{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Cole JH"],"funding":["UKRI) Innovation Fellowship","Medical Research Council"],"pagination":["34-42"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7280786"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["92"],"pubmed_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."],"journal":["Neurobiology of aging"],"pubmed_title":["Multimodality neuroimaging brain-age in UK biobank: relationship to biomedical, lifestyle, and cognitive factors."],"pmcid":["PMC7280786"],"funding_grant_id":["MC_PC_17228","MR/R024790/1","MR/R024790/2","MC_QA137853","MR/R024790/1; MR/R024790/2"],"pubmed_authors":["Cole JH"],"additional_accession":[]},"is_claimable":false,"name":"Multimodality neuroimaging brain-age in UK biobank: relationship to biomedical, lifestyle, and cognitive factors.","description":"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.","dates":{"release":"2020-01-01T00:00:00Z","publication":"2020 Aug","modification":"2025-04-05T15:20:51.94Z","creation":"2025-04-05T15:20:51.94Z"},"accession":"S-EPMC7280786","cross_references":{"pubmed":["32380363"],"doi":["10.1016/j.neurobiolaging.2020.03.014"]}}