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@article{Kolbeinsson:2020:10.1038/s41598-020-76518-z,
author = {Kolbeinsson, A and Filippi, S and Panagakis, I and Matthews, P and Elliott, P and Dehghan, A and Tzoulaki, I},
doi = {10.1038/s41598-020-76518-z},
journal = {Scientific Reports},
title = {Accelerated MRI-predicted brain ageing and its associations with cardiometabolic and brain disorders},
url = {http://dx.doi.org/10.1038/s41598-020-76518-z},
volume = {10},
year = {2020}
}

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TY  - JOUR
AB  - Brain structure in later life reflects both influences of intrinsic aging and those of lifestyle, environment and disease. We developed a deep neural network model trained on brain MRI scans of healthy people to predict “healthy” brain age. Brain regions most informative for the prediction included the cerebellum, hippocampus, amygdala and insular cortex. We then applied this model to data from an independent group of people not stratified for health. A phenome-wide association analysis of over 1,410 traits in the UK Biobank with differences between the predicted and chronological ages for the second group identified significant associations with over 40 traits including diseases (e.g., type I and type II diabetes), disease risk factors (e.g., increased diastolic blood pressure and body mass index), and poorer cognitive function. These observations highlight relationships between brain and systemic health and have implications for understanding contributions of the latter to late life dementia risk.
AU  - Kolbeinsson,A
AU  - Filippi,S
AU  - Panagakis,I
AU  - Matthews,P
AU  - Elliott,P
AU  - Dehghan,A
AU  - Tzoulaki,I
DO  - 10.1038/s41598-020-76518-z
PY  - 2020///
SN  - 2045-2322
TI  - Accelerated MRI-predicted brain ageing and its associations with cardiometabolic and brain disorders
T2  - Scientific Reports
UR  - http://dx.doi.org/10.1038/s41598-020-76518-z
UR  - http://hdl.handle.net/10044/1/84887
VL  - 10
ER  -

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