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

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. 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 t

SUBMITTER: Honnorat N 

PROVIDER: S-EPMC6013334 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature