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Networks of myelin covariance

Networks of anatomical covariance have been widely used to study connectivity patterns in both normal and pathological brains based on the concurrent changes of morphometric measures (i.e., cortical thickness) between brain structures across subjects (Evans, 2013). However, the existence of networks...

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Autores principales: Melie‐Garcia, Lester, Slater, David, Ruef, Anne, Sanabria‐Diaz, Gretel, Preisig, Martin, Kherif, Ferath, Draganski, Bogdan, Lutti, Antoine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5873432/
https://www.ncbi.nlm.nih.gov/pubmed/29271053
http://dx.doi.org/10.1002/hbm.23929
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author Melie‐Garcia, Lester
Slater, David
Ruef, Anne
Sanabria‐Diaz, Gretel
Preisig, Martin
Kherif, Ferath
Draganski, Bogdan
Lutti, Antoine
author_facet Melie‐Garcia, Lester
Slater, David
Ruef, Anne
Sanabria‐Diaz, Gretel
Preisig, Martin
Kherif, Ferath
Draganski, Bogdan
Lutti, Antoine
author_sort Melie‐Garcia, Lester
collection PubMed
description Networks of anatomical covariance have been widely used to study connectivity patterns in both normal and pathological brains based on the concurrent changes of morphometric measures (i.e., cortical thickness) between brain structures across subjects (Evans, 2013). However, the existence of networks of microstructural changes within brain tissue has been largely unexplored so far. In this article, we studied in vivo the concurrent myelination processes among brain anatomical structures that gathered together emerge to form nonrandom networks. We name these “networks of myelin covariance” (Myelin‐Nets). The Myelin‐Nets were built from quantitative Magnetization Transfer data—an in‐vivo magnetic resonance imaging (MRI) marker of myelin content. The synchronicity of the variations in myelin content between anatomical regions was measured by computing the Pearson's correlation coefficient. We were especially interested in elucidating the effect of age on the topological organization of the Myelin‐Nets. We therefore selected two age groups: Young‐Age (20–31 years old) and Old‐Age (60–71 years old) and a pool of participants from 48 to 87 years old for a Myelin‐Nets aging trajectory study. We found that the topological organization of the Myelin‐Nets is strongly shaped by aging processes. The global myelin correlation strength, between homologous regions and locally in different brain lobes, showed a significant dependence on age. Interestingly, we also showed that the aging process modulates the resilience of the Myelin‐Nets to damage of principal network structures. In summary, this work sheds light on the organizational principles driving myelination and myelin degeneration in brain gray matter and how such patterns are modulated by aging.
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spelling pubmed-58734322018-03-31 Networks of myelin covariance Melie‐Garcia, Lester Slater, David Ruef, Anne Sanabria‐Diaz, Gretel Preisig, Martin Kherif, Ferath Draganski, Bogdan Lutti, Antoine Hum Brain Mapp Research Articles Networks of anatomical covariance have been widely used to study connectivity patterns in both normal and pathological brains based on the concurrent changes of morphometric measures (i.e., cortical thickness) between brain structures across subjects (Evans, 2013). However, the existence of networks of microstructural changes within brain tissue has been largely unexplored so far. In this article, we studied in vivo the concurrent myelination processes among brain anatomical structures that gathered together emerge to form nonrandom networks. We name these “networks of myelin covariance” (Myelin‐Nets). The Myelin‐Nets were built from quantitative Magnetization Transfer data—an in‐vivo magnetic resonance imaging (MRI) marker of myelin content. The synchronicity of the variations in myelin content between anatomical regions was measured by computing the Pearson's correlation coefficient. We were especially interested in elucidating the effect of age on the topological organization of the Myelin‐Nets. We therefore selected two age groups: Young‐Age (20–31 years old) and Old‐Age (60–71 years old) and a pool of participants from 48 to 87 years old for a Myelin‐Nets aging trajectory study. We found that the topological organization of the Myelin‐Nets is strongly shaped by aging processes. The global myelin correlation strength, between homologous regions and locally in different brain lobes, showed a significant dependence on age. Interestingly, we also showed that the aging process modulates the resilience of the Myelin‐Nets to damage of principal network structures. In summary, this work sheds light on the organizational principles driving myelination and myelin degeneration in brain gray matter and how such patterns are modulated by aging. John Wiley and Sons Inc. 2017-12-21 /pmc/articles/PMC5873432/ /pubmed/29271053 http://dx.doi.org/10.1002/hbm.23929 Text en © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Melie‐Garcia, Lester
Slater, David
Ruef, Anne
Sanabria‐Diaz, Gretel
Preisig, Martin
Kherif, Ferath
Draganski, Bogdan
Lutti, Antoine
Networks of myelin covariance
title Networks of myelin covariance
title_full Networks of myelin covariance
title_fullStr Networks of myelin covariance
title_full_unstemmed Networks of myelin covariance
title_short Networks of myelin covariance
title_sort networks of myelin covariance
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5873432/
https://www.ncbi.nlm.nih.gov/pubmed/29271053
http://dx.doi.org/10.1002/hbm.23929
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