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

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 contro...

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Autores principales: Jing, Bin, Long, Zhuqing, Liu, Han, Yan, Huagang, Dong, Jianxin, Mo, Xiao, Li, Dan, Liu, Chunhong, Li, Haiyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Impact Journals LLC 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5685765/
https://www.ncbi.nlm.nih.gov/pubmed/29163844
http://dx.doi.org/10.18632/oncotarget.19860
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author Jing, Bin
Long, Zhuqing
Liu, Han
Yan, Huagang
Dong, Jianxin
Mo, Xiao
Li, Dan
Liu, Chunhong
Li, Haiyun
author_facet Jing, Bin
Long, Zhuqing
Liu, Han
Yan, Huagang
Dong, Jianxin
Mo, Xiao
Li, Dan
Liu, Chunhong
Li, Haiyun
author_sort Jing, Bin
collection PubMed
description 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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spelling pubmed-56857652017-11-21 Identifying current and remitted major depressive disorder with the Hurst exponent: a comparative study on two automated anatomical labeling atlases Jing, Bin Long, Zhuqing Liu, Han Yan, Huagang Dong, Jianxin Mo, Xiao Li, Dan Liu, Chunhong Li, Haiyun Oncotarget Clinical Research Paper 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. Impact Journals LLC 2017-08-03 /pmc/articles/PMC5685765/ /pubmed/29163844 http://dx.doi.org/10.18632/oncotarget.19860 Text en Copyright: © 2017 Jing et al. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License 3.0 (http://creativecommons.org/licenses/by/3.0/) (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Clinical Research Paper
Jing, Bin
Long, Zhuqing
Liu, Han
Yan, Huagang
Dong, Jianxin
Mo, Xiao
Li, Dan
Liu, Chunhong
Li, Haiyun
Identifying current and remitted major depressive disorder with the Hurst exponent: a comparative study on two automated anatomical labeling atlases
title Identifying current and remitted major depressive disorder with the Hurst exponent: a comparative study on two automated anatomical labeling atlases
title_full Identifying current and remitted major depressive disorder with the Hurst exponent: a comparative study on two automated anatomical labeling atlases
title_fullStr Identifying current and remitted major depressive disorder with the Hurst exponent: a comparative study on two automated anatomical labeling atlases
title_full_unstemmed Identifying current and remitted major depressive disorder with the Hurst exponent: a comparative study on two automated anatomical labeling atlases
title_short Identifying current and remitted major depressive disorder with the Hurst exponent: a comparative study on two automated anatomical labeling atlases
title_sort identifying current and remitted major depressive disorder with the hurst exponent: a comparative study on two automated anatomical labeling atlases
topic Clinical Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5685765/
https://www.ncbi.nlm.nih.gov/pubmed/29163844
http://dx.doi.org/10.18632/oncotarget.19860
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