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

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.
Source: Schizophrenia Research - Category: Psychiatry Authors: Source Type: research