Improving brain age estimates with deep learning leads to identification of novel genetic factors associated with brain aging.
Convolutional neural network
Genetics
Predicted brain age
Relative brain age
Journal
Neurobiology of aging
ISSN: 1558-1497
Titre abrégé: Neurobiol Aging
Pays: United States
ID NLM: 8100437
Informations de publication
Date de publication:
09 2021
09 2021
Historique:
received:
30
09
2020
revised:
24
03
2021
accepted:
25
03
2021
pubmed:
8
6
2021
medline:
24
12
2021
entrez:
7
6
2021
Statut:
ppublish
Résumé
To study genetic factors associated with brain aging, we first need to quantify brain aging. Statistical models have been created for estimating the apparent age of the brain, or predicted brain age (PBA), using imaging data. Recent studies have refined these models to obtain a more accurate PBA, but research has yet to demonstrate the scientific value of doing so. Here, we show that a more accurate PBA leads to better characterization of genetic factors associated with brain aging. We trained a convolutional neural network (CNN) model on 16,998 UK Biobank subjects to derive PBA, then conducted a genome-wide association study on the PBA, in which we identified single nucleotide polymorphisms from four independent loci significantly associated with brain aging, three of which were novel. By comparing association results based on the CNN-derived PBA to those based on a linear regression-derived PBA, we concluded that a more accurate PBA enables the discovery of novel genetic associations. Our results may be valuable for identifying other lifestyle factors associated with brain aging.
Identifiants
pubmed: 34098431
pii: S0197-4580(21)00112-3
doi: 10.1016/j.neurobiolaging.2021.03.014
pmc: PMC9004720
mid: NIHMS1711876
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
199-204Subventions
Organisme : Medical Research Council
ID : MC_PC_17228
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_QA137853
Pays : United Kingdom
Organisme : NIBIB NIH HHS
ID : P41 EB015922
Pays : United States
Organisme : NIBIB NIH HHS
ID : U54 EB020406
Pays : United States
Informations de copyright
Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.
Références
Front Aging Neurosci. 2013 Dec 17;5:90
pubmed: 24381557
Acta Neuropathol. 2008 Dec;116(6):603-14
pubmed: 18836734
Neuroimage. 2021 Jan 1;224:117002
pubmed: 32502668
Nature. 2017 Feb 2;542(7639):115-118
pubmed: 28117445
Nat Commun. 2017 Oct 13;8(1):914
pubmed: 29030550
Mol Psychiatry. 2017 Mar;22(3):336-345
pubmed: 28093568
Neuroimage. 2017 Dec;163:115-124
pubmed: 28765056
Nat Commun. 2017 Nov 28;8(1):1826
pubmed: 29184056
Glia. 2010 Mar;58(4):458-68
pubmed: 19780200
J Appl Physiol (1985). 2006 Oct;101(4):1237-42
pubmed: 16778001
Nat Genet. 2018 Aug;50(8):1112-1121
pubmed: 30038396
Neuroimage. 2019 Oct 15;200:528-539
pubmed: 31201988
Nat Genet. 2015 Mar;47(3):284-90
pubmed: 25642633
Nat Rev Endocrinol. 2018 Mar;14(3):174-182
pubmed: 29376523
Med Image Anal. 2021 Feb;68:101871
pubmed: 33197716
Ann Neurol. 1985 Jan;17(1):2-10
pubmed: 3885841
Ann Intern Med. 2006 Jan 17;144(2):73-81
pubmed: 16418406
IEEE Trans Med Imaging. 2020 May;39(5):1430-1437
pubmed: 31675324
Science. 2015 Aug 7;349(6248):1255555
pubmed: 26250687
Mol Cell Biol. 2003 May;23(9):3339-51
pubmed: 12697832
Neuroimage. 2010 Apr 15;50(3):883-92
pubmed: 20070949
Sci Rep. 2020 Jan 30;10(1):10
pubmed: 32001736
Can J Psychiatry. 2008 Jun;53(6):346-53
pubmed: 18616854
Am J Psychiatry. 2016 Jun 1;173(6):607-16
pubmed: 26917166
Front Neurosci. 2018 Nov 05;12:777
pubmed: 30455622
J Neurosci. 2004 Dec 15;24(50):11215-25
pubmed: 15601927
Nat Commun. 2019 Nov 27;10(1):5409
pubmed: 31776335
Clin Endocrinol (Oxf). 2016 May;84(5):756-63
pubmed: 26406918
Sci Rep. 2020 Apr 8;10(1):6100
pubmed: 32269255
Neuroimage. 2012 Aug 15;62(2):774-81
pubmed: 22248573
Trends Neurosci. 2017 Dec;40(12):681-690
pubmed: 29074032