Suchergebnisse

Publisher's version (útgefin grein). ; 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). ; This research has been conducted using the UK Biobank Resource under Application Number 24898. The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreements no. 115008 (NEWMEDS) and no. 115300 (EUAIMS), of which resources are composed of EFPIA in-kind contribution and financial contribution from the European Union's Seventh Framework Programme (EU-FP7/2007-2013). The financial support from the European Commission to the NeuroPain project (FP7#HEALTH-2013-602891-2) is acknowledged. The authors are grateful to the participants, and we thank the research nurses and staff at the Recruitment centre (Þjónustumiðstöð rannsóknarverkefna). ; Peer Reviewed