UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker

Cole, JH; Poudel, RPK; Tsagkrasoulis, D; Caan, MWA; Steves, C; Spector, TD; Montana, G; (2017) Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. NeuroImage , 163 pp. 115-124. 10.1016/j.neuroimage.2017.07.059. Green open access

[thumbnail of Cole 2017 NeuroImage_accepted_version.pdf]
Preview
Text
Cole 2017 NeuroImage_accepted_version.pdf - Accepted Version

Download (3MB) | Preview

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. Firstly, we aimed to demonstrate the accuracy of CNN brain-predicted age using a large dataset of healthy adults (N = 2001). Next, we sought to establish the heritability of brain-predicted age using a sample of monozygotic and dizygotic female twins (N = 62). Thirdly, we examined the test-retest and multi-centre reliability of brain-predicted age using two samples (within-scanner N = 20; between-scanner N = 11). CNN brain-predicted ages were generated and compared to a Gaussian Process Regression (GPR) approach, on all datasets. Input data were grey matter (GM) or white matter (WM) volumetric maps generated by Statistical Parametric Mapping (SPM) or raw data. CNN accurately predicted chronological age using GM (correlation between brain-predicted age and chronological age r = 0.96, mean absolute error [MAE] = 4.16 years) and raw (r = 0.94, MAE = 4.65 years) data. This was comparable to GPR brain-predicted age using GM data (r = 0.95, MAE = 4.66 years). Brain-predicted age was a heritable phenotype for all models and input data (h2 ≥ 0.5). Brain-predicted age showed high test-retest reliability (intraclass correlation coefficient [ICC] = 0.90–0.99). Multi-centre reliability was more variable within high ICCs for GM (0.83–0.96) and poor-moderate levels for WM and raw data (0.51–0.77). Brain-predicted age represents an accurate, highly reliable and genetically-influenced phenotype, that has potential to be used as a biomarker of brain ageing. Moreover, age predictions can be accurately generated on raw T1-MRI data, substantially reducing computation time for novel data, bringing the process closer to giving real-time information on brain health in clinical settings.

Type: Article
Title: Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.neuroimage.2017.07.059
Publisher version: https://doi.org/10.1016/j.neuroimage.2017.07.059
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Science & Technology, Life Sciences & Biomedicine, Neurosciences, Neuroimaging, Radiology, Nuclear Medicine & Medical Imaging, Neurosciences & Neurology, Brain ageing, Reliability, Heritability, Biomarker, Deep learning, Convolutional neural networks, Gaussian processes, RELIABILITY, VOLUMES, MRI, REGISTRATION, ATROPHY, SEGMENTATION, GENETICS, MOTION, TWIN
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10104523
Downloads since deposit
378Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item