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...
| Main Authors: | , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Wiley
2021
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| _version_ | 1797100185746145280 |
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| author | Butler, ER Chen, A Ramadan, R Nichols, T Shinohara, RT |
| author_facet | Butler, ER Chen, A Ramadan, R Nichols, T Shinohara, RT |
| author_sort | Butler, ER |
| collection | OXFORD |
| 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 (MBAG) is treated as a corrected deviation from age, model accuracy
statistics such as R2 will be artificially inflated to the extent that it is highly
improbable that an R2 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. |
| first_indexed | 2024-03-07T05:34:10Z |
| format | Journal article |
| id | oxford-uuid:e34e6da6-b26b-40aa-9b22-fd7c117b2ede |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T05:34:10Z |
| publishDate | 2021 |
| publisher | Wiley |
| record_format | dspace |
| spelling | oxford-uuid:e34e6da6-b26b-40aa-9b22-fd7c117b2ede2022-03-27T10:08:10ZPitfalls in brain age analysesJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:e34e6da6-b26b-40aa-9b22-fd7c117b2edeEnglishSymplectic ElementsWiley2021Butler, ERChen, ARamadan, RNichols, TShinohara, RTOver 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 (MBAG) is treated as a corrected deviation from age, model accuracy statistics such as R2 will be artificially inflated to the extent that it is highly improbable that an R2 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. |
| spellingShingle | Butler, ER Chen, A Ramadan, R Nichols, T Shinohara, RT 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 |
| work_keys_str_mv | AT butlerer pitfallsinbrainageanalyses AT chena pitfallsinbrainageanalyses AT ramadanr pitfallsinbrainageanalyses AT nicholst pitfallsinbrainageanalyses AT shinoharart pitfallsinbrainageanalyses |