期刊
NATURE COMMUNICATIONS
卷 10, 期 -, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41467-019-13163-9
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资金
- Innovative Medicines Initiative Joint Undertaking from the European Union [115008, 115300]
- European Commission [HEALTH-2013-602891-2]
- MRC [MC_PC_12028] Funding Source: UKRI
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 (beta = -0.08, P = 1.15 x 10(-9)) and rs2435204-G (beta = 0.102, P = 9.73 x 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).
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