Brain-age prediction: Systematic evaluation of site effects, and sample age range and size

Yuetong Yu, Hao Qi Cui, ENIGMA Lifespan Working Group, Shalaila S. Haas, Faye New, Nicole Sanford, Kevin Yu, Denghuang Zhan, Guoyuan Yang, Jia Hong Gao, Dongtao Wei, Jiang Qiu, Nerisa Banaj, Dorret I. Boomsma, Alan Breier, Henry Brodaty, Randy L. Buckner, Jan K. Buitelaar, Dara M. Cannon, Xavier CaserasVincent P. Clark, Patricia J. Conrod, Fabrice Crivello, Eveline A. Crone, Udo Dannlowski, Christopher G. Davey, Lieuwe de Haan, Greig I. de Zubicaray, Annabella Di Giorgio, Lukas Fisch, Simon E. Fisher, Barbara Franke, David C. Glahn, Dominik Grotegerd, Oliver Gruber, Raquel E. Gur, Ruben C. Gur, Tim Hahn, Ben J. Harrison, Sean Hatton, Ian B. Hickie, Hilleke E. Hulshoff Pol, Alec J. Jamieson, Terry L. Jernigan, Jiyang Jiang, Andrew J. Kalnin, Sim Kang, Nicole A. Kochan, Anna Kraus, Jim Lagopoulos, Luisa Lazaro, Sophia Frangou*

*Corresponding author for this work

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Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5–90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8–80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9–25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5–40 and 40–90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (, an open-science, web-based platform for individualized neuroimaging metrics.

Original languageEnglish
Article numbere26768
JournalHuman Brain Mapping
Issue number10
Publication statusPublished - 1 Jul 2024

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© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.

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