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Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker
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Cole, James H., Poudel, Rudra P. K., Tsagkrasoulis, Dimosthenis, Caan, Matthan W. A., Steves, Claire, Spector, Tim D. and Montana, Giovanni (2017) Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. NeuroImage, 163 . pp. 115-124. doi:10.1016/j.neuroimage.2017.07.059 ISSN 1053-8119.
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Official URL: http://dx.doi.org/10.1016/j.neuroimage.2017.07.059
Abstract
Machine learning analysis of neuroimaging data can accurately predict chronological age in healthy people. Deviations from healthy brain ageing have been associated with cognitive impairment and disease. Here we sought to further establish the credentials of ‘brain-predicted age’ as a biomarker of individual differences in the brain ageing process, using a predictive modelling approach based on deep learning, and specifically convolutional neural networks (CNN), and applied to both pre-processed and raw T1-weighted MRI data.
| Item Type: | Journal Article | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||
| Journal or Publication Title: | NeuroImage | ||||||||
| Publisher: | Elsevier | ||||||||
| ISSN: | 1053-8119 | ||||||||
| Official Date: | December 2017 | ||||||||
| Dates: |
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| Volume: | 163 | ||||||||
| Page Range: | pp. 115-124 | ||||||||
| DOI: | 10.1016/j.neuroimage.2017.07.059 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
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