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

Authors :
Vidal-Pineiro, Didac
Wang, Yunpeng
Krogsrud, Stine K.
Amlien, Inge K.
Baaré, William FC
Bartres-Faz, David
Bertram, Lars
Brandmaier, Andreas M.
Drevon, Christian A.
Düzel, Sandra
Ebmeier, Klaus
Henson, Richard N.
Junqué, Carme
Kievit, Rogier Andrew
Kühn, Simone
Leonardsen, Esten
Lindenberger, Ulman
Madsen, Kathrine S.
Magnussen, Fredrik
Mowinckel, Athanasia Monika
Nyberg, Lars
Roe, James M.
Segura, Barbara
Smith, Stephen M.
Sørensen, Øystein
Suri, Sana
Westerhausen, Rene
Zalesky, Andrew
Zsoldos, Enikő
Walhovd, Kristine Beate
Fjell, Anders
Vidal-Pineiro, Didac
Wang, Yunpeng
Krogsrud, Stine K.
Amlien, Inge K.
Baaré, William FC
Bartres-Faz, David
Bertram, Lars
Brandmaier, Andreas M.
Drevon, Christian A.
Düzel, Sandra
Ebmeier, Klaus
Henson, Richard N.
Junqué, Carme
Kievit, Rogier Andrew
Kühn, Simone
Leonardsen, Esten
Lindenberger, Ulman
Madsen, Kathrine S.
Magnussen, Fredrik
Mowinckel, Athanasia Monika
Nyberg, Lars
Roe, James M.
Segura, Barbara
Smith, Stephen M.
Sørensen, Øystein
Suri, Sana
Westerhausen, Rene
Zalesky, Andrew
Zsoldos, Enikő
Walhovd, Kristine Beate
Fjell, Anders
Publication Year :
2021

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 indepen-dent 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.

Details

Database :
OAIster
Notes :
application/pdf, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1290266508
Document Type :
Electronic Resource
Full Text :
https://doi.org/10.7554.eLife.69995