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Brain age estimation at tract group level and its association with daily life measures, cardiac risk factors and genetic variants
Brain age can be estimated using different Magnetic Resonance Imaging (MRI) modalities including diffusion MRI. Recent studies demonstrated that white matter (WM) tracts that share the same function might experience similar alterations. Therefore, in this work, we sought to investigate such issue fo...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8523533/ https://www.ncbi.nlm.nih.gov/pubmed/34663856 http://dx.doi.org/10.1038/s41598-021-99153-8 |
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author | Salih, Ahmed Boscolo Galazzo, Ilaria Raisi-Estabragh, Zahra Rauseo, Elisa Gkontra, Polyxeni Petersen, Steffen E. Lekadir, Karim Altmann, André Radeva, Petia Menegaz, Gloria |
author_facet | Salih, Ahmed Boscolo Galazzo, Ilaria Raisi-Estabragh, Zahra Rauseo, Elisa Gkontra, Polyxeni Petersen, Steffen E. Lekadir, Karim Altmann, André Radeva, Petia Menegaz, Gloria |
author_sort | Salih, Ahmed |
collection | PubMed |
description | Brain age can be estimated using different Magnetic Resonance Imaging (MRI) modalities including diffusion MRI. Recent studies demonstrated that white matter (WM) tracts that share the same function might experience similar alterations. Therefore, in this work, we sought to investigate such issue focusing on five WM bundles holding that feature that is Association, Brainstem, Commissural, Limbic and Projection fibers, respectively. For each tract group, we estimated brain age for 15,335 healthy participants from United Kingdom Biobank relying on diffusion MRI data derived endophenotypes, Bayesian ridge regression modeling and 10 fold-cross validation. Furthermore, we estimated brain age for an Ensemble model that gathers all the considered WM bundles. Association analysis was subsequently performed between the estimated brain age delta as resulting from the six models, that is for each tract group as well as for the Ensemble model, and 38 daily life style measures, 14 cardiac risk factors and cardiovascular magnetic resonance imaging features and genetic variants. The Ensemble model that used all tracts from all fiber groups (FG) performed better than other models to estimate brain age. Limbic tracts based model reached the highest accuracy with a Mean Absolute Error (MAE) of 5.08, followed by the Commissural ([Formula: see text] ), Association ([Formula: see text] ), and Projection ([Formula: see text] ) ones. The Brainstem tracts based model was the less accurate achieving a MAE of 5.86. Accordingly, our study suggests that the Limbic tracts experience less brain aging or allows for more accurate estimates compared to other tract groups. Moreover, the results suggest that Limbic tract leads to the largest number of significant associations with daily lifestyle factors than the other tract groups. Lastly, two SNPs were significantly (p value [Formula: see text] ) associated with brain age delta in the Projection fibers. Those SNPs are mapped to HIST1H1A and SLC17A3 genes. |
format | Online Article Text |
id | pubmed-8523533 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-85235332021-10-20 Brain age estimation at tract group level and its association with daily life measures, cardiac risk factors and genetic variants Salih, Ahmed Boscolo Galazzo, Ilaria Raisi-Estabragh, Zahra Rauseo, Elisa Gkontra, Polyxeni Petersen, Steffen E. Lekadir, Karim Altmann, André Radeva, Petia Menegaz, Gloria Sci Rep Article Brain age can be estimated using different Magnetic Resonance Imaging (MRI) modalities including diffusion MRI. Recent studies demonstrated that white matter (WM) tracts that share the same function might experience similar alterations. Therefore, in this work, we sought to investigate such issue focusing on five WM bundles holding that feature that is Association, Brainstem, Commissural, Limbic and Projection fibers, respectively. For each tract group, we estimated brain age for 15,335 healthy participants from United Kingdom Biobank relying on diffusion MRI data derived endophenotypes, Bayesian ridge regression modeling and 10 fold-cross validation. Furthermore, we estimated brain age for an Ensemble model that gathers all the considered WM bundles. Association analysis was subsequently performed between the estimated brain age delta as resulting from the six models, that is for each tract group as well as for the Ensemble model, and 38 daily life style measures, 14 cardiac risk factors and cardiovascular magnetic resonance imaging features and genetic variants. The Ensemble model that used all tracts from all fiber groups (FG) performed better than other models to estimate brain age. Limbic tracts based model reached the highest accuracy with a Mean Absolute Error (MAE) of 5.08, followed by the Commissural ([Formula: see text] ), Association ([Formula: see text] ), and Projection ([Formula: see text] ) ones. The Brainstem tracts based model was the less accurate achieving a MAE of 5.86. Accordingly, our study suggests that the Limbic tracts experience less brain aging or allows for more accurate estimates compared to other tract groups. Moreover, the results suggest that Limbic tract leads to the largest number of significant associations with daily lifestyle factors than the other tract groups. Lastly, two SNPs were significantly (p value [Formula: see text] ) associated with brain age delta in the Projection fibers. Those SNPs are mapped to HIST1H1A and SLC17A3 genes. Nature Publishing Group UK 2021-10-18 /pmc/articles/PMC8523533/ /pubmed/34663856 http://dx.doi.org/10.1038/s41598-021-99153-8 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Salih, Ahmed Boscolo Galazzo, Ilaria Raisi-Estabragh, Zahra Rauseo, Elisa Gkontra, Polyxeni Petersen, Steffen E. Lekadir, Karim Altmann, André Radeva, Petia Menegaz, Gloria Brain age estimation at tract group level and its association with daily life measures, cardiac risk factors and genetic variants |
title | Brain age estimation at tract group level and its association with daily life measures, cardiac risk factors and genetic variants |
title_full | Brain age estimation at tract group level and its association with daily life measures, cardiac risk factors and genetic variants |
title_fullStr | Brain age estimation at tract group level and its association with daily life measures, cardiac risk factors and genetic variants |
title_full_unstemmed | Brain age estimation at tract group level and its association with daily life measures, cardiac risk factors and genetic variants |
title_short | Brain age estimation at tract group level and its association with daily life measures, cardiac risk factors and genetic variants |
title_sort | brain age estimation at tract group level and its association with daily life measures, cardiac risk factors and genetic variants |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8523533/ https://www.ncbi.nlm.nih.gov/pubmed/34663856 http://dx.doi.org/10.1038/s41598-021-99153-8 |
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