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Nat Commun. 2019 Nov 27;10(1):5409. doi: 10.1038/s41467-019-13163-9.

Brain age prediction using deep learning uncovers associated sequence variants.

Author information

1
deCODE Genetics/Amgen, Inc., 101, Reykjavik, Iceland.
2
University of Iceland, 101, Reykjavik, Iceland.
3
deCODE Genetics/Amgen, Inc., 101, Reykjavik, Iceland. kstefans@decode.is.
4
University of Iceland, 101, Reykjavik, Iceland. kstefans@decode.is.
5
deCODE Genetics/Amgen, Inc., 101, Reykjavik, Iceland. mou@hi.is.
6
University of Iceland, 101, Reykjavik, Iceland. mou@hi.is.

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: [Formula: see text], replication set: [Formula: see text]) yielded two sequence variants, rs1452628-T ([Formula: see text], [Formula: see text]) and rs2435204-G ([Formula: see text], [Formula: see text]). 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).

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