Your browser doesn't support javascript.
loading
Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning.
Chand, Ganesh B; Dwyer, Dominic B; Erus, Guray; Sotiras, Aristeidis; Varol, Erdem; Srinivasan, Dhivya; Doshi, Jimit; Pomponio, Raymond; Pigoni, Alessandro; Dazzan, Paola; Kahn, Rene S; Schnack, Hugo G; Zanetti, Marcus V; Meisenzahl, Eva; Busatto, Geraldo F; Crespo-Facorro, Benedicto; Pantelis, Christos; Wood, Stephen J; Zhuo, Chuanjun; Shinohara, Russell T; Shou, Haochang; Fan, Yong; Gur, Ruben C; Gur, Raquel E; Satterthwaite, Theodore D; Koutsouleris, Nikolaos; Wolf, Daniel H; Davatzikos, Christos.
Afiliação
  • Chand GB; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
  • Dwyer DB; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
  • Erus G; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany.
  • Sotiras A; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
  • Varol E; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
  • Srinivasan D; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
  • Doshi J; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
  • Pomponio R; Department of Radiology, School of Medicine, Washington University in St. Louis, St. Louis, USA.
  • Pigoni A; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
  • Dazzan P; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
  • Kahn RS; Department of Statistics, Zuckerman Institute, Columbia University, New York, USA.
  • Schnack HG; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
  • Zanetti MV; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
  • Meisenzahl E; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
  • Busatto GF; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
  • Crespo-Facorro B; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
  • Pantelis C; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
  • Wood SJ; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany.
  • Zhuo C; Department of Neurosciences and Mental Health, University of Milan, Milan, Italy.
  • Shinohara RT; Institute of Psychiatry, King's College London, London, UK.
  • Shou H; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, USA.
  • Fan Y; Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands.
  • Gur RC; Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil.
  • Gur RE; Hospital Sírio-Libanês, São Paulo, Brazil.
  • Satterthwaite TD; LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany.
  • Koutsouleris N; Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil.
  • Wolf DH; University of Cantabria; IDIVAL-CIBERSAM, Cantabria, Spain.
  • Davatzikos C; Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, University of Sevilla, Spain.
Brain ; 143(3): 1027-1038, 2020 03 01.
Article em En | MEDLINE | ID: mdl-32103250
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.
Assuntos
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Esquizofrenia / Substância Cinzenta / Substância Branca / Aprendizado de Máquina Tipo de estudo: Clinical_trials / Observational_studies / Risk_factors_studies Limite: Adult / Female / Humans / Male Idioma: En Revista: Brain Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Esquizofrenia / Substância Cinzenta / Substância Branca / Aprendizado de Máquina Tipo de estudo: Clinical_trials / Observational_studies / Risk_factors_studies Limite: Adult / Female / Humans / Male Idioma: En Revista: Brain Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Estados Unidos