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Cognitive Vulnerability to Major Depression: View from the Intrinsic Network and Cross-network Interactions

Although it is generally accepted that cognitive factors contribute to the pathogenesis of major depressive disorder (MDD), there are missing links between behavioral and biological models of depression. Nevertheless, research employing neuroimaging technologies has elucidated some of the neurobiolo...

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Autores principales: Wang, Xiang, Öngür, Dost, Auerbach, Randy P., Yao, Shuqiao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4859203/
https://www.ncbi.nlm.nih.gov/pubmed/27148911
http://dx.doi.org/10.1097/HRP.0000000000000081
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author Wang, Xiang
Öngür, Dost
Auerbach, Randy P.
Yao, Shuqiao
author_facet Wang, Xiang
Öngür, Dost
Auerbach, Randy P.
Yao, Shuqiao
author_sort Wang, Xiang
collection PubMed
description Although it is generally accepted that cognitive factors contribute to the pathogenesis of major depressive disorder (MDD), there are missing links between behavioral and biological models of depression. Nevertheless, research employing neuroimaging technologies has elucidated some of the neurobiological mechanisms related to cognitive-vulnerability factors, especially from a whole-brain, dynamic perspective. In this review, we integrate well-established cognitive-vulnerability factors for MDD and corresponding neural mechanisms in intrinsic networks using a dual-process framework. We propose that the dynamic alteration and imbalance among the intrinsic networks, both in the resting-state and the rest-task transition stages, contribute to the development of cognitive vulnerability and MDD. Specifically, we propose that abnormally increased resting-state default mode network (DMN) activity and connectivity (mainly in anterior DMN regions) contribute to the development of cognitive vulnerability. Furthermore, when subjects confront negative stimuli in the period of rest-to-task transition, the following three kinds of aberrant network interactions have been identified as facilitators of vulnerability and dysphoric mood, each through a different cognitive mechanism: DMN dominance over the central executive network (CEN), an impaired salience network–mediated switching between the DMN and CEN, and ineffective CEN modulation of the DMN. This focus on interrelated networks and brain-activity changes between rest and task states provides a neural-system perspective for future research on cognitive vulnerability and resilience, and may potentially guide the development of new intervention strategies for MDD.
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spelling pubmed-48592032016-05-23 Cognitive Vulnerability to Major Depression: View from the Intrinsic Network and Cross-network Interactions Wang, Xiang Öngür, Dost Auerbach, Randy P. Yao, Shuqiao Harv Rev Psychiatry Reviews Although it is generally accepted that cognitive factors contribute to the pathogenesis of major depressive disorder (MDD), there are missing links between behavioral and biological models of depression. Nevertheless, research employing neuroimaging technologies has elucidated some of the neurobiological mechanisms related to cognitive-vulnerability factors, especially from a whole-brain, dynamic perspective. In this review, we integrate well-established cognitive-vulnerability factors for MDD and corresponding neural mechanisms in intrinsic networks using a dual-process framework. We propose that the dynamic alteration and imbalance among the intrinsic networks, both in the resting-state and the rest-task transition stages, contribute to the development of cognitive vulnerability and MDD. Specifically, we propose that abnormally increased resting-state default mode network (DMN) activity and connectivity (mainly in anterior DMN regions) contribute to the development of cognitive vulnerability. Furthermore, when subjects confront negative stimuli in the period of rest-to-task transition, the following three kinds of aberrant network interactions have been identified as facilitators of vulnerability and dysphoric mood, each through a different cognitive mechanism: DMN dominance over the central executive network (CEN), an impaired salience network–mediated switching between the DMN and CEN, and ineffective CEN modulation of the DMN. This focus on interrelated networks and brain-activity changes between rest and task states provides a neural-system perspective for future research on cognitive vulnerability and resilience, and may potentially guide the development of new intervention strategies for MDD. Lippincott Williams & Wilkins 2016-05 2016-05-09 /pmc/articles/PMC4859203/ /pubmed/27148911 http://dx.doi.org/10.1097/HRP.0000000000000081 Text en © 2016 President and Fellows of Harvard College This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND) (http://creativecommons.org/licenses/by-nc-nd/4.0/) , where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially.
spellingShingle Reviews
Wang, Xiang
Öngür, Dost
Auerbach, Randy P.
Yao, Shuqiao
Cognitive Vulnerability to Major Depression: View from the Intrinsic Network and Cross-network Interactions
title Cognitive Vulnerability to Major Depression: View from the Intrinsic Network and Cross-network Interactions
title_full Cognitive Vulnerability to Major Depression: View from the Intrinsic Network and Cross-network Interactions
title_fullStr Cognitive Vulnerability to Major Depression: View from the Intrinsic Network and Cross-network Interactions
title_full_unstemmed Cognitive Vulnerability to Major Depression: View from the Intrinsic Network and Cross-network Interactions
title_short Cognitive Vulnerability to Major Depression: View from the Intrinsic Network and Cross-network Interactions
title_sort cognitive vulnerability to major depression: view from the intrinsic network and cross-network interactions
topic Reviews
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4859203/
https://www.ncbi.nlm.nih.gov/pubmed/27148911
http://dx.doi.org/10.1097/HRP.0000000000000081
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