Brain age prediction using deep learning uncovers associated sequence variants

Nat Commun. 2019 Nov 27;10(1):5409. doi: 10.1038/s41467-019-13163-9.

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: [Formula: see text], replication set: [Formula: see text]) yielded two sequence variants, rs1452628-T ([Formula: see text], [Formula: see text]) and rs2435204-G ([Formula: see text], [Formula: see text]). 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).

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Aged, 80 and over
  • Aging*
  • Brain / diagnostic imaging*
  • Brain / physiology*
  • Databases, Factual
  • Deep Learning*
  • Genome-Wide Association Study
  • Humans
  • Iceland
  • Magnetic Resonance Imaging
  • Middle Aged
  • Neural Networks, Computer
  • Neuropsychological Tests
  • Polymorphism, Single Nucleotide
  • United Kingdom
  • Young Adult