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Multiparametric brainstem segmentation using a modified multivariate mixture of Gaussians()
The human brainstem is a densely packed, complex but highly organised structure. It not only serves as a conduit for long projecting axons conveying motor and sensory information, but also is the location of multiple primary nuclei that control or modulate a vast array of functions, including homeos...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3777756/ https://www.ncbi.nlm.nih.gov/pubmed/24179820 http://dx.doi.org/10.1016/j.nicl.2013.04.017 |
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author | Lambert, Christian Lutti, Antoine Helms, Gunther Frackowiak, Richard Ashburner, John |
author_facet | Lambert, Christian Lutti, Antoine Helms, Gunther Frackowiak, Richard Ashburner, John |
author_sort | Lambert, Christian |
collection | PubMed |
description | The human brainstem is a densely packed, complex but highly organised structure. It not only serves as a conduit for long projecting axons conveying motor and sensory information, but also is the location of multiple primary nuclei that control or modulate a vast array of functions, including homeostasis, consciousness, locomotion, and reflexive and emotive behaviours. Despite its importance, both in understanding normal brain function as well as neurodegenerative processes, it remains a sparsely studied structure in the neuroimaging literature. In part, this is due to the difficulties in imaging the internal architecture of the brainstem in vivo in a reliable and repeatable fashion. A modified multivariate mixture of Gaussians (mmMoG) was applied to the problem of multichannel tissue segmentation. By using quantitative magnetisation transfer and proton density maps acquired at 3 T with 0.8 mm isotropic resolution, tissue probability maps for four distinct tissue classes within the human brainstem were created. These were compared against an ex vivo fixated human brain, imaged at 0.5 mm, with excellent anatomical correspondence. These probability maps were used within SPM8 to create accurate individual subject segmentations, which were then used for further quantitative analysis. As an example, brainstem asymmetries were assessed across 34 right-handed individuals using voxel based morphometry (VBM) and tensor based morphometry (TBM), demonstrating highly significant differences within localised regions that corresponded to motor and vocalisation networks. This method may have important implications for future research into MRI biomarkers of pre-clinical neurodegenerative diseases such as Parkinson's disease. |
format | Online Article Text |
id | pubmed-3777756 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-37777562013-10-31 Multiparametric brainstem segmentation using a modified multivariate mixture of Gaussians() Lambert, Christian Lutti, Antoine Helms, Gunther Frackowiak, Richard Ashburner, John Neuroimage Clin Article The human brainstem is a densely packed, complex but highly organised structure. It not only serves as a conduit for long projecting axons conveying motor and sensory information, but also is the location of multiple primary nuclei that control or modulate a vast array of functions, including homeostasis, consciousness, locomotion, and reflexive and emotive behaviours. Despite its importance, both in understanding normal brain function as well as neurodegenerative processes, it remains a sparsely studied structure in the neuroimaging literature. In part, this is due to the difficulties in imaging the internal architecture of the brainstem in vivo in a reliable and repeatable fashion. A modified multivariate mixture of Gaussians (mmMoG) was applied to the problem of multichannel tissue segmentation. By using quantitative magnetisation transfer and proton density maps acquired at 3 T with 0.8 mm isotropic resolution, tissue probability maps for four distinct tissue classes within the human brainstem were created. These were compared against an ex vivo fixated human brain, imaged at 0.5 mm, with excellent anatomical correspondence. These probability maps were used within SPM8 to create accurate individual subject segmentations, which were then used for further quantitative analysis. As an example, brainstem asymmetries were assessed across 34 right-handed individuals using voxel based morphometry (VBM) and tensor based morphometry (TBM), demonstrating highly significant differences within localised regions that corresponded to motor and vocalisation networks. This method may have important implications for future research into MRI biomarkers of pre-clinical neurodegenerative diseases such as Parkinson's disease. Elsevier 2013-05-16 /pmc/articles/PMC3777756/ /pubmed/24179820 http://dx.doi.org/10.1016/j.nicl.2013.04.017 Text en © 2013 The Authors http://creativecommons.org/licenses/by-nc-nd/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-No Derivative Works License, which permits non-commercial use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Article Lambert, Christian Lutti, Antoine Helms, Gunther Frackowiak, Richard Ashburner, John Multiparametric brainstem segmentation using a modified multivariate mixture of Gaussians() |
title | Multiparametric brainstem segmentation using a modified multivariate mixture of Gaussians() |
title_full | Multiparametric brainstem segmentation using a modified multivariate mixture of Gaussians() |
title_fullStr | Multiparametric brainstem segmentation using a modified multivariate mixture of Gaussians() |
title_full_unstemmed | Multiparametric brainstem segmentation using a modified multivariate mixture of Gaussians() |
title_short | Multiparametric brainstem segmentation using a modified multivariate mixture of Gaussians() |
title_sort | multiparametric brainstem segmentation using a modified multivariate mixture of gaussians() |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3777756/ https://www.ncbi.nlm.nih.gov/pubmed/24179820 http://dx.doi.org/10.1016/j.nicl.2013.04.017 |
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