Number of the records: 1
Machine Learning Classification of First-Episode Schizophrenia Spectrum Disorders and Controls Using Whole Brain White Matter Fractional Anisotropy
- 1.
SYSNO ASEP 0490053 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Machine Learning Classification of First-Episode Schizophrenia Spectrum Disorders and Controls Using Whole Brain White Matter Fractional Anisotropy Author(s) Mikoláš, P. (CZ)
Hlinka, Jaroslav (UIVT-O) RID, SAI, ORCID
Škoch, A. (CZ)
Pitra, Zbyněk (UIVT-O) RID, ORCID, SAI
Frodl, T. (DE)
Španiel, F. (CZ)
Hájek, T. (CZ)Article number 97 Source Title BMC Psychiatry
Roč. 18, 10 April (2018)Number of pages 7 s. Language eng - English Country GB - United Kingdom Keywords First-episode schizophrenia spectrum disorders ; Diffusion tensor imaging ; Support vector machines ; Magnetic resonance imaging Subject RIV FH - Neurology OECD category Neurosciences (including psychophysiology R&D Projects GA17-01251S GA ČR - Czech Science Foundation (CSF) Institutional support UIVT-O - RVO:67985807 UT WOS 000429938400006 EID SCOPUS 85045269051 DOI 10.1186/s12888-018-1678-y Annotation 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. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2019
Number of the records: 1