Brain‐age prediction: Systematic evaluation of site effects, and sample age range and size
Authors: 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 , 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 ,   ENIGMA‐Lifespan Working Group
Published: July 2024

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.