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Characterizing Brain Structures and Remodeling after TBI Based on Information Content, Diffusion Entropy
BACKGROUND: To overcome the limitations of conventional diffusion tensor magnetic resonance imaging resulting from the assumption of a Gaussian diffusion model for characterizing voxels containing multiple axonal orientations, Shannon's entropy was employed to evaluate white matter structure in...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3797055/ https://www.ncbi.nlm.nih.gov/pubmed/24143186 http://dx.doi.org/10.1371/journal.pone.0076343 |
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author | Fozouni, Niloufar Chopp, Michael Nejad-Davarani, Siamak P. Zhang, Zheng Gang Lehman, Norman L. Gu, Steven Ueno, Yuji Lu, Mei Ding, Guangliang Li, Lian Hu, Jiani Bagher-Ebadian, Hassan Hearshen, David Jiang, Quan |
author_facet | Fozouni, Niloufar Chopp, Michael Nejad-Davarani, Siamak P. Zhang, Zheng Gang Lehman, Norman L. Gu, Steven Ueno, Yuji Lu, Mei Ding, Guangliang Li, Lian Hu, Jiani Bagher-Ebadian, Hassan Hearshen, David Jiang, Quan |
author_sort | Fozouni, Niloufar |
collection | PubMed |
description | BACKGROUND: To overcome the limitations of conventional diffusion tensor magnetic resonance imaging resulting from the assumption of a Gaussian diffusion model for characterizing voxels containing multiple axonal orientations, Shannon's entropy was employed to evaluate white matter structure in human brain and in brain remodeling after traumatic brain injury (TBI) in a rat. METHODS: Thirteen healthy subjects were investigated using a Q-ball based DTI data sampling scheme. FA and entropy values were measured in white matter bundles, white matter fiber crossing areas, different gray matter (GM) regions and cerebrospinal fluid (CSF). Axonal densities' from the same regions of interest (ROIs) were evaluated in Bielschowsky and Luxol fast blue stained autopsy (n = 30) brain sections by light microscopy. As a case demonstration, a Wistar rat subjected to TBI and treated with bone marrow stromal cells (MSC) 1 week after TBI was employed to illustrate the superior ability of entropy over FA in detecting reorganized crossing axonal bundles as confirmed by histological analysis with Bielschowsky and Luxol fast blue staining. RESULTS: Unlike FA, entropy was less affected by axonal orientation and more affected by axonal density. A significant agreement (r = 0.91) was detected between entropy values from in vivo human brain and histologically measured axonal density from post mortum from the same brain structures. The MSC treated TBI rat demonstrated that the entropy approach is superior to FA in detecting axonal remodeling after injury. Compared with FA, entropy detected new axonal remodeling regions with crossing axons, confirmed with immunohistological staining. CONCLUSIONS: Entropy measurement is more effective in distinguishing axonal remodeling after injury, when compared with FA. Entropy is also more sensitive to axonal density than axonal orientation, and thus may provide a more accurate reflection of axonal changes that occur in neurological injury and disease. |
format | Online Article Text |
id | pubmed-3797055 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-37970552013-10-18 Characterizing Brain Structures and Remodeling after TBI Based on Information Content, Diffusion Entropy Fozouni, Niloufar Chopp, Michael Nejad-Davarani, Siamak P. Zhang, Zheng Gang Lehman, Norman L. Gu, Steven Ueno, Yuji Lu, Mei Ding, Guangliang Li, Lian Hu, Jiani Bagher-Ebadian, Hassan Hearshen, David Jiang, Quan PLoS One Research Article BACKGROUND: To overcome the limitations of conventional diffusion tensor magnetic resonance imaging resulting from the assumption of a Gaussian diffusion model for characterizing voxels containing multiple axonal orientations, Shannon's entropy was employed to evaluate white matter structure in human brain and in brain remodeling after traumatic brain injury (TBI) in a rat. METHODS: Thirteen healthy subjects were investigated using a Q-ball based DTI data sampling scheme. FA and entropy values were measured in white matter bundles, white matter fiber crossing areas, different gray matter (GM) regions and cerebrospinal fluid (CSF). Axonal densities' from the same regions of interest (ROIs) were evaluated in Bielschowsky and Luxol fast blue stained autopsy (n = 30) brain sections by light microscopy. As a case demonstration, a Wistar rat subjected to TBI and treated with bone marrow stromal cells (MSC) 1 week after TBI was employed to illustrate the superior ability of entropy over FA in detecting reorganized crossing axonal bundles as confirmed by histological analysis with Bielschowsky and Luxol fast blue staining. RESULTS: Unlike FA, entropy was less affected by axonal orientation and more affected by axonal density. A significant agreement (r = 0.91) was detected between entropy values from in vivo human brain and histologically measured axonal density from post mortum from the same brain structures. The MSC treated TBI rat demonstrated that the entropy approach is superior to FA in detecting axonal remodeling after injury. Compared with FA, entropy detected new axonal remodeling regions with crossing axons, confirmed with immunohistological staining. CONCLUSIONS: Entropy measurement is more effective in distinguishing axonal remodeling after injury, when compared with FA. Entropy is also more sensitive to axonal density than axonal orientation, and thus may provide a more accurate reflection of axonal changes that occur in neurological injury and disease. Public Library of Science 2013-10-15 /pmc/articles/PMC3797055/ /pubmed/24143186 http://dx.doi.org/10.1371/journal.pone.0076343 Text en © 2013 Fozouni 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, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Fozouni, Niloufar Chopp, Michael Nejad-Davarani, Siamak P. Zhang, Zheng Gang Lehman, Norman L. Gu, Steven Ueno, Yuji Lu, Mei Ding, Guangliang Li, Lian Hu, Jiani Bagher-Ebadian, Hassan Hearshen, David Jiang, Quan Characterizing Brain Structures and Remodeling after TBI Based on Information Content, Diffusion Entropy |
title | Characterizing Brain Structures and Remodeling after TBI Based on Information Content, Diffusion Entropy |
title_full | Characterizing Brain Structures and Remodeling after TBI Based on Information Content, Diffusion Entropy |
title_fullStr | Characterizing Brain Structures and Remodeling after TBI Based on Information Content, Diffusion Entropy |
title_full_unstemmed | Characterizing Brain Structures and Remodeling after TBI Based on Information Content, Diffusion Entropy |
title_short | Characterizing Brain Structures and Remodeling after TBI Based on Information Content, Diffusion Entropy |
title_sort | characterizing brain structures and remodeling after tbi based on information content, diffusion entropy |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3797055/ https://www.ncbi.nlm.nih.gov/pubmed/24143186 http://dx.doi.org/10.1371/journal.pone.0076343 |
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