Brain age prediction using deep learning uncovers associated sequence variants

B. A. Jonsson, G. Bjornsdottir, T. E. Thorgeirsson, L. M. Ellingsen, G. Bragi Walters, D. F. Gudbjartsson, H. Stefansson, K. Stefansson () and M. O. Ulfarsson ()
Additional contact information
B. A. Jonsson: deCODE Genetics/Amgen, Inc.
G. Bjornsdottir: deCODE Genetics/Amgen, Inc.
T. E. Thorgeirsson: deCODE Genetics/Amgen, Inc.
L. M. Ellingsen: University of Iceland
G. Bragi Walters: deCODE Genetics/Amgen, Inc.
D. F. Gudbjartsson: deCODE Genetics/Amgen, Inc.
H. Stefansson: deCODE Genetics/Amgen, Inc.
K. Stefansson: deCODE Genetics/Amgen, Inc.
M. O. Ulfarsson: deCODE Genetics/Amgen, Inc.

Nature Communications, 2019, vol. 10, issue 1, 1-10

Abstract: Abstract 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: $$N=12378$$N=12378, replication set: $$N=4456$$N=4456) yielded two sequence variants, rs1452628-T ($$\beta =-0.08$$β=−0.08, $$P=1.15\times{10}^{-9}$$P=1.15×10−9) and rs2435204-G ($$\beta =0.102$$β=0.102, $$P=9.73\times 1{0}^{-12}$$P=9.73×10−12). 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).

Date: 2019
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DOI: 10.1038/s41467-019-13163-9

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