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

Authors :
Yu Y
Cui HQ
Haas SS
New F
Sanford N
Yu K
Zhan D
Yang G
Gao JH
Wei D
Qiu J
Banaj N
Boomsma DI
Breier A
Brodaty H
Buckner RL
Buitelaar JK
Cannon DM
Caseras X
Clark VP
Conrod PJ
Crivello F
Crone EA
Dannlowski U
Davey CG
de Haan L
de Zubicaray GI
Di Giorgio A
Fisch L
Fisher SE
Franke B
Glahn DC
Grotegerd D
Gruber O
Gur RE
Gur RC
Hahn T
Harrison BJ
Hatton S
Hickie IB
Hulshoff Pol HE
Jamieson AJ
Jernigan TL
Jiang J
Kalnin AJ
Kang S
Kochan NA
Kraus A
Lagopoulos J
Lazaro L
McDonald BC
McDonald C
McMahon KL
Mwangi B
Piras F
Rodriguez-Cruces R
Royer J
Sachdev PS
Satterthwaite TD
Saykin AJ
Schumann G
Sevaggi P
Smoller JW
Soares JC
Spalletta G
Tamnes CK
Trollor JN
Van't Ent D
Vecchio D
Walter H
Wang Y
Weber B
Wen W
Wierenga LM
Williams SCR
Wu MJ
Zunta-Soares GB
Bernhardt B
Thompson P
Frangou S
Ge R
Source :
Human brain mapping [Hum Brain Mapp] 2024 Jul 15; Vol. 45 (10), pp. e26768.
Publication Year :
2024

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

Details

Language :
English
ISSN :
1097-0193
Volume :
45
Issue :
10
Database :
MEDLINE
Journal :
Human brain mapping
Publication Type :
Academic Journal
Accession number :
38949537
Full Text :
https://doi.org/10.1002/hbm.26768