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Patch‐wise brain age longitudinal reliability
We recently introduced a patch‐wise technique to estimate brain age from anatomical T1‐weighted magnetic resonance imaging (T1w MRI) data. Here, we sought to assess its longitudinal reliability by leveraging a unique dataset of 99 longitudinal MRI scans from a single, cognitively healthy volunteer a...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley & Sons, Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7814761/ https://www.ncbi.nlm.nih.gov/pubmed/33205863 http://dx.doi.org/10.1002/hbm.25253 |
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author | Beheshti, Iman Potvin, Olivier Duchesne, Simon |
author_facet | Beheshti, Iman Potvin, Olivier Duchesne, Simon |
author_sort | Beheshti, Iman |
collection | PubMed |
description | We recently introduced a patch‐wise technique to estimate brain age from anatomical T1‐weighted magnetic resonance imaging (T1w MRI) data. Here, we sought to assess its longitudinal reliability by leveraging a unique dataset of 99 longitudinal MRI scans from a single, cognitively healthy volunteer acquired over a period of 17 years (aged 29–46 years) at multiple sites. We built a robust patch‐wise brain age estimation framework on the basis of 100 cognitively healthy individuals from the MindBoggle dataset (aged 19–61 years) using the Desikan‐Killiany‐Tourville atlas, then applied the model to the volunteer dataset. The results show a high prediction accuracy on the independent test set (R(2) = .94, mean absolute error of 0.63 years) and no statistically significant difference between manufacturers, suggesting that the patch‐wise technique has high reliability and can be used for longitudinal multi‐centric studies. |
format | Online Article Text |
id | pubmed-7814761 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | John Wiley & Sons, Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78147612021-01-26 Patch‐wise brain age longitudinal reliability Beheshti, Iman Potvin, Olivier Duchesne, Simon Hum Brain Mapp Research Articles We recently introduced a patch‐wise technique to estimate brain age from anatomical T1‐weighted magnetic resonance imaging (T1w MRI) data. Here, we sought to assess its longitudinal reliability by leveraging a unique dataset of 99 longitudinal MRI scans from a single, cognitively healthy volunteer acquired over a period of 17 years (aged 29–46 years) at multiple sites. We built a robust patch‐wise brain age estimation framework on the basis of 100 cognitively healthy individuals from the MindBoggle dataset (aged 19–61 years) using the Desikan‐Killiany‐Tourville atlas, then applied the model to the volunteer dataset. The results show a high prediction accuracy on the independent test set (R(2) = .94, mean absolute error of 0.63 years) and no statistically significant difference between manufacturers, suggesting that the patch‐wise technique has high reliability and can be used for longitudinal multi‐centric studies. John Wiley & Sons, Inc. 2020-11-18 /pmc/articles/PMC7814761/ /pubmed/33205863 http://dx.doi.org/10.1002/hbm.25253 Text en © 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Articles Beheshti, Iman Potvin, Olivier Duchesne, Simon Patch‐wise brain age longitudinal reliability |
title | Patch‐wise brain age longitudinal reliability |
title_full | Patch‐wise brain age longitudinal reliability |
title_fullStr | Patch‐wise brain age longitudinal reliability |
title_full_unstemmed | Patch‐wise brain age longitudinal reliability |
title_short | Patch‐wise brain age longitudinal reliability |
title_sort | patch‐wise brain age longitudinal reliability |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7814761/ https://www.ncbi.nlm.nih.gov/pubmed/33205863 http://dx.doi.org/10.1002/hbm.25253 |
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