Integrating machining learning and multimodal neuroimaging to detect schizophrenia at the level of the individual.

Journal: Human brain mapping
Published Date: Apr 1, 2020

Abstract

Schizophrenia is a severe psychiatric disorder associated with both structural and functional brain abnormalities. In the past few years, there has been growing interest in the application of machine learning techniques to neuroimaging data for the diagnostic and prognostic assessment of this disorder. However, the vast majority of studies published so far have used either structural or functional neuroimaging data, without accounting for the multimodal nature of the disorder. Structural MRI and resting-state functional MRI data were acquired from a total of 295 patients with schizophrenia and 452 healthy controls at five research centers. We extracted features from the data including gray matter volume, white matter volume, amplitude of low-frequency fluctuation, regional homogeneity and two connectome-wide based metrics: structural covariance matrices and functional connectivity matrices. A support vector machine classifier was trained on each dataset separately to distinguish the subjects at individual level using each of the single feature as well as their combination, and 10-fold cross-validation was used to assess the performance of the model. Functional data allow higher accuracy of classification than structural data (mean 82.75% vs. 75.84%). Within each modality, the combination of images and matrices improves performance, resulting in mean accuracies of 81.63% for structural data and 87.59% for functional data. The use of all combined structural and functional measures allows the highest accuracy of classification (90.83%). We conclude that combining multimodal measures within a single model is a promising direction for developing biologically informed diagnostic tools in schizophrenia.

Authors

  • Du Lei
    Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
  • Walter H L Pinaya
    Center of Mathematics, Computation, and Cognition. Universidade Federal do ABC, Santo André, Brazil.
  • Jonathan Young
    Novai Ltd, Reading, United Kingdom.
  • Therese van Amelsvoort
    Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands.
  • Machteld Marcelis
    Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands.
  • Gary Donohoe
    School of Psychology, NUI Galway, Galway, Ireland.
  • David O Mothersill
    School of Psychology & Center for neuroimaging and Cognitive Genomics, NUI Galway University, Galway, Ireland.
  • Aiden Corvin
    Department of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland.
  • Sandra Vieira
    Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, UK.
  • Xiaoqi Huang
    Huaxi MR Research Center (HMRRC) Department of Radiology, West China Hospital Sichuan University, Chengdu, 610041, China. julianahuang@163.com.
  • Su Lui
    Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China ; Radiology Department of the Second Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China.
  • Cristina Scarpazza
    Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, UK.
  • Celso Arango
  • Ed Bullmore
    Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK.
  • Qiyong Gong
    Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Philip McGuire
    Institute of Psychiatry, King's College London, London, United Kingdom.
  • Andrea Mechelli
    Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK. Electronic address: a.mechelli@kcl.ac.uk.

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