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A Computational Network Control Theory Analysis of Depression Symptoms

Rumination and impaired inhibition are considered core characteristics of depression. However, the neurocognitive mechanisms that contribute to these atypical cognitive processes remain unclear. To address this question, we apply a computational network control theory approach to structural brain im...

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Autores principales: Kenett, Yoed N., Beaty, Roger E., Medaglia, John D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cambridge University Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6349380/
https://www.ncbi.nlm.nih.gov/pubmed/30706049
http://dx.doi.org/10.1017/pen.2018.15
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author Kenett, Yoed N.
Beaty, Roger E.
Medaglia, John D.
author_facet Kenett, Yoed N.
Beaty, Roger E.
Medaglia, John D.
author_sort Kenett, Yoed N.
collection PubMed
description Rumination and impaired inhibition are considered core characteristics of depression. However, the neurocognitive mechanisms that contribute to these atypical cognitive processes remain unclear. To address this question, we apply a computational network control theory approach to structural brain imaging data acquired via diffusion tensor imaging in a large sample of participants, to examine how network control theory relates to individual differences in subclinical depression. Recent application of this theory at the neural level is built on a model of brain dynamics, which mathematically models patterns of inter-region activity propagated along the structure of an underlying network. The strength of this approach is its ability to characterize the potential role of each brain region in regulating whole-brain network function based on its anatomical fingerprint and a simplified model of node dynamics. We find that subclinical depression is negatively related to higher integration abilities in the right anterior insula, replicating and extending previous studies implicating atypical switching between the default mode and Executive Control Networks in depression. We also find that subclinical depression is related to the ability to “drive” the brain system into easy to reach neural states in several brain regions, including the bilateral lingual gyrus and lateral occipital gyrus. These findings highlight brain regions less known in their role in depression, and clarify their roles in driving the brain into different neural states related to depression symptoms.
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spelling pubmed-63493802019-02-10 A Computational Network Control Theory Analysis of Depression Symptoms Kenett, Yoed N. Beaty, Roger E. Medaglia, John D. Personal Neurosci Empirical Paper Rumination and impaired inhibition are considered core characteristics of depression. However, the neurocognitive mechanisms that contribute to these atypical cognitive processes remain unclear. To address this question, we apply a computational network control theory approach to structural brain imaging data acquired via diffusion tensor imaging in a large sample of participants, to examine how network control theory relates to individual differences in subclinical depression. Recent application of this theory at the neural level is built on a model of brain dynamics, which mathematically models patterns of inter-region activity propagated along the structure of an underlying network. The strength of this approach is its ability to characterize the potential role of each brain region in regulating whole-brain network function based on its anatomical fingerprint and a simplified model of node dynamics. We find that subclinical depression is negatively related to higher integration abilities in the right anterior insula, replicating and extending previous studies implicating atypical switching between the default mode and Executive Control Networks in depression. We also find that subclinical depression is related to the ability to “drive” the brain system into easy to reach neural states in several brain regions, including the bilateral lingual gyrus and lateral occipital gyrus. These findings highlight brain regions less known in their role in depression, and clarify their roles in driving the brain into different neural states related to depression symptoms. Cambridge University Press 2018-10-15 /pmc/articles/PMC6349380/ /pubmed/30706049 http://dx.doi.org/10.1017/pen.2018.15 Text en © The Author(s) 2018 http://creativecommons.org/licenses/by-ncnd/4.0/ This is an Open Access article, distributed under the terms of the Creative Commons Attribution- NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-ncnd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited.
spellingShingle Empirical Paper
Kenett, Yoed N.
Beaty, Roger E.
Medaglia, John D.
A Computational Network Control Theory Analysis of Depression Symptoms
title A Computational Network Control Theory Analysis of Depression Symptoms
title_full A Computational Network Control Theory Analysis of Depression Symptoms
title_fullStr A Computational Network Control Theory Analysis of Depression Symptoms
title_full_unstemmed A Computational Network Control Theory Analysis of Depression Symptoms
title_short A Computational Network Control Theory Analysis of Depression Symptoms
title_sort computational network control theory analysis of depression symptoms
topic Empirical Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6349380/
https://www.ncbi.nlm.nih.gov/pubmed/30706049
http://dx.doi.org/10.1017/pen.2018.15
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