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Multi-contrast anatomical subcortical structures parcellation

The human subcortex is comprised of more than 450 individual nuclei which lie deep in the brain. Due to their small size and close proximity, up until now only 7% have been depicted in standard MRI atlases. Thus, the human subcortex can largely be considered as terra incognita. Here, we present a ne...

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Autores principales: Bazin, Pierre-Louis, Alkemade, Anneke, Mulder, Martijn J, Henry, Amanda G, Forstmann, Birte U
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7771958/
https://www.ncbi.nlm.nih.gov/pubmed/33325368
http://dx.doi.org/10.7554/eLife.59430
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author Bazin, Pierre-Louis
Alkemade, Anneke
Mulder, Martijn J
Henry, Amanda G
Forstmann, Birte U
author_facet Bazin, Pierre-Louis
Alkemade, Anneke
Mulder, Martijn J
Henry, Amanda G
Forstmann, Birte U
author_sort Bazin, Pierre-Louis
collection PubMed
description The human subcortex is comprised of more than 450 individual nuclei which lie deep in the brain. Due to their small size and close proximity, up until now only 7% have been depicted in standard MRI atlases. Thus, the human subcortex can largely be considered as terra incognita. Here, we present a new open-source parcellation algorithm to automatically map the subcortex. The new algorithm has been tested on 17 prominent subcortical structures based on a large quantitative MRI dataset at 7 Tesla. It has been carefully validated against expert human raters and previous methods, and can easily be extended to other subcortical structures and applied to any quantitative MRI dataset. In sum, we hope this novel parcellation algorithm will facilitate functional and structural neuroimaging research into small subcortical nuclei and help to chart terra incognita.
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spelling pubmed-77719582020-12-30 Multi-contrast anatomical subcortical structures parcellation Bazin, Pierre-Louis Alkemade, Anneke Mulder, Martijn J Henry, Amanda G Forstmann, Birte U eLife Neuroscience The human subcortex is comprised of more than 450 individual nuclei which lie deep in the brain. Due to their small size and close proximity, up until now only 7% have been depicted in standard MRI atlases. Thus, the human subcortex can largely be considered as terra incognita. Here, we present a new open-source parcellation algorithm to automatically map the subcortex. The new algorithm has been tested on 17 prominent subcortical structures based on a large quantitative MRI dataset at 7 Tesla. It has been carefully validated against expert human raters and previous methods, and can easily be extended to other subcortical structures and applied to any quantitative MRI dataset. In sum, we hope this novel parcellation algorithm will facilitate functional and structural neuroimaging research into small subcortical nuclei and help to chart terra incognita. eLife Sciences Publications, Ltd 2020-12-16 /pmc/articles/PMC7771958/ /pubmed/33325368 http://dx.doi.org/10.7554/eLife.59430 Text en © 2020, Bazin et al http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Neuroscience
Bazin, Pierre-Louis
Alkemade, Anneke
Mulder, Martijn J
Henry, Amanda G
Forstmann, Birte U
Multi-contrast anatomical subcortical structures parcellation
title Multi-contrast anatomical subcortical structures parcellation
title_full Multi-contrast anatomical subcortical structures parcellation
title_fullStr Multi-contrast anatomical subcortical structures parcellation
title_full_unstemmed Multi-contrast anatomical subcortical structures parcellation
title_short Multi-contrast anatomical subcortical structures parcellation
title_sort multi-contrast anatomical subcortical structures parcellation
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7771958/
https://www.ncbi.nlm.nih.gov/pubmed/33325368
http://dx.doi.org/10.7554/eLife.59430
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