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Automatic morphometry in Alzheimer's disease and mild cognitive impairment()()

This paper presents a novel, publicly available repository of anatomically segmented brain images of healthy subjects as well as patients with mild cognitive impairment and Alzheimer's disease. The underlying magnetic resonance images have been obtained from the Alzheimer's Disease Neuroim...

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Autores principales: Heckemann, Rolf A., Keihaninejad, Shiva, Aljabar, Paul, Gray, Katherine R., Nielsen, Casper, Rueckert, Daniel, Hajnal, Joseph V., Hammers, Alexander
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Academic Press 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3153069/
https://www.ncbi.nlm.nih.gov/pubmed/21397703
http://dx.doi.org/10.1016/j.neuroimage.2011.03.014
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author Heckemann, Rolf A.
Keihaninejad, Shiva
Aljabar, Paul
Gray, Katherine R.
Nielsen, Casper
Rueckert, Daniel
Hajnal, Joseph V.
Hammers, Alexander
author_facet Heckemann, Rolf A.
Keihaninejad, Shiva
Aljabar, Paul
Gray, Katherine R.
Nielsen, Casper
Rueckert, Daniel
Hajnal, Joseph V.
Hammers, Alexander
author_sort Heckemann, Rolf A.
collection PubMed
description This paper presents a novel, publicly available repository of anatomically segmented brain images of healthy subjects as well as patients with mild cognitive impairment and Alzheimer's disease. The underlying magnetic resonance images have been obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. T1-weighted screening and baseline images (1.5 T and 3 T) have been processed with the multi-atlas based MAPER procedure, resulting in labels for 83 regions covering the whole brain in 816 subjects. Selected segmentations were subjected to visual assessment. The segmentations are self-consistent, as evidenced by strong agreement between segmentations of paired images acquired at different field strengths (Jaccard coefficient: 0.802 ± 0.0146). Morphometric comparisons between diagnostic groups (normal; stable mild cognitive impairment; mild cognitive impairment with progression to Alzheimer's disease; Alzheimer's disease) showed highly significant group differences for individual regions, the majority of which were located in the temporal lobe. Additionally, significant effects were seen in the parietal lobe. Increased left/right asymmetry was found in posterior cortical regions. An automatically derived white-matter hypointensities index was found to be a suitable means of quantifying white-matter disease. This repository of segmentations is a potentially valuable resource to researchers working with ADNI data.
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spelling pubmed-31530692011-09-28 Automatic morphometry in Alzheimer's disease and mild cognitive impairment()() Heckemann, Rolf A. Keihaninejad, Shiva Aljabar, Paul Gray, Katherine R. Nielsen, Casper Rueckert, Daniel Hajnal, Joseph V. Hammers, Alexander Neuroimage Article This paper presents a novel, publicly available repository of anatomically segmented brain images of healthy subjects as well as patients with mild cognitive impairment and Alzheimer's disease. The underlying magnetic resonance images have been obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. T1-weighted screening and baseline images (1.5 T and 3 T) have been processed with the multi-atlas based MAPER procedure, resulting in labels for 83 regions covering the whole brain in 816 subjects. Selected segmentations were subjected to visual assessment. The segmentations are self-consistent, as evidenced by strong agreement between segmentations of paired images acquired at different field strengths (Jaccard coefficient: 0.802 ± 0.0146). Morphometric comparisons between diagnostic groups (normal; stable mild cognitive impairment; mild cognitive impairment with progression to Alzheimer's disease; Alzheimer's disease) showed highly significant group differences for individual regions, the majority of which were located in the temporal lobe. Additionally, significant effects were seen in the parietal lobe. Increased left/right asymmetry was found in posterior cortical regions. An automatically derived white-matter hypointensities index was found to be a suitable means of quantifying white-matter disease. This repository of segmentations is a potentially valuable resource to researchers working with ADNI data. Academic Press 2011-06-15 /pmc/articles/PMC3153069/ /pubmed/21397703 http://dx.doi.org/10.1016/j.neuroimage.2011.03.014 Text en © 2011 Elsevier Inc. https://creativecommons.org/licenses/by/3.0/ Open Access under CC BY 3.0 (https://creativecommons.org/licenses/by/3.0/) license
spellingShingle Article
Heckemann, Rolf A.
Keihaninejad, Shiva
Aljabar, Paul
Gray, Katherine R.
Nielsen, Casper
Rueckert, Daniel
Hajnal, Joseph V.
Hammers, Alexander
Automatic morphometry in Alzheimer's disease and mild cognitive impairment()()
title Automatic morphometry in Alzheimer's disease and mild cognitive impairment()()
title_full Automatic morphometry in Alzheimer's disease and mild cognitive impairment()()
title_fullStr Automatic morphometry in Alzheimer's disease and mild cognitive impairment()()
title_full_unstemmed Automatic morphometry in Alzheimer's disease and mild cognitive impairment()()
title_short Automatic morphometry in Alzheimer's disease and mild cognitive impairment()()
title_sort automatic morphometry in alzheimer's disease and mild cognitive impairment()()
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3153069/
https://www.ncbi.nlm.nih.gov/pubmed/21397703
http://dx.doi.org/10.1016/j.neuroimage.2011.03.014
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