Abstract:Given the heterogeneity and possible disease progression in schizophrenia, identifying the neurobiological subtypes and progression patterns in each patient may lead to the development of clinically useful biomarkers. In this cross-sectional study, we adopted data-driven machine-learning techniques to classify and stage the progression patterns of brain morphological changes in schizophrenia and investigate the association with treatment resistance. We included 177 patients with schizophrenia, characterized by… Show more
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