Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/184155
Title: Individual variations in 'brain age' relate to early-life factors more than to longitudinal brain change
Author: Vidal Piñeiro, Dídac
Wang, Yunpeng
Krogsrud, Stine K.
Amlien, Inge K.
Baaré, William F. C.
Bartrés Faz, David
Bertram, Lars
Brandmaier, Andreas M.
Drevon, Christian A
Düzel, Sandra
Ebmeier, Klaus P.
Henson, Richard N.
Junqué i Plaja, Carme, 1955-
Kievit, Rogier A.
Kühn, Simone
Leonardsen, Esten
Lindenberger, Ulman
Madsen, Kathrine Skak
Magnussen, Fredrik
Mowinckel, Athanasia Monika
Nyberg, Lars
Roe, James M.
Segura i Fàbregas, Bàrbara
Smith, Stephen M.
Sørensen, Øystein
Suri, Sana
Westerhausen, René
Zalesky, Andrew
Zsoldos, Enikő
Walhovd, Kristine Beate
Fjell, Anders Martin
Keywords: Diferències individuals
Envelliment cerebral
Individual differences
Aging brain
Issue Date: 10-Nov-2021
Publisher: eLife Sciences
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.
Note: Reproducció del document publicat a: https://doi.org/10.7554/eLife.69995
It is part of: eLife, 2021, vol. 10, num. e69995
URI: http://hdl.handle.net/2445/184155
Related resource: https://doi.org/10.7554/eLife.69995
https://doi.org/10.7554/eLife.79475
ISSN: 2050-084X
Appears in Collections:Publicacions de projectes de recerca finançats per la UE
Articles publicats en revistes (Medicina)
Articles publicats en revistes (IDIBAPS: Institut d'investigacions Biomèdiques August Pi i Sunyer)
Articles publicats en revistes (Institut de Neurociències (UBNeuro))

Files in This Item:
File Description SizeFormat 
Original article.pdf1.07 MBAdobe PDFView/Open
Correction.pdf2.69 MBAdobe PDFView/Open


This item is licensed under a Creative Commons License Creative Commons