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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Impact Journals LLC
2017
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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. |
format | Online Article Text |
id | pubmed-5685765 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Impact Journals LLC |
record_format | MEDLINE/PubMed |
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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