Abstract
Early brain maturational processes such as myelination manifest as changes in the relative appearance of white-gray matter tissue classes in MR images. Imaging modalities such as T1W (T1-Weighted) and T2W (T2-Weighted) MRI each display specific patterns of appearance change associated with distinct neurobiological components of these maturational processes. In this paper we present a framework to jointly model multimodal appearance changes across time for a longitudinal imaging dataset, resulting in quantitative assessment of the patterns of early brain maturation not yet available to clinicians. We measure appearance by quantifying contrast between white and gray matter in terms of the distance between their intensity distributions, a method demonstrated to be relatively stable to interscan variability. A multivariate nonlinear mixed effects (NLME) model is used for joint statistical modeling of this contrast measure across multiple imaging modalities. The multivariate NLME procedure considers correlations between modalities in addition to intra-modal variability. The parameters of the logistic growth function used in NLME modeling provide useful quantitative information about the timing and progression of contrast change in multimodal datasets. Inverted patterns of relative white-gray matter intensity gradient that are observable in T1W scans with respect to T2W scans are characterized by the SIR (Signal Intensity Ratio). The CONTDIR (Contrast Direction) which measures the direction of the gradient at each time point relative to that in the adult-like scan adds a directional attribute to contrast. The major contribution of this paper is a framework for joint multimodal temporal modeling of white-gray matter MRI contrast change and estimation of subject-specific and population growth trajectories. Results confirm qualitative descriptions of growth patterns in pediatric radiology studies and our new quantitative modeling scheme has the potential to advance understanding of variability of brain tissue maturation and to eventually differentiate normal from abnormal growth for early diagnosis of pathology.
This work is supported by NIH grants ACE RO1 HD 055741, Twin R01 MH070890, Conte Center MH064065, NA-MIC Roadmap U54 EB005149, CAMID NIDA DA022446-01, and the Utah Science and Technology Research (USTAR) initiative at the University of Utah. The authors thank Tom Fletcher for discussions about the use of mixed effects models.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Rutherford, M.: MRI of the Neonatal Brain. WB Saunders Co. (2002)
Barkovich, A.J.: Concepts of myelin and myelination in neuroradiology. American Journal of Neuroradiology 21(6), 1099–1109 (2000)
Van der Knaap, M.S., Valk, J.: Magnetic resonance of myelination and myelin disorders. Springer (2005)
Giedd, J.N., Blumenthal, J., Jeffries, N.O., Castellanos, F.X., Liu, H., Zijdenbos, A., Paus, T., Evans, A.C., Rapoport, J.L., et al.: Brain development during childhood and adolescence: a longitudinal mri study. Nature Neuroscience 2(10), 861–862 (1999)
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)
Sadeghi, N., Prastawa, M., Fletcher, P.T., Vachet, C., Wang, B., Gilmore, J., Gerig, G.: Multivariate modeling of longitudinal mri in early brain development with confidence measures. In: 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI), pp. 1400–1403. IEEE (2013)
Serag, A., Aljabar, P., Counsell, S., Boardman, J., Hajnal, J., Rueckert, D.: Tracking developmental changes in subcortical structures of the preterm brain using multi-modal mri. In: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 349–352. IEEE (2011)
Vardhan, A., Prastawa, M., Vachet, C., Piven, J., Gerig, G.: Characterizing growth patterns in longitudinal mri using image contrast. In: SPIE Medical Imaging, International Society for Optics and Photonics, pp. 90340D–90340D (2014)
Lindstrom, M.J., Bates, D.M.: Nonlinear mixed effects models for repeated measures data. Biometrics, pp. 673–687 (1990)
Xu, S., Styner, M., Gilmore, J., Piven, J., Gerig, G.: Multivariate nonlinear mixed model to analyze longitudinal image data: Mri study of early brain development, pp. 1–8. IEEE Computer Society, Los Alamitos (2008)
Rueckert, Daniel, Hayes, C., Studholme, Colin, Summers, Paul, Leach, Martin O., Hawkes, David J.: Non-rigid Registration of Breast MR Images Using Mutual Information. In: Wells, William M., Colchester, Alan C.F., Delp, Scott L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 1144–1152. Springer, Heidelberg (1998)
Joshi, S., Davis, B., Jomier, M., Gerig, G., et al.: Unbiased diffeomorphic atlas construction for computational anatomy. NeuroImage 23(1), S151–S160 (2004)
Van Leemput, K., Maes, F., Vandermeulen, D., Suetens, P.: Automated model-based tissue classification of mr images of the brain. IEEE Transactions on Medical Imaging 18(10), 897–908 (1999)
Kim, S.H., Fonov, V., Dietrich, C., Vachet, C., Hazlett, H.C., Smith, R.G., Graves, M., Piven, J., Gilmore, J.H., Collins, D.L., et al.: Adaptive prior probability and spatial temporal intensity change estimation for segmentation of the one-year-old human brain. Journal of Neuroscience Methods 212(1), 43–55 (2012)
Laird, N.M., Ware, J.H.: Random-effects models for longitudinal data. Biometrics, 963–974 (1982)
Pinheiro, J.C., Bates, D.M.: Mixed-effects models in S and S-PLUS. Springer (2000)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Vardhan, A., Prastawa, M., Sadeghi, N., Vachet, C., Piven, J., Gerig, G. (2015). Joint Longitudinal Modeling of Brain Appearance in Multimodal MRI for the Characterization of Early Brain Developmental Processes. In: Durrleman, S., Fletcher, T., Gerig, G., Niethammer, M., Pennec, X. (eds) Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data. STIA 2014. Lecture Notes in Computer Science(), vol 8682. Springer, Cham. https://doi.org/10.1007/978-3-319-14905-9_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-14905-9_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14904-2
Online ISBN: 978-3-319-14905-9
eBook Packages: Computer ScienceComputer Science (R0)