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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2017
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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. |
format | Online Article Text |
id | pubmed-5873432 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
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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