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Brain region’s relative proximity as marker for Alzheimer’s disease based on structural MRI
BACKGROUND: Alzheimer’s disease (AD) is a progressive, incurable neurodegenerative disease and the most common type of dementia. It cannot be prevented, cured or drastically slowed, even though AD research has increased in the past 5-10 years. Instead of focusing on the brain volume or on the single...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4048460/ https://www.ncbi.nlm.nih.gov/pubmed/24889999 http://dx.doi.org/10.1186/1471-2342-14-21 |
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author | Lillemark, Lene Sørensen, Lauge Pai, Akshay Dam, Erik B Nielsen, Mads |
author_facet | Lillemark, Lene Sørensen, Lauge Pai, Akshay Dam, Erik B Nielsen, Mads |
author_sort | Lillemark, Lene |
collection | PubMed |
description | BACKGROUND: Alzheimer’s disease (AD) is a progressive, incurable neurodegenerative disease and the most common type of dementia. It cannot be prevented, cured or drastically slowed, even though AD research has increased in the past 5-10 years. Instead of focusing on the brain volume or on the single brain structures like hippocampus, this paper investigates the relationship and proximity between regions in the brain and uses this information as a novel way of classifying normal control (NC), mild cognitive impaired (MCI), and AD subjects. METHODS: A longitudinal cohort of 528 subjects (170 NC, 240 MCI, and 114 AD) from ADNI at baseline and month 12 was studied. We investigated a marker based on Procrustes aligned center of masses and the percentile surface connectivity between regions. These markers were classified using a linear discriminant analysis in a cross validation setting and compared to whole brain and hippocampus volume. RESULTS: We found that both our markers was able to significantly classify the subjects. The surface connectivity marker showed the best results with an area under the curve (AUC) at 0.877 (p<0.001), 0.784 (p<0.001), 0,766 (p<0.001) for NC-AD, NC-MCI, and MCI-AD, respectively, for the functional regions in the brain. The surface connectivity marker was able to classify MCI-converters with an AUC of 0.599 (p<0.05) for the 1-year period. CONCLUSION: Our results show that our relative proximity markers include more information than whole brain and hippocampus volume. Our results demonstrate that our proximity markers have the potential to assist in early diagnosis of AD. |
format | Online Article Text |
id | pubmed-4048460 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-40484602014-06-23 Brain region’s relative proximity as marker for Alzheimer’s disease based on structural MRI Lillemark, Lene Sørensen, Lauge Pai, Akshay Dam, Erik B Nielsen, Mads BMC Med Imaging Research Article BACKGROUND: Alzheimer’s disease (AD) is a progressive, incurable neurodegenerative disease and the most common type of dementia. It cannot be prevented, cured or drastically slowed, even though AD research has increased in the past 5-10 years. Instead of focusing on the brain volume or on the single brain structures like hippocampus, this paper investigates the relationship and proximity between regions in the brain and uses this information as a novel way of classifying normal control (NC), mild cognitive impaired (MCI), and AD subjects. METHODS: A longitudinal cohort of 528 subjects (170 NC, 240 MCI, and 114 AD) from ADNI at baseline and month 12 was studied. We investigated a marker based on Procrustes aligned center of masses and the percentile surface connectivity between regions. These markers were classified using a linear discriminant analysis in a cross validation setting and compared to whole brain and hippocampus volume. RESULTS: We found that both our markers was able to significantly classify the subjects. The surface connectivity marker showed the best results with an area under the curve (AUC) at 0.877 (p<0.001), 0.784 (p<0.001), 0,766 (p<0.001) for NC-AD, NC-MCI, and MCI-AD, respectively, for the functional regions in the brain. The surface connectivity marker was able to classify MCI-converters with an AUC of 0.599 (p<0.05) for the 1-year period. CONCLUSION: Our results show that our relative proximity markers include more information than whole brain and hippocampus volume. Our results demonstrate that our proximity markers have the potential to assist in early diagnosis of AD. BioMed Central 2014-06-02 /pmc/articles/PMC4048460/ /pubmed/24889999 http://dx.doi.org/10.1186/1471-2342-14-21 Text en Copyright © 2014 Lillemark et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Lillemark, Lene Sørensen, Lauge Pai, Akshay Dam, Erik B Nielsen, Mads Brain region’s relative proximity as marker for Alzheimer’s disease based on structural MRI |
title | Brain region’s relative proximity as marker for Alzheimer’s disease based on structural MRI |
title_full | Brain region’s relative proximity as marker for Alzheimer’s disease based on structural MRI |
title_fullStr | Brain region’s relative proximity as marker for Alzheimer’s disease based on structural MRI |
title_full_unstemmed | Brain region’s relative proximity as marker for Alzheimer’s disease based on structural MRI |
title_short | Brain region’s relative proximity as marker for Alzheimer’s disease based on structural MRI |
title_sort | brain region’s relative proximity as marker for alzheimer’s disease based on structural mri |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4048460/ https://www.ncbi.nlm.nih.gov/pubmed/24889999 http://dx.doi.org/10.1186/1471-2342-14-21 |
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