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Huntington’s Disease Mouse Models Online: High-Resolution MRI Images with Stereotaxic Templates for Computational Neuroanatomy

Magnetic resonance imaging (MRI) has proved to be an ideal modality for non-destructive and highly detailed assessment of structural morphology in biological tissues. Here we used MRI to make a dataset of ex vivo brains from two different rodent models of Huntington’s disease (HD), the R6/2 line and...

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Autores principales: Sawiak, Stephen J., Wood, Nigel I., Carpenter, T. Adrian, Morton, A. Jennifer
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3534048/
https://www.ncbi.nlm.nih.gov/pubmed/23300918
http://dx.doi.org/10.1371/journal.pone.0053361
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author Sawiak, Stephen J.
Wood, Nigel I.
Carpenter, T. Adrian
Morton, A. Jennifer
author_facet Sawiak, Stephen J.
Wood, Nigel I.
Carpenter, T. Adrian
Morton, A. Jennifer
author_sort Sawiak, Stephen J.
collection PubMed
description Magnetic resonance imaging (MRI) has proved to be an ideal modality for non-destructive and highly detailed assessment of structural morphology in biological tissues. Here we used MRI to make a dataset of ex vivo brains from two different rodent models of Huntington’s disease (HD), the R6/2 line and the YAC 128 mouse. We are making the whole dataset (399 transgenic HD and wildtype (WT) brains, from mice aged 9–80 weeks) publicly available. These data will be useful, not only to investigators interested in the study of HD, but also to researchers of computational neuroanatomy who may not have access to such large datasets from mouse models. Here we demonstrate a number of uses of such data, for example to produce maps of grey and white matter and cortical thickness. As an example of how the library might provide insights in mouse models of HD, we calculated whole brain grey matter volumes across different age groups with different numbers of cytosine-adenine-guanine (CAG) repeats in a fragment of the gene responsible for HD in humans. (The R6/2 dataset was obtained from an allelic series of R6/2 mice carrying a range of CAG repeat lengths between 109 and 464.) This analysis revealed different trajectories for each fragment length. In particular there was a gradient of decreasing pathology with longer CAG repeat lengths, reflecting our previous findings with behavioural and histological studies. There will be no constraints placed on the use of the datasets included here. The original data will be easily and permanently accessible via the University of Cambridge data repository (http://www.dspace.cam.ac.uk/handle/1810/243361).
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spelling pubmed-35340482013-01-08 Huntington’s Disease Mouse Models Online: High-Resolution MRI Images with Stereotaxic Templates for Computational Neuroanatomy Sawiak, Stephen J. Wood, Nigel I. Carpenter, T. Adrian Morton, A. Jennifer PLoS One Research Article Magnetic resonance imaging (MRI) has proved to be an ideal modality for non-destructive and highly detailed assessment of structural morphology in biological tissues. Here we used MRI to make a dataset of ex vivo brains from two different rodent models of Huntington’s disease (HD), the R6/2 line and the YAC 128 mouse. We are making the whole dataset (399 transgenic HD and wildtype (WT) brains, from mice aged 9–80 weeks) publicly available. These data will be useful, not only to investigators interested in the study of HD, but also to researchers of computational neuroanatomy who may not have access to such large datasets from mouse models. Here we demonstrate a number of uses of such data, for example to produce maps of grey and white matter and cortical thickness. As an example of how the library might provide insights in mouse models of HD, we calculated whole brain grey matter volumes across different age groups with different numbers of cytosine-adenine-guanine (CAG) repeats in a fragment of the gene responsible for HD in humans. (The R6/2 dataset was obtained from an allelic series of R6/2 mice carrying a range of CAG repeat lengths between 109 and 464.) This analysis revealed different trajectories for each fragment length. In particular there was a gradient of decreasing pathology with longer CAG repeat lengths, reflecting our previous findings with behavioural and histological studies. There will be no constraints placed on the use of the datasets included here. The original data will be easily and permanently accessible via the University of Cambridge data repository (http://www.dspace.cam.ac.uk/handle/1810/243361). Public Library of Science 2012-12-31 /pmc/articles/PMC3534048/ /pubmed/23300918 http://dx.doi.org/10.1371/journal.pone.0053361 Text en © 2012 Sawiak 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
Sawiak, Stephen J.
Wood, Nigel I.
Carpenter, T. Adrian
Morton, A. Jennifer
Huntington’s Disease Mouse Models Online: High-Resolution MRI Images with Stereotaxic Templates for Computational Neuroanatomy
title Huntington’s Disease Mouse Models Online: High-Resolution MRI Images with Stereotaxic Templates for Computational Neuroanatomy
title_full Huntington’s Disease Mouse Models Online: High-Resolution MRI Images with Stereotaxic Templates for Computational Neuroanatomy
title_fullStr Huntington’s Disease Mouse Models Online: High-Resolution MRI Images with Stereotaxic Templates for Computational Neuroanatomy
title_full_unstemmed Huntington’s Disease Mouse Models Online: High-Resolution MRI Images with Stereotaxic Templates for Computational Neuroanatomy
title_short Huntington’s Disease Mouse Models Online: High-Resolution MRI Images with Stereotaxic Templates for Computational Neuroanatomy
title_sort huntington’s disease mouse models online: high-resolution mri images with stereotaxic templates for computational neuroanatomy
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3534048/
https://www.ncbi.nlm.nih.gov/pubmed/23300918
http://dx.doi.org/10.1371/journal.pone.0053361
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