eprintid: 10140313 rev_number: 12 eprint_status: archive userid: 608 dir: disk0/10/14/03/13 datestamp: 2021-12-14 08:44:22 lastmod: 2021-12-14 08:44:22 status_changed: 2021-12-14 08:44:22 type: article metadata_visibility: show creators_name: Popescu, S creators_name: Glocker, B creators_name: Sharp, D creators_name: Cole, J title: Local Brain-Age: A U-Net Model ispublished: pub divisions: UCL divisions: B04 divisions: C05 divisions: F48 keywords: brain age, deep learning, dementia, U-net, voxelwise note: t © 2021 Popescu, Glocker, Sharp and Cole. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. abstract: We propose a new framework for estimating neuroimaging-derived “brain-age” at a local level within the brain, using deep learning. The local approach, contrary to existing global methods, provides spatial information on anatomical patterns of brain ageing. We trained a U-Net model using brain MRI scans from n = 3,463 healthy people (aged 18–90 years) to produce individualised 3D maps of brain-predicted age. When testing on n = 692 healthy people, we found a median (across participant) mean absolute error (within participant) of 9.5 years. Performance was more accurate (MAE around 7 years) in the prefrontal cortex and periventricular areas. We also introduce a new voxelwise method to reduce the age-bias when predicting local brain-age “gaps.” To validate local brain-age predictions, we tested the model in people with mild cognitive impairment or dementia using data from OASIS3 (n = 267). Different local brain-age patterns were evident between healthy controls and people with mild cognitive impairment or dementia, particularly in subcortical regions such as the accumbens, putamen, pallidum, hippocampus, and amygdala. Comparing groups based on mean local brain-age over regions-of-interest resulted in large effects sizes, with Cohen's d values >1.5, for example when comparing people with stable and progressive mild cognitive impairment. Our local brain-age framework has the potential to provide spatial information leading to a more mechanistic understanding of individual differences in patterns of brain ageing in health and disease. date: 2021-12-13 date_type: published publisher: Frontiers Media official_url: https://doi.org/10.3389/fnagi.2021.761954 oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 1909948 doi: 10.3389/fnagi.2021.761954 lyricists_name: Cole, James lyricists_id: JCOLE07 actors_name: Cole, James actors_id: JCOLE07 actors_role: owner full_text_status: public publication: Frontiers in Aging Neuroscience volume: 13 article_number: 761954 issn: 1663-4365 citation: Popescu, S; Glocker, B; Sharp, D; Cole, J; (2021) Local Brain-Age: A U-Net Model. Frontiers in Aging Neuroscience , 13 , Article 761954. 10.3389/fnagi.2021.761954 <https://doi.org/10.3389/fnagi.2021.761954>. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10140313/1/Popescu%202021%20Local%20Brain%20Age%20Frontiers.pdf