Two distinc neuroanatomica subtypes of schizophrenia revealed using machine learning

Ganesh B. Chand, Dominic B. Dwyer, Guray Erus, Aristeidis Sotiras, Erdem Varol, Dhivya Srinivasan, Jimit Doshi, Raymond Pomponio, Alessandro Pigoni, Paola Dazzan, Rene S. Kahn, Hugo G. Schnack, Marcus V. Zanetti, Eva Meisenzahl, Geraldo F. Busatto, Benedicto Crespo-Facorro, Christos Pantelis, Stephen J. Wood, Chuanjun Zhuo, Russell T. ShinoharaHaochang Shou, Yong Fan, Ruben C. Gur, Raquel E. Gur, Theodore D. Satterthwaite, Nikolaos Koutsouleris, Daniel H. Wolf, Christos Davatzikos

Research output: Contribution to journalArticleAcademicpeer-review


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.

Original languageEnglish
Pages (from-to)1027-1038
Number of pages12
JournalBrain : a journal of neurology
Issue number3
Early online date27 Feb 2020
Publication statusPublished - 1 Mar 2020


  • Neuroanatomical heterogeneity
  • Schizophrenia
  • Semi-supervised machine learning
  • Structural MRI
  • Voxel-wise analysis


Dive into the research topics of 'Two distinc neuroanatomica subtypes of schizophrenia revealed using machine learning'. Together they form a unique fingerprint.

Cite this