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Identifying brain changes related to cognitive aging using VBM and visual rating scales
Aging is often associated with changes in brain structures as well as in cognitive functions. Structural changes can be visualized with Magnetic Resonance Imaging (MRI) using voxel-based grey matter morphometry (VBM) and visual rating scales to assess atrophy level. Several MRI studies have shown th...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6370556/ https://www.ncbi.nlm.nih.gov/pubmed/30739844 http://dx.doi.org/10.1016/j.nicl.2019.101697 |
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author | Pergher, Valentina Demaerel, Philippe Soenen, Olivier Saarela, Carina Tournoy, Jos Schoenmakers, Birgitte Karrasch, Mira Van Hulle, Marc M. |
author_facet | Pergher, Valentina Demaerel, Philippe Soenen, Olivier Saarela, Carina Tournoy, Jos Schoenmakers, Birgitte Karrasch, Mira Van Hulle, Marc M. |
author_sort | Pergher, Valentina |
collection | PubMed |
description | Aging is often associated with changes in brain structures as well as in cognitive functions. Structural changes can be visualized with Magnetic Resonance Imaging (MRI) using voxel-based grey matter morphometry (VBM) and visual rating scales to assess atrophy level. Several MRI studies have shown that possible neural correlates of cognitive changes can be seen in normal aging. It is still not fully understood how cognitive function as measured by tests and demographic factors are related to brain changes in the MRI. We recruited 55 healthy elderly subjects aged 50–79 years. A battery of cognitive tests was administered to all subjects prior to MRI scanning. Our aim was to assess correlations between age, sex, education, cognitive test performance, and the said two MRI-based measures. Our results show significant differences in VBM grey matter volume for education level (≤ 12 vs. > 12 years), with a smaller amount of grey matter volume in subjects with lower educational levels, and for age in interaction with education, indicating larger grey matter volume for young, higher educated adults. Also, grey matter volume was found to be correlated with working memory function (Digit Span Backward). Furthermore, significant positive correlations were found between visual ratings and both age and education, showing larger atrophy levels with increasing age and decreasing level of education. These findings provide supportive evidence that MRI-VBM detects structural differences for education level, and correlates with educational level and age, and working memory task performance. |
format | Online Article Text |
id | pubmed-6370556 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-63705562019-02-21 Identifying brain changes related to cognitive aging using VBM and visual rating scales Pergher, Valentina Demaerel, Philippe Soenen, Olivier Saarela, Carina Tournoy, Jos Schoenmakers, Birgitte Karrasch, Mira Van Hulle, Marc M. Neuroimage Clin Regular Article Aging is often associated with changes in brain structures as well as in cognitive functions. Structural changes can be visualized with Magnetic Resonance Imaging (MRI) using voxel-based grey matter morphometry (VBM) and visual rating scales to assess atrophy level. Several MRI studies have shown that possible neural correlates of cognitive changes can be seen in normal aging. It is still not fully understood how cognitive function as measured by tests and demographic factors are related to brain changes in the MRI. We recruited 55 healthy elderly subjects aged 50–79 years. A battery of cognitive tests was administered to all subjects prior to MRI scanning. Our aim was to assess correlations between age, sex, education, cognitive test performance, and the said two MRI-based measures. Our results show significant differences in VBM grey matter volume for education level (≤ 12 vs. > 12 years), with a smaller amount of grey matter volume in subjects with lower educational levels, and for age in interaction with education, indicating larger grey matter volume for young, higher educated adults. Also, grey matter volume was found to be correlated with working memory function (Digit Span Backward). Furthermore, significant positive correlations were found between visual ratings and both age and education, showing larger atrophy levels with increasing age and decreasing level of education. These findings provide supportive evidence that MRI-VBM detects structural differences for education level, and correlates with educational level and age, and working memory task performance. Elsevier 2019-02-05 /pmc/articles/PMC6370556/ /pubmed/30739844 http://dx.doi.org/10.1016/j.nicl.2019.101697 Text en © 2019 Published by Elsevier Inc. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Regular Article Pergher, Valentina Demaerel, Philippe Soenen, Olivier Saarela, Carina Tournoy, Jos Schoenmakers, Birgitte Karrasch, Mira Van Hulle, Marc M. Identifying brain changes related to cognitive aging using VBM and visual rating scales |
title | Identifying brain changes related to cognitive aging using VBM and visual rating scales |
title_full | Identifying brain changes related to cognitive aging using VBM and visual rating scales |
title_fullStr | Identifying brain changes related to cognitive aging using VBM and visual rating scales |
title_full_unstemmed | Identifying brain changes related to cognitive aging using VBM and visual rating scales |
title_short | Identifying brain changes related to cognitive aging using VBM and visual rating scales |
title_sort | identifying brain changes related to cognitive aging using vbm and visual rating scales |
topic | Regular Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6370556/ https://www.ncbi.nlm.nih.gov/pubmed/30739844 http://dx.doi.org/10.1016/j.nicl.2019.101697 |
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