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Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning.

Authors
  • Chand, Ganesh B1, 2
  • Dwyer, Dominic B3
  • Erus, Guray1, 2
  • Sotiras, Aristeidis1, 2, 4
  • Varol, Erdem1, 2, 5
  • Srinivasan, Dhivya1, 2
  • Doshi, Jimit1, 2
  • Pomponio, Raymond1, 2
  • Pigoni, Alessandro3, 6
  • Dazzan, Paola7
  • Kahn, Rene S8
  • Schnack, Hugo G9
  • Zanetti, Marcus V10, 11
  • Meisenzahl, Eva12
  • Busatto, Geraldo F10
  • Crespo-Facorro, Benedicto13, 14
  • Pantelis, Christos15
  • Wood, Stephen J16, 17, 18
  • Zhuo, Chuanjun19, 20
  • Shinohara, Russell T2, 21
  • And 8 more
  • 1 Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
  • 2 Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
  • 3 Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany. , (Germany)
  • 4 Department of Radiology, School of Medicine, Washington University in St. Louis, St. Louis, USA.
  • 5 Department of Statistics, Zuckerman Institute, Columbia University, New York, USA.
  • 6 Department of Neurosciences and Mental Health, University of Milan, Milan, Italy. , (Italy)
  • 7 Institute of Psychiatry, King's College London, London, UK.
  • 8 Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, USA.
  • 9 Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands. , (Netherlands)
  • 10 Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil. , (Brazil)
  • 11 Hospital Sírio-Libanês, São Paulo, Brazil. , (Brazil)
  • 12 LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany. , (Germany)
  • 13 University of Cantabria; IDIVAL-CIBERSAM, Cantabria, Spain. , (Spain)
  • 14 Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, University of Sevilla, Spain. , (Spain)
  • 15 Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia. , (Australia)
  • 16 Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia. , (Australia)
  • 17 Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia. , (Australia)
  • 18 School of Psychology, University of Birmingham, Edgbaston, UK.
  • 19 Department of Psychiatric-Neuroimaging-Genetics and Co-morbidity Laboratory (PNGC-Lab), Nankai University Affiliated Tianjin Anding Hospital, Tianjin Medical University, Tianjin, China. , (China)
  • 20 Department of Psychiatry, Tianjin Medical University, Tianjin, China. , (China)
  • 21 Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
  • 22 Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
Type
Published Article
Journal
Brain
Publisher
Oxford University Press
Publication Date
Mar 01, 2020
Volume
143
Issue
3
Pages
1027–1038
Identifiers
DOI: 10.1093/brain/awaa025
PMID: 32103250
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Neurobiological heterogeneity in schizophrenia is poorly understood and confounds current analyses. We investigated neuroanatomical subtypes in a multi-institutional multi-ethnic cohort, using novel semi-supervised machine learning methods designed to discover patterns associated with disease rather than normal anatomical variation. Structural MRI and clinical measures in established schizophrenia (n = 307) and healthy controls (n = 364) were analysed across three sites of PHENOM (Psychosis Heterogeneity Evaluated via Dimensional Neuroimaging) consortium. Regional volumetric measures of grey matter, white matter, and CSF were used to identify distinct and reproducible neuroanatomical subtypes of schizophrenia. Two distinct neuroanatomical subtypes were found. Subtype 1 showed widespread lower grey matter volumes, most prominent in thalamus, nucleus accumbens, medial temporal, medial prefrontal/frontal and insular cortices. Subtype 2 showed increased volume in the basal ganglia and internal capsule, and otherwise normal brain volumes. Grey matter volume correlated negatively with illness duration in Subtype 1 (r = -0.201, P = 0.016) but not in Subtype 2 (r = -0.045, P = 0.652), potentially indicating different underlying neuropathological processes. The subtypes did not differ in age (t = -1.603, df = 305, P = 0.109), sex (chi-square = 0.013, df = 1, P = 0.910), illness duration (t = -0.167, df = 277, P = 0.868), antipsychotic dose (t = -0.439, df = 210, P = 0.521), age of illness onset (t = -1.355, df = 277, P = 0.177), positive symptoms (t = 0.249, df = 289, P = 0.803), negative symptoms (t = 0.151, df = 289, P = 0.879), or antipsychotic type (chi-square = 6.670, df = 3, P = 0.083). Subtype 1 had lower educational attainment than Subtype 2 (chi-square = 6.389, df = 2, P = 0.041). In conclusion, we discovered two distinct and highly reproducible neuroanatomical subtypes. Subtype 1 displayed widespread volume reduction correlating with illness duration, and worse premorbid functioning. Subtype 2 had normal and stable anatomy, except for larger basal ganglia and internal capsule, not explained by antipsychotic dose. These subtypes challenge the notion that brain volume loss is a general feature of schizophrenia and suggest differential aetiologies. They can facilitate strategies for clinical trial enrichment and stratification, and precision diagnostics. © The Author(s) (2020). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For permissions, please email: journals.p[email protected]

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