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Neuroanatomical heterogeneity of schizophrenia revealed by semi-supervised machine learning methods.

Authors
  • Honnorat, Nicolas1
  • Dong, Aoyan2
  • Meisenzahl-Lechner, Eva3
  • Koutsouleris, Nikolaos3
  • Davatzikos, Christos2
  • 1 Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA. Electronic address: [email protected]
  • 2 Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA.
  • 3 Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany. , (Germany)
Type
Published Article
Journal
Schizophrenia research
Publication Date
Dec 01, 2019
Volume
214
Pages
43–50
Identifiers
DOI: 10.1016/j.schres.2017.12.008
PMID: 29274735
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Schizophrenia is associated with heterogeneous clinical symptoms and neuroanatomical alterations. In this work, we aim to disentangle the patterns of neuroanatomical alterations underlying a heterogeneous population of patients using a semi-supervised clustering method. We apply this strategy to a cohort of patients with schizophrenia of varying extends of disease duration, and we describe the neuroanatomical, demographic and clinical characteristics of the subtypes discovered. We analyze the neuroanatomical heterogeneity of 157 patients diagnosed with Schizophrenia, relative to a control population of 169 subjects, using a machine learning method called CHIMERA. CHIMERA clusters the differences between patients and a demographically-matched population of healthy subjects, rather than clustering patients themselves, thereby specifically assessing disease-related neuroanatomical alterations. Voxel-Based Morphometry was conducted to visualize the neuroanatomical patterns associated with each group. The clinical presentation and the demographics of the groups were then investigated. Three subgroups were identified. The first two differed substantially, in that one involved predominantly temporal-thalamic-peri-Sylvian regions, whereas the other involved predominantly frontal regions and the thalamus. Both subtypes included primarily male patients. The third pattern was a mix of these two and presented milder neuroanatomic alterations and comprised a comparable number of men and women. VBM and statistical analyses suggest that these groups could correspond to different neuroanatomical dimensions of schizophrenia. Our analysis suggests that schizophrenia presents distinct neuroanatomical variants. This variability points to the need for a dimensional neuroanatomical approach using data-driven, mathematically principled multivariate pattern analysis methods, and should be taken into account in clinical studies. Copyright © 2017 Elsevier B.V. All rights reserved.

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