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Divergent topological architecture of the default mode network as a pretreatment predictor of early antidepressant response in major depressive disorder

Identifying a robust pretreatment neuroimaging marker would be helpful for the selection of an optimal therapy for major depressive disorder (MDD). We recruited 82 MDD patients [n = 42 treatment-responsive depression (RD) and n = 40 non-responding depression (NRD)] and 50 healthy controls (HC) for t...

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Autores principales: Hou, Zhenghua, Wang, Zan, Jiang, Wenhao, Yin, Yingying, Yue, Yingying, Zhang, Yuqun, Song, Xiaopeng, Yuan, Yonggui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5155246/
https://www.ncbi.nlm.nih.gov/pubmed/27966645
http://dx.doi.org/10.1038/srep39243
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author Hou, Zhenghua
Wang, Zan
Jiang, Wenhao
Yin, Yingying
Yue, Yingying
Zhang, Yuqun
Song, Xiaopeng
Yuan, Yonggui
author_facet Hou, Zhenghua
Wang, Zan
Jiang, Wenhao
Yin, Yingying
Yue, Yingying
Zhang, Yuqun
Song, Xiaopeng
Yuan, Yonggui
author_sort Hou, Zhenghua
collection PubMed
description Identifying a robust pretreatment neuroimaging marker would be helpful for the selection of an optimal therapy for major depressive disorder (MDD). We recruited 82 MDD patients [n = 42 treatment-responsive depression (RD) and n = 40 non-responding depression (NRD)] and 50 healthy controls (HC) for this study. Based on the thresholded partial correlation matrices of 58 specific brain regions, a graph theory approach was applied to analyse the topological properties. When compared to HC, both RD and NRD patients exhibited a lower nodal degree (D(nodal)) in the left anterior cingulate gyrus; as for RD, the D(nodal) of the left superior medial orbitofrontal gyrus was significantly reduced, but the right inferior orbitofrontal gyrus was increased (all P < 0.017, FDR corrected). Moreover, the nodal degree in the right dorsolateral superior frontal cortex (SFGdor) was significantly lower in RD than in NRD. Receiver operating characteristic curve analysis demonstrated that the λ and nodal degree in the right SFGdor exhibited a good ability to distinguish nonresponding patients from responsive patients, which could serve as a specific maker to predict an early response to antidepressants. The disrupted topological configurations in the present study extend the understanding of pretreatment neuroimaging predictors for antidepressant medication.
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spelling pubmed-51552462016-12-28 Divergent topological architecture of the default mode network as a pretreatment predictor of early antidepressant response in major depressive disorder Hou, Zhenghua Wang, Zan Jiang, Wenhao Yin, Yingying Yue, Yingying Zhang, Yuqun Song, Xiaopeng Yuan, Yonggui Sci Rep Article Identifying a robust pretreatment neuroimaging marker would be helpful for the selection of an optimal therapy for major depressive disorder (MDD). We recruited 82 MDD patients [n = 42 treatment-responsive depression (RD) and n = 40 non-responding depression (NRD)] and 50 healthy controls (HC) for this study. Based on the thresholded partial correlation matrices of 58 specific brain regions, a graph theory approach was applied to analyse the topological properties. When compared to HC, both RD and NRD patients exhibited a lower nodal degree (D(nodal)) in the left anterior cingulate gyrus; as for RD, the D(nodal) of the left superior medial orbitofrontal gyrus was significantly reduced, but the right inferior orbitofrontal gyrus was increased (all P < 0.017, FDR corrected). Moreover, the nodal degree in the right dorsolateral superior frontal cortex (SFGdor) was significantly lower in RD than in NRD. Receiver operating characteristic curve analysis demonstrated that the λ and nodal degree in the right SFGdor exhibited a good ability to distinguish nonresponding patients from responsive patients, which could serve as a specific maker to predict an early response to antidepressants. The disrupted topological configurations in the present study extend the understanding of pretreatment neuroimaging predictors for antidepressant medication. Nature Publishing Group 2016-12-14 /pmc/articles/PMC5155246/ /pubmed/27966645 http://dx.doi.org/10.1038/srep39243 Text en Copyright © 2016, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Hou, Zhenghua
Wang, Zan
Jiang, Wenhao
Yin, Yingying
Yue, Yingying
Zhang, Yuqun
Song, Xiaopeng
Yuan, Yonggui
Divergent topological architecture of the default mode network as a pretreatment predictor of early antidepressant response in major depressive disorder
title Divergent topological architecture of the default mode network as a pretreatment predictor of early antidepressant response in major depressive disorder
title_full Divergent topological architecture of the default mode network as a pretreatment predictor of early antidepressant response in major depressive disorder
title_fullStr Divergent topological architecture of the default mode network as a pretreatment predictor of early antidepressant response in major depressive disorder
title_full_unstemmed Divergent topological architecture of the default mode network as a pretreatment predictor of early antidepressant response in major depressive disorder
title_short Divergent topological architecture of the default mode network as a pretreatment predictor of early antidepressant response in major depressive disorder
title_sort divergent topological architecture of the default mode network as a pretreatment predictor of early antidepressant response in major depressive disorder
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5155246/
https://www.ncbi.nlm.nih.gov/pubmed/27966645
http://dx.doi.org/10.1038/srep39243
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