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BMC Psychiatry. 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.

Author information

1
Mental Health Center Department of Psychiatry, West China Hospital Sichuan University, Chengdu, China.
2
Shenzhen Mental Health Center, Shenzhen, China.
3
Huaxi MR Research Center (HMRRC) Department of Radiology, West China Hospital Sichuan University, Chengdu, 610041, China.
4
Mental Health Center Department of Psychiatry, West China Hospital Sichuan University, Chengdu, China. yanchunyang1958@sina.com.
5
Huaxi MR Research Center (HMRRC) Department of Radiology, West China Hospital Sichuan University, Chengdu, 610041, China. qiyonggong@hmrrc.org.cn.
6
Huaxi MR Research Center (HMRRC) Department of Radiology, West China Hospital Sichuan University, Chengdu, 610041, China. julianahuang@163.com.

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 (pā€‰ā‰¤ā€‰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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