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Brain age prediction using deep learning uncovers associated sequence variants.

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).

SUBMITTER: Jonsson BA 

PROVIDER: S-EPMC6881321 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Brain age prediction using deep learning uncovers associated sequence variants.

Jonsson B A BA   Bjornsdottir G G   Thorgeirsson T E TE   Ellingsen L M LM   Walters G Bragi GB   Gudbjartsson D F DF   Stefansson H H   Stefansson K K   Ulfarsson M O MO  

Nature communications 20191127 1

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 si  ...[more]

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