%0 Journal Article
%T Brain-age prediction: Systematic evaluation of site effects, and sample age range and size.
%A Yu Y
%A Cui HQ
%A Haas SS
%A New F
%A Sanford N
%A Yu K
%A Zhan D
%A Yang G
%A Gao JH
%A Wei D
%A Qiu J
%A Banaj N
%A Boomsma DI
%A Breier A
%A Brodaty H
%A Buckner RL
%A Buitelaar JK
%A Cannon DM
%A Caseras X
%A Clark VP
%A Conrod PJ
%A Crivello F
%A Crone EA
%A Dannlowski U
%A Davey CG
%A de Haan L
%A de Zubicaray GI
%A Di Giorgio A
%A Fisch L
%A Fisher SE
%A Franke B
%A Glahn DC
%A Grotegerd D
%A Gruber O
%A Gur RE
%A Gur RC
%A Hahn T
%A Harrison BJ
%A Hatton S
%A Hickie IB
%A Hulshoff Pol HE
%A Jamieson AJ
%A Jernigan TL
%A Jiang J
%A Kalnin AJ
%A Kang S
%A Kochan NA
%A Kraus A
%A Lagopoulos J
%A Lazaro L
%A McDonald BC
%A McDonald C
%A McMahon KL
%A Mwangi B
%A Piras F
%A Rodriguez-Cruces R
%A Royer J
%A Sachdev PS
%A Satterthwaite TD
%A Saykin AJ
%A Schumann G
%A Sevaggi P
%A Smoller JW
%A Soares JC
%A Spalletta G
%A Tamnes CK
%A Trollor JN
%A Van't Ent D
%A Vecchio D
%A Walter H
%A Wang Y
%A Weber B
%A Wen W
%A Wierenga LM
%A Williams SCR
%A Wu MJ
%A Zunta-Soares GB
%A Bernhardt B
%A Thompson P
%A Frangou S
%A Ge R
%A  
%J Hum Brain Mapp
%V 45
%N 10
%D 2024 Jul 15
%M 38949537
%F 5.399
%R 10.1002/hbm.26768
%X 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.