Machine learning classification of first-episode schizophrenia spectrum disorders and controls using whole brain white matter fractional anisotropy

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023752%3A_____%2F18%3A43919380" target="_blank" >RIV/00023752:_____/18:43919380 - isvavai.cz</a>

Alternative codes found

RIV/67985807:_____/18:00490053 RIV/00216208:11120/18:43916642 RIV/00023001:_____/18:00076893

Result on the web

<a href="https://bmcpsychiatry.biomedcentral.com/articles/10.1186/s12888-018-1678-y" target="_blank" >https://bmcpsychiatry.biomedcentral.com/articles/10.1186/s12888-018-1678-y</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s12888-018-1678-y" target="_blank" >10.1186/s12888-018-1678-y</a>

Result language

angličtina

Original language name

Machine learning classification of first-episode schizophrenia spectrum disorders and controls using whole brain white matter fractional anisotropy

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

30215 - Psychiatry

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

BMC Psychiatry

ISSN

1471-244X

e-ISSN

—

Volume of the periodical

18

Issue of the periodical within the volume

Article Number: 97

Country of publishing house

GB - UNITED KINGDOM

Number of pages

7

Pages from-to

1-7

UT code for WoS article

000429938400006

EID of the result in the Scopus database

2-s2.0-85045269051