dc.contributor |
Háskóli Íslands |
dc.contributor |
University of Iceland |
dc.contributor.author |
Jónsson, Benedikt Atli |
dc.contributor.author |
Bjornsdottir, Gyda |
dc.contributor.author |
Thorgeirsson, Thorgeir |
dc.contributor.author |
Ellingsen, Lotta María |
dc.contributor.author |
Walters, G. Bragi |
dc.contributor.author |
Gudbjartsson, Daniel |
dc.contributor.author |
Stefansson, Hreinn |
dc.contributor.author |
Stefansson, Kari |
dc.contributor.author |
Ulfarsson, Magnus |
dc.date.accessioned |
2020-02-10T11:04:46Z |
dc.date.available |
2020-02-10T11:04:46Z |
dc.date.issued |
2019-11-27 |
dc.identifier.citation |
Jonsson, B.A., Bjornsdottir, G., Thorgeirsson, T.E. et al. Brain age prediction using deep learning uncovers associated sequence variants. Nat Commun 10, 5409 (2019). https://doi.org/10.1038/s41467-019-13163-9 |
dc.identifier.issn |
2041-1723 |
dc.identifier.uri |
https://hdl.handle.net/20.500.11815/1517 |
dc.description |
Publisher's version (útgefin grein). |
dc.description.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). |
dc.description.sponsorship |
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). |
dc.format.extent |
5409 |
dc.language.iso |
en |
dc.publisher |
Springer Science and Business Media LLC |
dc.relation |
info:eu-repo/grantAgreement/EC/FP7/HEALTH-2013-602891-2 |
dc.relation.ispartofseries |
Nature Communications;10(1) |
dc.rights |
info:eu-repo/semantics/openAccess |
dc.subject |
Heilinn |
dc.subject |
Öldrun |
dc.subject |
Erfðarannsóknir |
dc.title |
Brain age prediction using deep learning uncovers associated sequence variants |
dc.type |
info:eu-repo/semantics/article |
dcterms.license |
Open Access. This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative
Commons license, and indicate if changes were made. The images or other third party
material in this article are included in the article’s Creative Commons license, unless
indicated otherwise in a credit line to the material. If material is not included in the
article’s Creative Commons license and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from
the copyright holder. To view a copy of this license, visit http://creativecommons.org/
licenses/by/4.0/. |
dc.description.version |
Peer Reviewed |
dc.identifier.journal |
Nature Communications |
dc.identifier.doi |
10.1038/s41467-019-13163-9 |