Research Area:  Machine Learning

Abstract:

Machine learning algorithms can be trained to estimate age from brain structural MRI. The difference between an individuals 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: , replication set: ) yielded two sequence variants, rs1452628-T (, ) and rs2435204-G (, ). 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).

Keywords:  
Brain age
Deep learning
Machine learning
MRIs
White matter

Author(s) Name:  B. A. Jonsson, G. Bjornsdottir, T. E. Thorgeirsson, L. M. Ellingsen, G. Bragi Walters, D. F. Gudbjartsson, H. Stefansson, K. Stefansson

Publisher name:  Springer

DOI:  10.1038/s41467-019-13163-9

Volume Information:  Volume 10

Paper Link:   https://www.nature.com/articles/s41467-019-13163-9