Imperial College London
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ProfessorDavidSharp

Faculty of Medicine, Department of Brain Sciences

Professor of Neurology
 
 
 
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Contact

 

+44 (0)20 7594 7991david.sharp Website

 
 
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Location

 

UREN.927Sir Michael Uren HubWhite City Campus

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Summary

 

Publications

Citation

BibTex format

@article{Popescu:2021:10.3389/fnagi.2021.761954,
author = {Popescu, SG and Glocker, B and Sharp, DJ and Cole, JH},
doi = {10.3389/fnagi.2021.761954},
journal = {Frontiers in Aging Neuroscience},
pages = {1--17},
title = {Local brain-age: A u-net model},
url = {http://dx.doi.org/10.3389/fnagi.2021.761954},
volume = {13},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Popescu,SG
AU  - Glocker,B
AU  - Sharp,DJ
AU  - Cole,JH
DO  - 10.3389/fnagi.2021.761954
EP  - 17
PY  - 2021///
SN  - 1663-4365
SP  - 1
TI  - Local brain-age: A u-net model
T2  - Frontiers in Aging Neuroscience
UR  - http://dx.doi.org/10.3389/fnagi.2021.761954
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000736059000001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://www.frontiersin.org/articles/10.3389/fnagi.2021.761954/full
UR  - http://hdl.handle.net/10044/1/93732
VL  - 13
ER  -
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