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

Authors
  • Lei, Du1, 2
  • Pinaya, Walter H L2
  • Young, Jonathan3
  • van Amelsvoort, Therese4
  • Marcelis, Machteld4, 5
  • Donohoe, Gary6
  • Mothersill, David O6
  • Corvin, Aiden7
  • Vieira, Sandra2
  • Huang, Xiaoqi1
  • Lui, Su1
  • Scarpazza, Cristina2, 8
  • Arango, Celso9
  • Bullmore, Ed10
  • Gong, Qiyong1, 11, 12
  • McGuire, Philip2
  • Mechelli, Andrea2
  • 1 Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China. , (China)
  • 2 Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, UK.
  • 3 Department of Neuroimaging, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK.
  • 4 Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands. , (Netherlands)
  • 5 Mental Health Care Institute Eindhoven (GGzE), Eindhoven, The Netherlands. , (Netherlands)
  • 6 School of Psychology & Center for neuroimaging and Cognitive Genomics, NUI Galway University, Galway, Ireland. , (Ireland)
  • 7 Department of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland. , (Ireland)
  • 8 Department of General Psychology, University of Padua, Padua, Italy. , (Italy)
  • 9 Child and Adolescent Department of Psychiatry, Hospital General Universitario Gregorio Marañon, School of Medicine, Universidad Complutense Madrid, IiSGM, CIBERSAM, Madrid, Spain. , (Spain)
  • 10 Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK.
  • 11 Psychoradiology Research Unit of Chinese Academy of Medical Sciences, West China Hospital of Sichuan University, Chengdu, Sichuan, China. , (China)
  • 12 Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China. , (China)
Type
Published Article
Journal
Human Brain Mapping
Publisher
Wiley (John Wiley & Sons)
Publication Date
Apr 01, 2020
Volume
41
Issue
5
Pages
1119–1135
Identifiers
DOI: 10.1002/hbm.24863
PMID: 31737978
Source
Medline
Keywords
Language
English
License
Unknown

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. © 2019 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.

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