Individual variations in 'brain age' relate to early-life factors more than to longitudinal brain change.


Change log
Authors
Vidal-Pineiro, Didac  ORCID logo  https://orcid.org/0000-0001-9997-9156
Wang, Yunpeng 
Krogsrud, Stine K 
Amlien, Inge K 
Baaré, William Fc 
Abstract

Brain age is a widely used index for quantifying individuals' brain health as deviation from a normative brain aging trajectory. Higher-than-expected brain age is thought partially to reflect above-average rate of brain aging. Here, we explicitly tested this assumption in two independent large test datasets (UK Biobank [main] and Lifebrain [replication]; longitudinal observations ≈ 2750 and 4200) by assessing the relationship between cross-sectional and longitudinal estimates of brain age. Brain age models were estimated in two different training datasets (n ≈ 38,000 [main] and 1800 individuals [replication]) based on brain structural features. The results showed no association between cross-sectional brain age and the rate of brain change measured longitudinally. Rather, brain age in adulthood was associated with the congenital factors of birth weight and polygenic scores of brain age, assumed to reflect a constant, lifelong influence on brain structure from early life. The results call for nuanced interpretations of cross-sectional indices of the aging brain and question their validity as markers of ongoing within-person changes of the aging brain. Longitudinal imaging data should be preferred whenever the goal is to understand individual change trajectories of brain and cognition in aging.

Description

Funder: Norges Forskningsråd; FundRef: http://dx.doi.org/10.13039/501100005416


Funder: Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg; FundRef: http://dx.doi.org/10.13039/501100012319


Funder: NIHR Biomedical Research Centre, Oxford; FundRef: http://dx.doi.org/10.13039/501100013373


Funder: Knut and Alice Wallenberg Foundation; FundRef: http://dx.doi.org/10.13039/501100004063


Funder: ICREA Academia Award

Keywords
Research Article, Neuroscience, Aging, brain age gap, Brain age delta, brain decline, neuroimaging, T1w, Human
Journal Title
Elife
Conference Name
Journal ISSN
2050-084X
2050-084X
Volume Title
10
Publisher
eLife Sciences Publications, Ltd
Sponsorship
H2020 European Research Council (732592)
H2020 European Research Council (283634 725025)
H2020 European Research Council (313440)
María de Maeztu Unit of Excellence (Institute of Neurosciences,University of Barcelona) (MDM-2017-0729)
European Research Council (677804)
UK Medical Research Council (G1001354)
Charitable Trust (1117747)
Alzheimer’s Research UK (441)
Norges Forskningsråd (324882)
Medical Research Council (SUAG/046 G101400)