Cargando…
Magnetic resonance imaging-based cerebral tissue classification reveals distinct spatiotemporal patterns of changes after stroke in non-human primates
BACKGROUND: Spatial and temporal changes in brain tissue after acute ischemic stroke are still poorly understood. Aims of this study were three-fold: (1) to determine unique temporal magnetic resonance imaging (MRI) patterns at the acute, subacute and chronic stages after stroke in macaques by combi...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4678699/ https://www.ncbi.nlm.nih.gov/pubmed/26666889 http://dx.doi.org/10.1186/s12868-015-0226-7 |
_version_ | 1782405491353190400 |
---|---|
author | Bouts, Mark. J. R. J. Westmoreland, Susan. V. de Crespigny, Alex J. Liu, Yutong Vangel, Mark Dijkhuizen, Rick M. Wu, Ona D’Arceuil, Helen E. |
author_facet | Bouts, Mark. J. R. J. Westmoreland, Susan. V. de Crespigny, Alex J. Liu, Yutong Vangel, Mark Dijkhuizen, Rick M. Wu, Ona D’Arceuil, Helen E. |
author_sort | Bouts, Mark. J. R. J. |
collection | PubMed |
description | BACKGROUND: Spatial and temporal changes in brain tissue after acute ischemic stroke are still poorly understood. Aims of this study were three-fold: (1) to determine unique temporal magnetic resonance imaging (MRI) patterns at the acute, subacute and chronic stages after stroke in macaques by combining quantitative T(2) and diffusion MRI indices into MRI ‘tissue signatures’, (2) to evaluate temporal differences in these signatures between transient (n = 2) and permanent (n = 2) middle cerebral artery occlusion, and (3) to correlate histopathology findings in the chronic stroke period to the acute and subacute MRI derived tissue signatures. RESULTS: An improved iterative self-organizing data analysis algorithm was used to combine T(2), apparent diffusion coefficient (ADC), and fractional anisotropy (FA) maps across seven successive timepoints (1, 2, 3, 24, 72, 144, 240 h) which revealed five temporal MRI signatures, that were different from the normal tissue pattern (P < 0.001). The distribution of signatures between brains with permanent and transient occlusions varied significantly between groups (P < 0.001). Qualitative comparisons with histopathology revealed that these signatures represented regions with different histopathology. Two signatures identified areas of progressive injury marked by severe necrosis and the presence of gitter cells. Another signature identified less severe but pronounced neuronal and axonal degeneration, while the other signatures depicted tissue remodeling with vascular proliferation and astrogliosis. CONCLUSION: These exploratory results demonstrate the potential of temporally and spatially combined voxel-based methods to generate tissue signatures that may correlate with distinct histopathological features. The identification of distinct ischemic MRI signatures associated with specific tissue fates may further aid in assessing and monitoring the efficacy of novel pharmaceutical treatments for stroke in a pre-clinical and clinical setting. |
format | Online Article Text |
id | pubmed-4678699 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-46786992015-12-16 Magnetic resonance imaging-based cerebral tissue classification reveals distinct spatiotemporal patterns of changes after stroke in non-human primates Bouts, Mark. J. R. J. Westmoreland, Susan. V. de Crespigny, Alex J. Liu, Yutong Vangel, Mark Dijkhuizen, Rick M. Wu, Ona D’Arceuil, Helen E. BMC Neurosci Research Article BACKGROUND: Spatial and temporal changes in brain tissue after acute ischemic stroke are still poorly understood. Aims of this study were three-fold: (1) to determine unique temporal magnetic resonance imaging (MRI) patterns at the acute, subacute and chronic stages after stroke in macaques by combining quantitative T(2) and diffusion MRI indices into MRI ‘tissue signatures’, (2) to evaluate temporal differences in these signatures between transient (n = 2) and permanent (n = 2) middle cerebral artery occlusion, and (3) to correlate histopathology findings in the chronic stroke period to the acute and subacute MRI derived tissue signatures. RESULTS: An improved iterative