07-Connectivity of the anterior insula differentiates participants with first-episode schizophrenia spectrum disorders from controls: A machine-learning study

Early diagnosis of schizophrenia might reduce the negative impact of the untreated disease. Progressive functional/structural changes were repeatedly detected using classical between-group statistics. However, these findings have been due to their low sensitivity and specificity not clinically useful. Machine learning methods are able to learn from the data and make predictions on the individual level, which might have a diagnostic potential. We performed a classification of patients with the first episode of schizophrenia (FES) and healthy controls (HC) from the resting state functional connectivity (rsFC) and fractional anisotropy (FA) using machine learning on 1:1 age and sex matched samples of 63/63 patients/HC (rsFC) and 77/77 (DTI).

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