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Jonsson, B A; Bjornsdottir, G; Thorgeirsson, T E; Ellingsen, L M; Walters, G Bragi; Gudbjartsson, D F; Stefansson, H; Stefansson, K; Ulfarsson, M O
Nature communications, 11/2019, Letnik: 10, Številka: 1Journal Article
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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