self-organizing data analysis algorithm was used to combine T(2), apparent diffusion coefficient (ADC), and fractional anisotropy (FA) maps across seven successive timepoints (1, 2, 3, 24, 72, 144, 240 h) which revealed five temporal MRI signatures, that were different from the normal tissue pattern (P < 0.001). The distribution of signatures between brains with permanent and transient occlusions varied significantly between groups (P < 0.001). Qualitative comparisons with histopathology revealed that these signatures represented regions with different histopathology. Two signatures identified areas of progressive injury marked by severe necrosis and the presence of gitter cells. Another signature identified less severe but pronounced neuronal and axonal degeneration, while the other signatures depicted tissue remodeling with vascular proliferation and astrogliosis. CONCLUSION: These exploratory results demonstrate the potential of temporally and spatially combined voxel-based methods to generate tissue signatures that may correlate with distinct histopathological features. The identification of distinct ischemic MRI signatures associated with specific tissue fates may further aid in assessing and monitoring the efficacy of novel pharmaceutical treatments for stroke in a pre-clinical and clinical setting. BioMed Central 2015-12-15 /pmc/articles/PMC4678699/ /pubmed/26666889 http://dx.doi.org/10.1186/s12868-015-0226-7 Text en © Bouts et al. 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Bouts, Mark. J. R. J. Westmoreland, Susan. V. de Crespigny, Alex J. Liu, Yutong Vangel, Mark Dijkhuizen, Rick M. Wu, Ona D’Arceuil, Helen E. Magnetic resonance imaging-based cerebral tissue classification reveals distinct spatiotemporal patterns of changes after stroke in non-human primates |
title | Magnetic resonance imaging-based cerebral tissue classification reveals distinct spatiotemporal patterns of changes after stroke in non-human primates |
title_full | Magnetic resonance imaging-based cerebral tissue classification reveals distinct spatiotemporal patterns of changes after stroke in non-human primates |
title_fullStr | Magnetic resonance imaging-based cerebral tissue classification reveals distinct spatiotemporal patterns of changes after stroke in non-human primates |
title_full_unstemmed | Magnetic resonance imaging-based cerebral tissue classification reveals distinct spatiotemporal patterns of changes after stroke in non-human primates |
title_short | Magnetic resonance imaging-based cerebral tissue classification reveals distinct spatiotemporal patterns of changes after stroke in non-human primates |
title_sort | magnetic resonance imaging-based cerebral tissue classification reveals distinct spatiotemporal patterns of changes after stroke in non-human primates |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4678699/ https://www.ncbi.nlm.nih.gov/pubmed/26666889 http://dx.doi.org/10.1186/s12868-015-0226-7 |
work_keys_str_mv | AT boutsmarkjrj magneticresonanceimagingbasedcerebraltissueclassificationrevealsdistinctspatiotemporalpatternsofchangesafterstrokeinnonhumanprimates AT westmorelandsusanv magneticresonanceimagingbasedcerebraltissueclassificationrevealsdistinctspatiotemporalpatternsofchangesafterstrokeinnonhumanprimates AT decrespignyalexj magneticresonanceimagingbasedcerebraltissueclassificationrevealsdistinctspatiotemporalpatternsofchangesafterstrokeinnonhumanprimates AT liuyutong magneticresonanceimagingbasedcerebraltissueclassificationrevealsdistinctspatiotemporalpatternsofchangesafterstrokeinnonhumanprimates AT vangelmark magneticresonanceimagingbasedcerebraltissueclassificationrevealsdistinctspatiotemporalpatternsofchangesafterstrokeinnonhumanprimates AT dijkhuizenrickm magneticresonanceimagingbasedcerebraltissueclassificationrevealsdistinctspatiotemporalpatternsofchangesafterstrokeinnonhumanprimates AT wuona magneticresonanceimagingbasedcerebraltissueclassificationrevealsdistinctspatiotemporalpatternsofchangesafterstrokeinnonhumanprimates AT darceuilhelene magneticresonanceimagingbasedcerebraltissueclassificationrevealsdistinctspatiotemporalpatternsofchangesafterstrokeinnonhumanprimates |