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- Title
Brain age prediction using deep learning uncovers associated sequence variants.
- Authors
Jonsson, B. A.; Bjornsdottir, G.; Thorgeirsson, T. E.; Ellingsen, L. M.; Walters, G. Bragi; Gudbjartsson, D. F.; Stefansson, H.; Stefansson, K.; Ulfarsson, M. O.
- 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 , replication set: N = 4456 ) yielded two sequence variants, rs1452628-T (β = − 0.08 , P = 1.15 × 10 − 9 ) and rs2435204-G (β = 0.102 , P = 9.73 × 1 0 − 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). 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.
- Subjects
BRAIN physiology; DEEP learning; MAGNETIC resonance imaging; MACHINE learning; ICELANDERS
- Publication
Nature Communications, 2019, Vol 10, Issue 1, pN.PAG
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-019-13163-9