Abstract
The term prediction implies expected outcome in the future, often based on a model and statistical inference. Longitudinal imaging studies offer the possibility to model temporal change trajectories of anatomy across populations of subjects. In the spirit of subject-specific analysis, such normative models can then be used to compare data from new subjects to the norm and to study progression of disease or to predict outcome. This paper follows a statistical inference approach and presents a framework for prediction of future observations based on past measurements and population statistics. We describe prediction in the context of nonlinear mixed effects modeling (NLME) where the full reference population’s statistics (estimated fixed effects, variance-covariance of random effects, variance of noise) is used along with the individual’s available observations to predict its trajectory. The proposed methodology is generic in regard to application domains. Here, we demonstrate analysis of early infant brain maturation from longitudinal DTI with up to three time points. Growth as observed in DTI-derived scalar invariants is modeled with a parametric function, its parameters being input to NLME population modeling. Trajectories of new subject’s data are estimated when using no observation, only the first or the first two time points. Leave-one-out experiments result in statistics on differences between actual and predicted observations. We also simulate a clinical scenario of prediction on multiple categories, where trajectories predicted from multiple models are classified based on maximum likelihood criteria.
Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Dubois, J., Dehaene-Lambertz, G., Perrin, M., Mangin, J.F., Cointepas, Y., Duchesnay, E., Le Bihan, D., Hertz-Pannier, L.: Asynchrony of the early maturation of white matter bundles in healthy infants: quantitative landmarks revealed noninvasively by diffusion tensor imaging. Hum Brain Mapp. 29, 14–27 (2008)
Giedd, J.N., Snell, J.W., Lange, N., Rajapakse, J.C., Casey, B.J., Kozuch, P.L., Vaituzis, A.C., Vauss, Y.C., Hamburger, S.D., Kaysen, D., Rapoport, J.L.: Quantitative magnetic resonance imaging of human brain development: ages 4-18. Cereb. Cortex 6(4), 551–560 (1996)
Kraemer, H.C., Yesavage, J.A., Taylor, J.L., Kupfer, D.: How can we learn about developmental processes from cross-sectional studies, or can we? Am. J. Psychiatry 157(2), 163–171 (2000)
Lindstrom, M.L., Bates, D.M.: Nonlinear mixed effects models for repeated measures data. Biometrics 46, 673–687 (1990)
Pinheiro, J.C., Bates, D.M.: Mixed-Effects Models in S and S-Plus. Springer (2000)
Sadeghi, N., Prastawa, M., Fletcher, P.T., Wolff, J., Gilmore, J.H., Gerig, G.: Regional characterization of longitudinal DT-MRI to study white matter maturation of the early developing brain. Neuroimage 68, 236–247 (2013)
Akaike, H.: A new look at the statistical model identification. IEEE Transactions on Automatic Control 19(6), 716–723 (1974)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Sadeghi, N., Fletcher, P.T., Prastawa, M., Gilmore, J.H., Gerig, G. (2014). Subject-Specific Prediction Using Nonlinear Population Modeling: Application to Early Brain Maturation from DTI. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. MICCAI 2014. Lecture Notes in Computer Science, vol 8675. Springer, Cham. https://doi.org/10.1007/978-3-319-10443-0_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-10443-0_5
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10442-3
Online ISBN: 978-3-319-10443-0
eBook Packages: Computer ScienceComputer Science (R0)