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. In our study, we presented 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 ( =-0.08, P=1.15 10-9) and rs2435204-G ( =0.102, P=9.73 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).
1 Application
Application ID | Title |
24898 | Genetics of lumbar disc disease and associated sciatica |
1 Return
Return ID | App ID | Description | Archive Date |
2282 | 24898 | Brain age prediction using deep learning uncovers associated sequence variants | 28 Jul 2020 |