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

Nicolas Honnorat, Aoyan Dong, Eva Meisenzahl-Lechner, Nikolaos Koutsouleris, Christos Davatzikos

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


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. Methods: 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. Results: 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. Conclusion: 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.

Original languageEnglish (US)
Pages (from-to)43-50
Number of pages8
JournalSchizophrenia research
StatePublished - Dec 2019
Externally publishedYes


  • Gray matter
  • Machine learning
  • Multivariate pattern analysis
  • Schizophrenia
  • VBM
  • White matter

ASJC Scopus subject areas

  • Psychiatry and Mental health
  • Biological Psychiatry


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