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

Cole, JH; (2020) Multi-modality neuroimaging brain-age in UK Biobank: relationship to biomedical, lifestyle and cognitive factors. Neurobiology of Aging 10.1016/j.neurobiolaging.2020.03.014. Green open access

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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=0.78, 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 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.

Type: Article
Title: Multi-modality neuroimaging brain-age in UK Biobank: relationship to biomedical, lifestyle and cognitive factors
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.neurobiolaging.2020.03.014
Publisher version: https://doi.org/10.1016/j.neurobiolaging.2020.03.0...
Language: English
Additional information: This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
Keywords: Brain aging; Neuroimaging; Multi-modality MRI; UK Biobank; Biomedical measures
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10096341
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