Histological data of axons, astrocytes, and myelin in deep subcortical white matter populations

This immunohistochemistry dataset contains the main structures in deep subcortical white matter (axons, astrocytes, and myelinated axons) in a representative cohort of an ageing population. A set of samples from 90 subjects of the Cognitive Function and Ageing Study (CFAS) were analysed, stratified...

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Detalles Bibliográficos
Autores principales: Coelho, Santiago, Pozo, Jose M., Costantini, Marina, Highley, J. Robin, Mozumder, Meghdoot, Simpson, Julie E., Ince, Paul G., Frangi, Alejandro F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Neuroscience
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6660516/
https://www.ncbi.nlm.nih.gov/pubmed/31372422
http://dx.doi.org/10.1016/j.dib.2019.103762
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author Coelho, Santiago
Pozo, Jose M.
Costantini, Marina
Highley, J. Robin
Mozumder, Meghdoot
Simpson, Julie E.
Ince, Paul G.
Frangi, Alejandro F.
author_facet Coelho, Santiago
Pozo, Jose M.
Costantini, Marina
Highley, J. Robin
Mozumder, Meghdoot
Simpson, Julie E.
Ince, Paul G.
Frangi, Alejandro F.
author_sort Coelho, Santiago
collection PubMed
description This immunohistochemistry dataset contains the main structures in deep subcortical white matter (axons, astrocytes, and myelinated axons) in a representative cohort of an ageing population. A set of samples from 90 subjects of the Cognitive Function and Ageing Study (CFAS) were analysed, stratified into three groups of 30 subjects each, in relation to the presence of age-associated deep subcortical lesions. High-resolution microscopy data enables the extraction of valuable information, such as volume fractions, for the construction and validation of diffusion MRI (dMRI) models. The dataset provided here was used in Coelho et al. [1].
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spelling pubmed-66605162019-08-01 Histological data of axons, astrocytes, and myelin in deep subcortical white matter populations Coelho, Santiago Pozo, Jose M. Costantini, Marina Highley, J. Robin Mozumder, Meghdoot Simpson, Julie E. Ince, Paul G. Frangi, Alejandro F. Data Brief Neuroscience This immunohistochemistry dataset contains the main structures in deep subcortical white matter (axons, astrocytes, and myelinated axons) in a representative cohort of an ageing population. A set of samples from 90 subjects of the Cognitive Function and Ageing Study (CFAS) were analysed, stratified into three groups of 30 subjects each, in relation to the presence of age-associated deep subcortical lesions. High-resolution microscopy data enables the extraction of valuable information, such as volume fractions, for the construction and validation of diffusion MRI (dMRI) models. The dataset provided here was used in Coelho et al. [1]. Elsevier 2019-03-06 /pmc/articles/PMC6660516/ /pubmed/31372422 http://dx.doi.org/10.1016/j.dib.2019.103762 Text en © 2019 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Neuroscience
Coelho, Santiago
Pozo, Jose M.
Costantini, Marina
Highley, J. Robin
Mozumder, Meghdoot
Simpson, Julie E.
Ince, Paul G.
Frangi, Alejandro F.
Histological data of axons, astrocytes, and myelin in deep subcortical white matter populations
title Histological data of axons, astrocytes, and myelin in deep subcortical white matter populations
title_full Histological data of axons, astrocytes, and myelin in deep subcortical white matter populations
title_fullStr Histological data of axons, astrocytes, and myelin in deep subcortical white matter populations
title_full_unstemmed Histological data of axons, astrocytes, and myelin in deep subcortical white matter populations
title_short Histological data of axons, astrocytes, and myelin in deep subcortical white matter populations
title_sort histological data of axons, astrocytes, and myelin in deep subcortical white matter populations
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6660516/
https://www.ncbi.nlm.nih.gov/pubmed/31372422
http://dx.doi.org/10.1016/j.dib.2019.103762
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