Keywords: Psychiatric Disorders, fMRI (resting state)Quantifiable biomarkers are urgently required to explore the potential physiological mechanism of schizophrenia and improve its diagnostic accuracy. Resting-state functional MRI (rs-fMRI)-based radiomics analysis obtained great classification performance, and it could be generalized to different brain atlases. The regions that we identified as discriminative features mainly included bilateral dorsal caudate and front-parietal, somato-motor, limbic, and default mode networks. Our findings showed that radiomics-based machine learning method could facilitate us to understand the potential pathological mechanism of schizophrenia more comprehensively and contribute to the accurate diagnosis of patients with schizophrenia.
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