Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning.

Journal: Brain : a journal of neurology
Published Date:

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.

Authors

  • Ganesh B Chand
    Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
  • Dominic B Dwyer
    Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany;
  • Guray Erus
    Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Aristeidis Sotiras
    Department of Radiology and Institute of Informatics, Washington University in St. Luis, St. Luis, MO63110, USA.
  • Erdem Varol
    Section for Biomedical Image Analysis, Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA. Electronic address: erdem.varol@uphs.upenn.edu.
  • Dhivya Srinivasan
    Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Jimit Doshi
    Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
  • Raymond Pomponio
    Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
  • Alessandro Pigoni
    Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Department of Neurosciences and Mental Health, Milan, Italy; University of Milan, Department of Pathophysiology and Transplantation, Milan, Italy.
  • Paola Dazzan
    Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom.
  • René S Kahn
    Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, The Netherlands.
  • Hugo G Schnack
    From the Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Centre Utrecht, Utrecht.
  • Marcus V Zanetti
    Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil.
  • Eva Meisenzahl
    Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany.
  • Geraldo F Busatto
    Laboratório de Neuroimagem em Psiquiatria (LIM21), Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo (USP), São Paulo, SP, Brazil.
  • Benedicto Crespo-Facorro
    Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM), Madrid, Spain.
  • Christos Pantelis
    Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia.
  • Stephen J Wood
    Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia.
  • Chuanjun Zhuo
    Tianjin Anning Hospital, Tianjin, China.
  • Russell T Shinohara
    Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Haochang Shou
    Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Yong Fan
    CPB/ECMO Children's Hospital, Zhejiang University School of Medicine, 310052 Hangzhou, Zhejiang, China.
  • Ruben C Gur
    Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA.
  • Raquel E Gur
    Brain Behavior Laboratory, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA.
  • Theodore D Satterthwaite
    Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA.
  • Nikolaos Koutsouleris
    Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.
  • Daniel H Wolf
    Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA.
  • Christos Davatzikos
    Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.