Abstract
Brain age estimation from Magnetic Resonance Images (MRI) derives the difference between a subject’s biological brain age and their chronological age. This is a potential biomarker for neurodegeneration, e.g. as part of Alzheimer’s disease. Early detection of neurodegeneration manifesting as a higher brain age can potentially facilitate better medical care and planning for affected individuals. Many studies have been proposed for the prediction of chronological age from brain MRI using machine learning and specifically deep learning techniques. Contrary to most studies, which use the whole brain volume, in this study, we develop a new deep learning approach that uses 3D patches of the brain as well as convolutional neural networks (CNNs) to develop a localised brain age estimator. In this way, we can obtain a visualization of the regions that play the most important role for estimating brain age, leading to more anatomically driven and interpretable results, and thus confirming relevant literature which suggests that the ventricles and the hippocampus are the areas that are most informative. In addition, we leverage this knowledge in order to improve the overall performance on the task of age estimation by combining the results of different patches using an ensemble method, such as averaging or linear regression. The network is trained on the UK Biobank dataset and the method achieves state-of-the-art results with a Mean Absolute Error of 2.46 years for purely regional estimates, and 2.13 years for an ensemble of patches before bias correction, while 1.96 years after bias correction.
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References
Alfaro-Almagro, F., et al.: Image processing and quality control for the first 10,000 brain imaging datasets from UK Biobank. Neuroimage 166, 400–424 (2018)
Alzheimer’s Association: 2019 Alzheimer’s disease facts and figures includes a special report on Alzheimer’s detection in the primary care setting: connecting patients and physicians. Technical report (2019). https://www.alz.org/media/Documents/alzheimers-facts-and-figures-2019-r.pdf
Becker, B.G., Klein, T., Wachinger, C., Initiative, A.D.N., et al.: Gaussian process uncertainty in age estimation as a measure of brain abnormality. Neuroimage 175, 246–258 (2018)
Beheshti, I., Nugent, S., Potvin, O., Duchesne, S.: Bias-adjustment in neuroimaging-based brain age frameworks: a robust scheme. Neuroimage Clin. 24, 102063 (2019)
Cole, J.H.: Multi-modality neuroimaging brain-age in UK Biobank: relationship to biomedical, lifestyle and cognitive factors. bioRxiv, p. 812982 (2019)
Cole, J.H., Franke, K.: Predicting age using neuroimaging: innovative brain ageing biomarkers. Trends Neurosci. 40(12), 681–690 (2017)
Cole, J.H., et al.: Brain age predicts mortality. Mol. Psychiatry 23(5), 1385–1392 (2018)
Coupé, P., Catheline, G., Lanuza, E., Manjón, J.V., Initiative, A.D.N.: Towards a unified analysis of brain maturation and aging across the entire lifespan: a MRI analysis. Hum. Brain Map. 38(11), 5501–5518 (2017)
Davatzikos, C., Bhatt, P., Shaw, L.M., Batmanghelich, K.N., Trojanowski, J.Q.: Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification. Neurobiol. Aging 32(12), 2322.e19–2322.e27 (2011)
Franke, K., Gaser, C.: Ten years of brainage as a neuroimaging biomarker of brain aging: what insights have we gained? Front. Neurol. 10, 789 (2019)
Franke, K., Ziegler, G., Kloppel, S., Gaser, C., Initiative, A.D.N., et al.: Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: exploring the influence of various parameters. Neuroimage 50(3), 883–892 (2010)
Good, C.D., Johnsrude, I.S., Ashburner, J., Henson, R.N., Friston, K.J., Frackowiak, R.S.: A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage 14(1), 21–36 (2001)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Huang, T.W., et al.: Age estimation from brain MRI images using deep learning. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp. 849–852. IEEE (2017)
Jonsson, B.A., Bjornsdottir, G., Thorgeirsson, T., Ellingsen, L.M., Walters, G.B., Gudbjartsson, D., Stefansson, H., Stefansson, K., Ulfarsson, M.: Brain age prediction using deep learning uncovers associated sequence variants. Nature Commun. 10(1), 1–10 (2019)
Juntu, J., Sijbers, J., Van Dyck, D., Gielen, J.: Bias field correction for MRI images. In: Computer Recognition Systems, pp. 543–551. Springer. https://doi.org/10.1007/3-540-32390-2_64 (2005)
Keihaninejad, S., et al.: Classification and lateralization of temporal lobe epilepsies with and without hippocampal atrophy based on whole-brain automatic MRI segmentation. PLoS ONE 7(4), e33096 (2012)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Kolbeinsson, A., et al.: Robust deep networks with randomized tensor regression layers. arXiv preprint arXiv:1902.10758 (2019)
Kondo, C., et al.: An age estimation method using brain local features for T1 weighted images. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 666–669. IEEE (2015)
de Lange, A.M.G., Cole, J.H.: Commentary: correction procedures in brain-age prediction. Neuroimage Clinical 26, 102229 (2020)
de Lange, A.M.G., et al.: Population-based neuroimaging reveals traces of childbirth in the maternal brain. Proc. Natl. Acad. Sci. 116(44), 22341–22346 (2019)
Le, T.T., et al.: A nonlinear simulation framework supports adjusting for age when analyzing brainage. Front. Aging Neurosci. 10, 317 (2018)
Lian, C., Liu, M., Zhang, J., Shen, D.: Hierarchical fully convolutional network for joint atrophy localization and Alzheimer’s disease diagnosis using structural MRI. IEEE Trans. Pattern Anal. Mach. Intell. 42(4), 880–893 (2018)
Liem, F., et al.: Predicting brain-age from multimodal imaging data captures cognitive impairment. Neuroimage 148, 179–188 (2017)
Liu, M., Zhang, J., Adeli, E., Shen, D.: Landmark-based deep multi-instance learning for brain disease diagnosis. Med. Image Anal. 43, 157–168 (2018)
Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, pp. 8024–8035 (2019)
Pawlowski, N., Glocker, B.: Is texture predictive for age and sex in brain MRI? arXiv preprint arXiv:1907.10961 (2019)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. Technical report California University San Diego La Jolla Inst for Cognitive Science (1985)
Savva, G.M., Wharton, S.B., Ince, P.G., Forster, G., Matthews, F.E., Brayne, C.: Age, neuropathology, and dementia. N. Engl. J. Med. 360(22), 2302–2309 (2009)
Smith, S.M., Vidaurre, D., Alfaro-Almagro, F., Nichols, T.E., Miller, K.L.: Estimation of brain age delta from brain imaging. Neuroimage 200, 528–539 (2019)
Tohka, J., Moradi, E., Huttunen, H., Initiative, A.D.N., et al.: Comparison of feature selection techniques in machine learning for anatomical brain MRI in dementia. Neuroinformatics 14(3), 279–296 (2016)
Acknowledgements
KMB would like to acknowledge funding from the EPSRC Centre for Doctoral Training in Medical Imaging (EP/L015226/1).
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Bintsi, KM., Baltatzis, V., Kolbeinsson, A., Hammers, A., Rueckert, D. (2020). Patch-Based Brain Age Estimation from MR Images. In: Kia, S.M., et al. Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology. MLCN RNO-AI 2020 2020. Lecture Notes in Computer Science(), vol 12449. Springer, Cham. https://doi.org/10.1007/978-3-030-66843-3_10
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