Multi-modality neuroimaging brain-age in UK Biobank: relationship to biomedical, lifestyle and cognitive factors

Abstract

The brain-age paradigm is proving increasingly useful for exploring ageing-related disease and can predict important future health outcomes. Most brain-age research utilises structural neuroimaging to index brain volume. However, ageing affects multiple aspects of brain structure and function, which can be examined using multi-modality neuroimaging. Using UK Biobank, brain-age was modelled in n=2,205 healthy people with T1-weighted MRI, T2-FLAIR, T2*, diffusion-MRI, task fMRI and resting-state fMRI. In a held-out healthy validation set (n=520), chronological age was accurately predicted (r=0.79, mean absolute error=3.52 years) using LASSO regression, higher than using any modality separately. Thirty-four neuroimaging phenotypes were deemed informative by the regression (after bootstrapping); predominantly grey-matter volume and white-matter microstructure measures. When applied to new individuals from UK Biobank (n=14,701), significant associations with multi-modality brain-predicted age difference (brain-PAD) were found for: stroke history, diabetes diagnosis, smoking, alcohol intake and some, but not all, cognitive measures (corrected p<0.05). Multi-modality neuroimaging can improve brain-age prediction, and derived brain-PAD values are sensitive to biomedical and lifestyle factors that negatively impact brain and cognitive health.