGorry
    Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, Australia; The Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia.
  • Christos Pantelis
    Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia.
  • Ramamohanarao Kotagiri
    Department of Computing and Information Systems, The University of Melbourne, Melbourne, Victoria, Australia.
  • Vanessa Cropley
    Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Victoria, Australia.
  • Andrew Zalesky
    Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia.

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