Brain age prediction using deep learning uncovers associated sequence variants

By Benedikt A. Jónsson, Gyda Bjornsdottir, Thorgeir E. Thorgeirsson, Lotta Maria Ellingsen, G. Bragi Walters, Daniel Fannar Gudbjartsson, Hreinn Stefansson, Kári Stefánsson, M. O. Ulfarsson. Nature Communications, 2019

361 citations recorded by the source corpus.

Machine learning algorithms can be trained to estimate age from brain structural MRI. The difference between an individual’s predicted and chronological age, predicted age difference (PAD), is a phenotype of relevance to aging and brain disease. Here, we present a new deep learning approach to predict brain age from a T1-weighted MRI. The method was trained on a dataset of healthy Icelanders and tested on two datasets, IXI and UK Biobank, utilizing transfer learning to improve accuracy on new sites. A genome-wide association study (GWAS) of PAD in the UK Biobank data (discovery set: N=12378\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$N=12378$$\end{document}, replication set: N=4456\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$N=4456$$\end{document}) yielded two sequence variants, rs1452628-T (β=−0.08\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta =-0.08$$\end{document}, P=1.15×10−9\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P=1.15\times{10}^{-9}$$\end{document}) and rs2435204-G (β=0.102\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta =0.102$$\end{document}, P=9.73×10−12\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P=9.73\times 1{0}^{-12}$$\end{document}). The former is near KCNK2 and correlates with reduced sulcal width, whereas the latter correlates with reduced white matter surface area and tags a well-known inversion at 17q21.31 (H2). Machine learning algorithms can be trained to estimate age from brain structural MRI. Here, the authors introduce a new deep-learning-based age prediction approach, and then carry out a GWAS of the difference between predicted and chronological age, revealing two associated variants.

Subfields: Artificial Intelligence, Health, Toxicology and Mutagenesis

View Paper: https://www.semanticscholar.org/paper/c0ecd56d11eb063ad0a153897aed575c82f72ede

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In Artificial Intelligence

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A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises (2020, background): “In recent years, a large amount of work has been published on DL-based prediction tasks, such as brain age prediction [136]–[138], Alzheimer’s disease classification and trajectory modeling [139]–[142], and schizophrenia classification [143], [144].”