Clinical Research Papers:

Identifying current and remitted major depressive disorder with the Hurst exponent: a comparative study on two automated anatomical labeling atlases

Bin Jing, Zhuqing Long, Han Liu, Huagang Yan, Jianxin Dong, Xiao Mo, Dan Li, Chunhong Liu and Haiyun Li _

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Oncotarget. 2017; 8:90452-90464. https://doi.org/10.18632/oncotarget.19860

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Bin Jing1,*, Zhuqing Long1,*, Han Liu1,*, Huagang Yan1, Jianxin Dong1, Xiao Mo1, Dan Li2, Chunhong Liu3,4 and Haiyun Li1

1School of Biomedical Engineering, Capital Medical University, Beijing, China

2School of Software Engineering, Beijing University of Technology, Beijing, China

3Acupuncture and Moxibustion Department, Beijing Hospital of Traditional Chinese Medicine Affiliated to Capital Medical University, Beijing, China

4Beijing Key Laboratory of Mental Disorders, Department of Radiology, Beijing Anding Hospital, Capital Medical University, Beijing China

*These authors contributed equally to this work

Correspondence to:

Haiyun Li, email: haiyunli@ccmu.edu.cn

Keywords: hurst exponent, automated anatomical labeling (AAL) atlas, resting-state fMRI, support vector machine, major depressive disorder

Received: January 07, 2017     Accepted: July 17, 2017     Published: August 03, 2017


Major depressive disorder (MDD) is a leading world-wide psychiatric disorder with high recurrence rate, therefore, it is desirable to identify current MDD (cMDD) and remitted MDD (rMDD) for their appropriate therapeutic interventions. In the study, 19 cMDD, 19 rMDD and 19 well-matched healthy controls (HC) were enrolled and scanned with the resting-state functional magnetic resonance imaging (rs-fMRI). The Hurst exponent (HE) of rs-fMRI in AAL-90 and AAL-1024 atlases were calculated and compared between groups. Then, a radial basis function (RBF) based support vector machine was proposed to identify every pair of the cMDD, rMDD and HC groups using the abnormal HE features, and a leave-one-out cross-validation was used to evaluate the classification performance. Applying the proposed method with AAL-1024 and AAL-90 atlas respectively, 87% and 84% subjects were correctly identified between cMDD and HC, 84% and 71% between rMDD and HC, and 89% and 74% between cMDD and rMDD. Our results indicated that the HE was an effective feature to distinguish cMDD and rMDD from HC, and the recognition performances with AAL-1024 parcellation were better than that with the conventional AAL-90 parcellation.

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