Abstract
Research on modeling and exploring of the normal brain maturity, such as in vivo study of the anatomy of the developing brain, can provide references for developmental pathologies. In this paper, we model and explore brain development by learning a discriminative representation of the cortical brain data (T1 MRI) with a class-wise non-negative dictionary learning (NDDL) approach. For each class, the proposed approach performs data modeling by first projecting the data into non-negative low-rank encoding coefficients with an analysis dictionary and then applying the coefficients onto an orthogonal synthesis dictionary to reconstruct the data. It also uses additional regularizers to enforce distal classes to fit into different analysis dictionaries. The learning problem is formulated as a sparse and low rank optimization problem, and solved with an alternating direction method of multipliers(ADMM). The effectiveness of the proposed approach is tested on brain age prediction problems by exploring the cortical status, and the experiments are conducted on the PING dataset. The proposed approach produces competitive results. Further, we were able for the first time to capture the status of brain thickness of specific cortical surface area with aging.
J.-B. Poline and A. Evans—Co-last author.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends® Mach. Learn. 3(1), 1–122 (2011)
Cole, J.H., Franke, K.: Predicting age using neuroimaging: innovative brain ageing biomarkers. Trends Neurosci. 40, 681–690 (2017)
Gu, S., Zhang, L., Zuo, W., Feng, X.: Projective dictionary pair learning for pattern classification. In: Advances in Neural Information Processing Systems, pp. 793–801 (2014)
Gu, S., Zhang, L., Zuo, W., Feng, X.: Weighted nuclear norm minimization with application to image denoising. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2862–2869 (2014)
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)
Li, J., Wu, Y., Zhao, J., Lu, K.: Low-rank discriminant embedding for multiview learning. IEEE Trans. Cybern. 47(11), 3516–3529 (2017)
Sowell, E.R., Trauner, D.A., Gamst, A., Jernigan, T.L.: Development of cortical and subcortical brain structures in childhood and adolescence: a structural MRI study. Dev. Med. Child Neurol. 44(1), 4–16 (2002)
Tong, T., et al.: A novel grading biomarker for the prediction of conversion from mild cognitive impairment to Alzheimer’s disease. IEEE Trans. Biomed. Eng. 64(1), 155–165 (2017)
Walhovd, K.B., et al.: Long-term influence of normal variation in neonatal characteristics on human brain development. Proc. Nat. Acad. Sci. 109(49), 20089–20094 (2012)
Zhang, M., Desrosiers, C.: High-quality image restoration using low-rank patch regularization and global structure sparsity. IEEE Trans. Image Process. 28(2), 868–879 (2019)
Zhang, M., Desrosiers, C., Guo, Y., Zhang, C., Khundrakpam, B., Evans, A.: Brain status prediction with non-negative projective dictionary learning. In: Shi, Y., Suk, H.-I., Liu, M. (eds.) MLMI 2018. LNCS, vol. 11046, pp. 152–160. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00919-9_18
Acknowledgements
This work was supported, in part, by the FRQS Quebec (CCC 246110, 271636), Brain Canada/HBHL/Exp (247858), National Nature Science Foundation of China (NSFC: 61902220, 61602277, U1609218), CONP RSA(201802), NIH-NIBIB P41 EB019936 (ReproNim) NIH-NIMH R01 MH083320 (CANDIShare) and NIH 5U24 DA039832 (NIF), as well as the Healthy Brains for Healthy Lives (HBHL) initiative.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, M., Guo, Y., Zhang, C., Poline, JB., Evans, A. (2019). Modeling and Analysis Brain Development via Discriminative Dictionary Learning. In: Knoll, F., Maier, A., Rueckert, D., Ye, J. (eds) Machine Learning for Medical Image Reconstruction. MLMIR 2019. Lecture Notes in Computer Science(), vol 11905. Springer, Cham. https://doi.org/10.1007/978-3-030-33843-5_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-33843-5_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33842-8
Online ISBN: 978-3-030-33843-5
eBook Packages: Computer ScienceComputer Science (R0)