| Title: | Multimodality neuroimaging brain-age in UK biobank: relationship to biomedical, lifestyle, and cognitive factors |
| Journal: | Neurobiology of Aging |
| Published: | 8 Apr 2020 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/32380363/ |
| DOI: | https://doi.org/10.1016/j.neurobiolaging.2020.03.014 |
| Citations: | 251 (117 in last 2 years) as of 8 Aug 2024 |
Publication 5426
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Abstract
The brain-age paradigm is proving increasingly useful for exploring aging-related disease and can predict important future health outcomes. Most brain-age research uses structural neuroimaging to index brain volume. However, aging affects multiple aspects of brain structure and function, which can be examined using multimodality neuroimaging. Using UK Biobank, brain-age was modeled in n = 2205 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 = 0.78, mean absolute error = 3.55 years) using LASSO regression, higher than using any modality separately. Thirty-four neuroimaging phenotypes were deemed informative by the regression (after bootstrapping); predominantly gray-matter volume and white-matter microstructure measures. When applied to new individuals from UK Biobank (n = 14,701), significant associations with multimodality 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). Multimodality 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.</p>
1 Author
- James H. Cole
Enabling scientific discoveries that improve human health