Brain‐age prediction: systematic evaluation of site effects, and sample age range and size

Yu, Yuetong, Cui, Hao‐Qi, Haas, Shalaila S., New, Faye, Sanford, Nicole, Yu, Kevin, Zhan, Denghuang, Yang, Guoyuan, Gao, Jia‐Hong, Wei, Dongtao, Qiu, Jiang, Banaj, Nerisa, Boomsma, Dorret I., Breier, Alan, Brodaty, Henry, Buckner, Randy L., Buitelaar, Jan K., Cannon, Dara M., Caseras, Xavier ORCID: https://orcid.org/0000-0002-8490-6891, Clark, Vincent P., Conrod, Patricia J., Crivello, Fabrice, Crone, Eveline A., Dannlowski, Udo, Davey, Christopher G., de Haan, Lieuwe, de Zubicaray, Greig I., Di Giorgio, Annabella, Fisch, Lukas, Fisher, Simon E., Franke, Barbara, Glahn, David C., Grotegerd, Dominik, Gruber, Oliver, Gur, Raquel E., Gur, Ruben C., Hahn, Tim, Harrison, Ben J., Hatton, Sean, Hickie, Ian B., Hulshoff Pol, Hilleke E., Jamieson, Alec J., Jernigan, Terry L., Jiang, Jiyang, Kalnin, Andrew J., Kang, Sim, Kochan, Nicole A., Kraus, Anna, Lagopoulos, Jim, Lazaro, Luisa, McDonald, Brenna C., McDonald, Colm, McMahon, Katie L., Mwangi, Benson, Piras, Fabrizio, Rodriguez‐Cruces, Raul, Royer, Jessica, Sachdev, Perminder S., Satterthwaite, Theodore D., Saykin, Andrew J., Schumann, Gunter, Sevaggi, Pierluigi, Smoller, Jordan W., Soares, Jair C., Spalletta, Gianfranco, Tamnes, Christian K., Trollor, Julian N., Van't Ent, Dennis, Vecchio, Daniela, Walter, Henrik, Wang, Yang, Weber, Bernd, Wen, Wei, Wierenga, Lara M., Williams, Steven C. R., Wu, Mon‐Ju, Zunta‐Soares, Giovana B., Bernhardt, Boris, Thompson, Paul, Frangou, Sophia, Ge, Ruiyang and ENIGMA‐Lifespan Working Group 2024. Brain‐age prediction: systematic evaluation of site effects, and sample age range and size. Human Brain Mapping 45 (10) , e26768. 10.1002/hbm.26768

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Abstract

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 (https://centilebrain.org/#/brainAGE2), an open‐science, web‐based platform for individualized neuroimaging metrics.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Medicine
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by-nc-nd/4.0/
Publisher:	Wiley Open Access
ISSN:	1065-9471
Date of First Compliant Deposit:	2 July 2024
Date of Acceptance:	10 June 2024
Last Modified:	02 Jul 2024 09:45
URI:	https://orca.cardiff.ac.uk/id/eprint/170209

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