Improving brain age estimates with deep learning leads to identification of novel genetic factors associated with brain aging

•We analyzed brain imaging and genetic data from more than 15,000 UK Biobank subjects.•We trained a convolutional neural network model to accurately predict brain age.•We identified novel genetic factors significantly associated with brain aging. To study genetic factors associated with brain aging,...

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Bibliographic Details
Published in:Neurobiology of aging 2021-09, Vol.105, p.199-204
Main Authors: Ning, Kaida, Duffy, Ben A., Franklin, Meredith, Matloff, Will, Zhao, Lu, Arzouni, Nibal, Sun, Fengzhu, Toga, Arthur W.
Format: Article
Language:English
Subjects:
Aged
Aged, 80 and over
Aging - genetics
Brain - pathology
Brain - physiology
Convolutional neural network
Deep Learning
Female
Genetics
Genome-Wide Association Study - methods
Humans
Life Style
Male
Middle Aged
Neural Networks, Computer
Polymorphism, Single Nucleotide
Predicted brain age
Relative brain age
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Summary:•We analyzed brain imaging and genetic data from more than 15,000 UK Biobank subjects.•We trained a convolutional neural network model to accurately predict brain age.•We identified novel genetic factors significantly associated with brain aging. 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.
ISSN:0197-4580
1558-1497
DOI:10.1016/j.neurobiolaging.2021.03.014