Machine Learning Classification of First-Episode Schizophrenia Spectrum Disorders and Controls Using Whole Brain White Matter Fractional Anisotropy

0490053 - ÚI 2019 RIV GB eng J - Článek v odborném periodiku
Mikoláš, P. - Hlinka, Jaroslav - Škoch, A. - Pitra, Zbyněk - Frodl, T. - Španiel, F. - Hájek, T.
Machine Learning Classification of First-Episode Schizophrenia Spectrum Disorders and Controls Using Whole Brain White Matter Fractional Anisotropy.
BMC Psychiatry. Roč. 18, 10 April (2018), č. článku 97. E-ISSN 1471-244X
Grant CEP: GA ČR GA17-01251S
Grant ostatní: GA MŠk(CZ) LO1611; GA MZd(CZ) NV16-32696A
Institucionální podpora: RVO:67985807
Klíčová slova: First-episode schizophrenia spectrum disorders * Diffusion tensor imaging * Support vector machines * Magnetic resonance imaging
Obor OECD: Neurosciences (including psychophysiology
Impakt faktor: 2.666, rok: 2018

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.
Trvalý link: http://hdl.handle.net/11104/0284354