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Diminished neural network dynamics after moderate and severe traumatic brain injury

Over the past decade there has been increasing enthusiasm in the cognitive neurosciences around using network science to understand the system-level changes associated with brain disorders. A growing literature has used whole-brain fMRI analysis to examine changes in the brain’s subnetworks followin...

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Autores principales: Gilbert, Nicholas, Bernier, Rachel A., Calhoun, Vincent D., Brenner, Einat, Grossner, Emily, Rajtmajer, Sarah M., Hillary, Frank G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5993261/
https://www.ncbi.nlm.nih.gov/pubmed/29883447
http://dx.doi.org/10.1371/journal.pone.0197419
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author Gilbert, Nicholas
Bernier, Rachel A.
Calhoun, Vincent D.
Brenner, Einat
Grossner, Emily
Rajtmajer, Sarah M.
Hillary, Frank G.
author_facet Gilbert, Nicholas
Bernier, Rachel A.
Calhoun, Vincent D.
Brenner, Einat
Grossner, Emily
Rajtmajer, Sarah M.
Hillary, Frank G.
author_sort Gilbert, Nicholas
collection PubMed
description Over the past decade there has been increasing enthusiasm in the cognitive neurosciences around using network science to understand the system-level changes associated with brain disorders. A growing literature has used whole-brain fMRI analysis to examine changes in the brain’s subnetworks following traumatic brain injury (TBI). Much of network modeling in this literature has focused on static network mapping, which provides a window into gross inter-nodal relationships, but is insensitive to more subtle fluctuations in network dynamics, which may be an important predictor of neural network plasticity. In this study, we examine the dynamic connectivity with focus on state-level connectivity (state) and evaluate the reliability of dynamic network states over the course of two runs of intermittent task and resting data. The goal was to examine the dynamic properties of neural networks engaged periodically with task stimulation in order to determine: 1) the reliability of inter-nodal and network-level characteristics over time and 2) the transitions between distinct network states after traumatic brain injury. To do so, we enrolled 23 individuals with moderate and severe TBI at least 1-year post injury and 19 age- and education-matched healthy adults using functional MRI methods, dynamic connectivity modeling, and graph theory. The results reveal several distinct network “states” that were reliably evident when comparing runs; the overall frequency of dynamic network states are highly reproducible (r-values>0.8) for both samples. Analysis of movement between states resulted in fewer state transitions in the TBI sample and, in a few cases, brain injury resulted in the appearance of states not exhibited by the healthy control (HC) sample. Overall, the findings presented here demonstrate the reliability of observable dynamic mental states during periods of on-task performance and support emerging evidence that brain injury may result in diminished network dynamics.
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spelling pubmed-59932612018-06-15 Diminished neural network dynamics after moderate and severe traumatic brain injury Gilbert, Nicholas Bernier, Rachel A. Calhoun, Vincent D. Brenner, Einat Grossner, Emily Rajtmajer, Sarah M. Hillary, Frank G. PLoS One Research Article Over the past decade there has been increasing enthusiasm in the cognitive neurosciences around using network science to understand the system-level changes associated with brain disorders. A growing literature has used whole-brain fMRI analysis to examine changes in the brain’s subnetworks following traumatic brain injury (TBI). Much of network modeling in this literature has focused on static network mapping, which provides a window into gross inter-nodal relationships, but is insensitive to more subtle fluctuations in network dynamics, which may be an important predictor of neural network plasticity. In this study, we examine the dynamic connectivity with focus on state-level connectivity (state) and evaluate the reliability of dynamic network states over the course of two runs of intermittent task and resting data. The goal was to examine the dynamic properties of neural networks engaged periodically with task stimulation in order to determine: 1) the reliability of inter-nodal and network-level characteristics over time and 2) the transitions between distinct network states after traumatic brain injury. To do so, we enrolled 23 individuals with moderate and severe TBI at least 1-year post injury and 19 age- and education-matched healthy adults using functional MRI methods, dynamic connectivity modeling, and graph theory. The results reveal several distinct network “states” that were reliably evident when comparing runs; the overall frequency of dynamic network states are highly reproducible (r-values>0.8) for both samples. Analysis of movement between states resulted in fewer state transitions in the TBI sample and, in a few cases, brain injury resulted in the appearance of states not exhibited by the healthy control (HC) sample. Overall, the findings presented here demonstrate the reliability of observable dynamic mental states during periods of on-task performance and support emerging evidence that brain injury may result in diminished network dynamics. Public Library of Science 2018-06-08 /pmc/articles/PMC5993261/ /pubmed/29883447 http://dx.doi.org/10.1371/journal.pone.0197419 Text en © 2018 Gilbert et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Gilbert, Nicholas
Bernier, Rachel A.
Calhoun, Vincent D.
Brenner, Einat
Grossner, Emily
Rajtmajer, Sarah M.
Hillary, Frank G.
Diminished neural network dynamics after moderate and severe traumatic brain injury
title Diminished neural network dynamics after moderate and severe traumatic brain injury
title_full Diminished neural network dynamics after moderate and severe traumatic brain injury
title_fullStr Diminished neural network dynamics after moderate and severe traumatic brain injury
title_full_unstemmed Diminished neural network dynamics after moderate and severe traumatic brain injury
title_short Diminished neural network dynamics after moderate and severe traumatic brain injury
title_sort diminished neural network dynamics after moderate and severe traumatic brain injury
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5993261/
https://www.ncbi.nlm.nih.gov/pubmed/29883447
http://dx.doi.org/10.1371/journal.pone.0197419
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