Abstract
Brain imaging can be used to study how individuals' brains are aging, compared against population norms. This can inform on aspects of brain health; for example, smoking and blood pressure can be seen to accelerate brain aging. Typically, a single 'brain age' is estimated per subject, whereas here we identified 62 modes of subject variability, from 21,407 subjects' multimodal brain imaging data in UK Biobank. The modes represent different aspects of brain aging, showing distinct patterns of functional and structural brain change, and distinct patterns of association with genetics, lifestyle, cognition, physical measures and disease. While conventional brain-age modelling found no genetic associations, 34 modes had genetic associations. We suggest that it is important not to treat brain aging as a single homogeneous process, and that modelling of distinct patterns of structural and functional change will reveal more biologically meaningful markers of brain aging in health and disease.
Highlights
Brain imaging can be used to predict ‘brain age’ - the apparent age of individuals’ brains - by comparing their imaging data against a normative population dataset
We found a genetic association with rs10052710, a SNP in an intron of VCAN, and in high linkage disequilibrium (LD) with a previous hit we had found strongly associated with diffusion measures across the entire white matter Elliott (2018)
We investigated the modes’ distinct associations with genetics, life factors and biological body measures, in the context of the modelling of brain age and brain-age delta - a measure of whether subjects’ brains appear to be aging faster or slower than the population average
Summary
Brain imaging can be used to predict ‘brain age’ - the apparent age of individuals’ brains - by comparing their imaging data against a normative population dataset. The difference between brain age and actual chronological age (the ‘delta’, or ‘brain age gap’) is often computed, providing a measure of whether a subject’s brain appears to have aged more (or less) than the average agematched population data. Looking at structural magnetic resonance imaging (MRI) data, a high degree of atrophy would cause a subject’s brain to appear older than a normal agematched brain. The structural images may be warped into a standard space, and grey matter segmentation carried out; the voxelwise segmentation values themselves can be the features. The algorithm learns to predict the subjects’ ages from their brain imaging features.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
