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Pitfalls in brain age analyses
Over the past decade, there has been an abundance of research on the difference between age and age predicted using brain features, which is commonly referred to as the “brain age gap.” Researchers have identified that the brain age gap, as a linear transformation of an out‐of‐sample residual, is de...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley & Sons, Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8357007/ https://www.ncbi.nlm.nih.gov/pubmed/34190372 http://dx.doi.org/10.1002/hbm.25533 |
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author | Butler, Ellyn R. Chen, Andrew Ramadan, Rabie Le, Trang T. Ruparel, Kosha Moore, Tyler M. Satterthwaite, Theodore D. Zhang, Fengqing Shou, Haochang Gur, Ruben C. Nichols, Thomas E. Shinohara, Russell T. |
author_facet | Butler, Ellyn R. Chen, Andrew Ramadan, Rabie Le, Trang T. Ruparel, Kosha Moore, Tyler M. Satterthwaite, Theodore D. Zhang, Fengqing Shou, Haochang Gur, Ruben C. Nichols, Thomas E. Shinohara, Russell T. |
author_sort | Butler, Ellyn R. |
collection | PubMed |
description | Over the past decade, there has been an abundance of research on the difference between age and age predicted using brain features, which is commonly referred to as the “brain age gap.” Researchers have identified that the brain age gap, as a linear transformation of an out‐of‐sample residual, is dependent on age. As such, any group differences on the brain age gap could simply be due to group differences on age. To mitigate the brain age gap's dependence on age, it has been proposed that age be regressed out of the brain age gap. If this modified brain age gap is treated as a corrected deviation from age, model accuracy statistics such as R (2) will be artificially inflated to the extent that it is highly improbable that an R (2) value below .85 will be obtained no matter the true model accuracy. Given the limitations of proposed brain age analyses, further theoretical work is warranted to determine the best way to quantify deviation from normality. |
format | Online Article Text |
id | pubmed-8357007 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley & Sons, Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-83570072021-08-15 Pitfalls in brain age analyses Butler, Ellyn R. Chen, Andrew Ramadan, Rabie Le, Trang T. Ruparel, Kosha Moore, Tyler M. Satterthwaite, Theodore D. Zhang, Fengqing Shou, Haochang Gur, Ruben C. Nichols, Thomas E. Shinohara, Russell T. Hum Brain Mapp Technical Report Over the past decade, there has been an abundance of research on the difference between age and age predicted using brain features, which is commonly referred to as the “brain age gap.” Researchers have identified that the brain age gap, as a linear transformation of an out‐of‐sample residual, is dependent on age. As such, any group differences on the brain age gap could simply be due to group differences on age. To mitigate the brain age gap's dependence on age, it has been proposed that age be regressed out of the brain age gap. If this modified brain age gap is treated as a corrected deviation from age, model accuracy statistics such as R (2) will be artificially inflated to the extent that it is highly improbable that an R (2) value below .85 will be obtained no matter the true model accuracy. Given the limitations of proposed brain age analyses, further theoretical work is warranted to determine the best way to quantify deviation from normality. John Wiley & Sons, Inc. 2021-06-30 /pmc/articles/PMC8357007/ /pubmed/34190372 http://dx.doi.org/10.1002/hbm.25533 Text en © 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. |
spellingShingle | Technical Report Butler, Ellyn R. Chen, Andrew Ramadan, Rabie Le, Trang T. Ruparel, Kosha Moore, Tyler M. Satterthwaite, Theodore D. Zhang, Fengqing Shou, Haochang Gur, Ruben C. Nichols, Thomas E. Shinohara, Russell T. Pitfalls in brain age analyses |
title | Pitfalls in brain age analyses |
title_full | Pitfalls in brain age analyses |
title_fullStr | Pitfalls in brain age analyses |
title_full_unstemmed | Pitfalls in brain age analyses |
title_short | Pitfalls in brain age analyses |
title_sort | pitfalls in brain age analyses |
topic | Technical Report |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8357007/ https://www.ncbi.nlm.nih.gov/pubmed/34190372 http://dx.doi.org/10.1002/hbm.25533 |
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