Open AccessPitfalls in brain age analyses
Author(s) -
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.
Publication year - 2021
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.25533
Subject(s) - normality , age groups , psychology , statistics , demography , mathematics , sociology
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.