Machine learning (ML) applications on the diagnosis of neuropsychiatric disorders (NPD) have not reached clinical practice yet, as the continuous spectrum of NPD demands more complex, non-binary classification approaches. Herein, a ML-based normative model was created from healthy subjects, which “fails” when tested on schizophrenia patients. In particular, abnormal functional connectivity patterns were found in such patients, in agreement to what has been described in the literature. Moreover, a clustering method and analysis at the individual level indicate that subgroups may exist within the schizophrenia spectrum, suggesting that a personalized and precision-based diagnosis is within reach for such NPD.
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