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Neuroimage. 2019 Oct 15;200:528-539. doi: 10.1016/j.neuroimage.2019.06.017. Epub 2019 Jun 12.

Estimation of brain age delta from brain imaging.

Author information

1
Wellcome Trust Centre for Integrative Neuroimaging (WIN-FMRIB), University of Oxford, Oxford, UK. Electronic address: steve@fmrib.ox.ac.uk.
2
Wellcome Trust Centre for Integrative Neuroimaging (WIN-OHBA), University of Oxford, Oxford, UK.
3
Wellcome Trust Centre for Integrative Neuroimaging (WIN-FMRIB), University of Oxford, Oxford, UK.
4
Big Data Institute, University of Oxford, Oxford, UK; Wellcome Trust Centre for Integrative Neuroimaging (WIN-FMRIB), University of Oxford, Oxford, UK.

Abstract

It is of increasing interest to study "brain age" - the apparent age of a subject, as inferred from brain imaging data. The difference between brain age and actual age (the "delta") is typically computed, reflecting deviation from the population norm. This therefore may reflect accelerated aging (positive delta) or resilience (negative delta) and has been found to be a useful correlate with factors such as disease and cognitive decline. However, although there has been a range of methods proposed for estimating brain age, there has been little study of the optimal ways of computing the delta. In this technical note we describe problems with the most common current approach, and present potential improvements. We evaluate different estimation methods on simulated and real data. We also find the strongest correlations of corrected brain age delta with 5,792 non-imaging variables (non-brain physical measures, life-factor measures, cognitive test scores, etc.), and also with 2,641 multimodal brain imaging-derived phenotypes, with data from 19,000 participants in UK Biobank.

KEYWORDS:

Brain aging; Brain imaging; UK biobank

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