LAUSR

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). Support vector machine distinguished between patients and controls with an accuracy 73.0% (p = 0.001) (rsFC) and 62.34% (p = 0.005) (DTI). These results were not influenced by symptoms or medication. Our work shows that rsFC and FA might be used to classify patients with FES and HC on the individual level. The classification reflects ‘’trait” markers of FES, as the symptoms and medication had no significant effect. Additionally the results of the analysis of rsFC show the significance of anterior insula in the pathophysiology of FES.