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. 2019 Jul 5;19(1):210.
doi: 10.1186/s12888-019-2184-6.

Multivariate classification of drug-naive obsessive-compulsive disorder patients and healthy controls by applying an SVM to resting-state functional MRI data

Xi Yang  1   2 , Xinyu Hu  3 , Wanjie Tang  1 , Bin Li  1 , Yanchun Yang  4 , Qiyong Gong  5 , Xiaoqi Huang  6
Affiliations

Multivariate classification of drug-naive obsessive-compulsive disorder patients and healthy controls by applying an SVM to resting-state functional MRI data

Xi Yang et al. BMC Psychiatry. .

Abstract

Background: Previous resting-state functional magnetic resonance imaging (rs-fMRI) studies have revealed intrinsic regional activity alterations in obsessive-compulsive disorder (OCD), but those results were based on group analyses, which limits their applicability to clinical diagnosis and treatment at the level of the individual.

Methods: We examined fractional amplitude low-frequency fluctuation (fALFF) and applied support vector machine (SVM) to discriminate OCD patients from healthy controls on the basis of rs-fMRI data. Values of fALFF, calculated from 68 drug-naive OCD patients and 68 demographically matched healthy controls, served as input features for the classification procedure.

Results: The classifier achieved 72% accuracy (p ≤ 0.001). This discrimination was based on regions that included the left superior temporal gyrus, the right middle temporal gyrus, the left supramarginal gyrus and the superior parietal lobule.

Conclusions: These results indicate that OCD-related abnormalities in temporal and parietal lobe activation have predictive power for group membership; furthermore, the findings suggest that machine learning techniques can be used to aid in the identification of individuals with OCD in clinical diagnosis.

Keywords: Drug-naive; Fractional amplitude of low-frequency fluctuation; Multivariate classification; Obsessive-compulsive disorder; Resting-state fMRI; Support vector machine.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Classification plot and ROC curve of OCD patients and healthy controls
Fig. 2
Fig. 2
Discrimination map of OCD abnormalities
See this image and copyright information in PMC

References

    1. Association AP . Diagnostic and statistical manual of mental disorders. Fifth 2013.
    1. Grabe HJ, Janowitz D, Freyberger HJ. Epidemiology, familiality and genetic findings in obsessive-compulsive disorder. Zeitschrift Fur Psychiatrie Psychologie Und Psychotherapie. 2007;55(4):219–226. doi: 10.1024/1661-4747.55.4.219. - DOI
    1. Ruscio AM, Stein DJ, Chiu WT, Kessler RC. The epidemiology of obsessive-compulsive disorder in the National Comorbidity Survey Replication. Mol Psychiatry. 2010;15(1):53–63. doi: 10.1038/mp.2008.94. - DOI - PMC - PubMed
    1. Voderholzer U, Schlegl S, Kulz AK. Epidemiology and health care situation of obsessive-compulsive disorders. Nervenarzt. 2011;82(3):273. doi: 10.1007/s00115-010-2960-5. - DOI - PubMed
    1. Murray C, Lopez A. The global burden of disease: a comprehensive assessment of mortality and disability from diseases: Cambrige, Harvard University Press; 1996.

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