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

Journal: Neurobiology of aging
Published Date: Sep 1, 2021

Abstract

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.

Authors

  • Kaida Ning
    USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA; Molecular and Computational Biology Program, University of Southern California, Los Angeles, CA, USA.
  • Ben A Duffy
    Laboratory of Neuro Imaging (LONI), Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
  • Meredith Franklin
    Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA.
  • Will Matloff
    USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA; Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA.
  • Lu Zhao
    Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.
  • Nibal Arzouni
    USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA; Molecular and Computational Biology Program, University of Southern California, Los Angeles, CA, USA.
  • Fengzhu Sun
    Molecular and Computational Biology Program, University of Southern California, Los Angeles, California, USA. fsun@usc.edu.
  • Arthur W Toga
    Laboratory of Neuro Imaging, Keck School of Medicine, Stevens Neuroimaging and Informatics Institute, University of Southern California Los Angeles, CA, USA.

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