BACKGROUND: Early diagnosis of schizophrenia could improve the outcome of the illness. Unlike classical between-group comparisons, machine learning can identify subtle disease patterns on a single subject level, which could help realize the potential of MRI in establishing a psychiatric diagnosis. Machine learning has previously been predominantly tested on gray-matter structural or functional MRI data. In this paper we used a machine learning classifier to differentiate patients with a first episode of schizophrenia-spectrum disorder (FES) from healthy controls using diffusion tensor imaging. METHODS: We applied linear support-vector machine (SVM) and traditional tract based spatial statistics between group analyses to brain fractional anisotropy (FA) data from 77 FES and 77 age and sex matched healthy controls. We also evaluated the effects of medication and symptoms on the SVM classification. RESULTS: The SVM distinguished between patients and controls with significant accuracy of 62.34% (p = 0.005). Participants with FES showed widespread FA reductions relative to controls in a large cluster (N  = 56,647 voxels, corrected p = 0.002). The white matter regions, which contributed to the correct identification of participants with FES, overlapped with the regions, which showed lower FA in patients relative to controls. There was no association between the classification performance and medication or symptoms. CONCLUSIONS: Our results provide a proof of concept that SVM might help differentiate FES patients early in the course of illness from healthy controls using white-matter fractional anisotropy. As there was no effect of medications or symptoms, the SVM classification seemed to be based on trait rather than state markers and appeared to capture the lower FA in FES participants relative to controls.

3rd Faculty of Medicine Charles University Ruska 87 100 00 Prague Czech Republic National Institute of Mental Health Topolova 748 250 67 Klecany Czech Republic

Department of Psychiatry and Psychotherapy Otto von Guericke University Leipziger Str 44 39120 Magdeburg Germany

Department of Psychiatry and Psychotherapy Otto von Guericke University Leipziger Str 44 39120 Magdeburg Germany 3rd Faculty of Medicine Charles University Ruska 87 100 00 Prague Czech Republic National Institute of Mental Health Topolova 748 250 67 Klecany Czech Republic

National Institute of Mental Health Topolova 748 250 67 Klecany Czech Republic Department of Psychiatry Dalhousie University QEII HSC A J Lane Bldg Room 3093 5909 Veteran's Memorial Lane Halifax NS B3H 2E2 Canada

National Institute of Mental Health Topolova 748 250 67 Klecany Czech Republic Institute of Computer Science of the Czech Academy of Sciences Pod Vodarenskou vezi 271 2 182 07 Prague Czech Republic

National Institute of Mental Health Topolova 748 250 67 Klecany Czech Republic Institute of Computer Science of the Czech Academy of Sciences Pod Vodarenskou vezi 271 2 182 07 Prague Czech Republic Faculty of Nuclear Sciences and Physical Engineering Czech Technical University Prague Prague Brehova 78 7 110 00 Praha Czech Republic

National Institute of Mental Health Topolova 748 250 67 Klecany Czech Republic MR Unit Department of Diagnostic and Interventional Radiology Institute for Clinical and Experimental Medicine Videnska 1958 9 140 21 Prague Czech Republic

References provided by Crossref.org