Joint Longitudinal Modeling of Brain Appearance in Multimodal MRI for the Characterization of Early Brain Developmental Processes

  • Avantika Vardhan
  • Marcel Prastawa
  • Neda Sadeghi
  • Clement Vachet
  • Joseph Piven
  • Guido Gerig
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8682)


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.


Signal Intensity Ratio Hellinger Distance Appearance Change Contrast Change Logistic Growth Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Rutherford, M.: MRI of the Neonatal Brain. WB Saunders Co. (2002)Google Scholar
  2. 2.
    Barkovich, A.J.: Concepts of myelin and myelination in neuroradiology. American Journal of Neuroradiology 21(6), 1099–1109 (2000)Google Scholar
  3. 3.
    Van der Knaap, M.S., Valk, J.: Magnetic resonance of myelination and myelin disorders. Springer (2005)Google Scholar
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    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)Google Scholar
  9. 9.
    Lindstrom, M.J., Bates, D.M.: Nonlinear mixed effects models for repeated measures data. Biometrics, pp. 673–687 (1990)Google Scholar
  10. 10.
    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)Google Scholar
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    Joshi, S., Davis, B., Jomier, M., Gerig, G., et al.: Unbiased diffeomorphic atlas construction for computational anatomy. NeuroImage 23(1), S151–S160 (2004)CrossRefGoogle Scholar
  13. 13.
    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)CrossRefGoogle Scholar
  14. 14.
    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)CrossRefGoogle Scholar
  15. 15.
    Laird, N.M., Ware, J.H.: Random-effects models for longitudinal data. Biometrics, 963–974 (1982)Google Scholar
  16. 16.
    Pinheiro, J.C., Bates, D.M.: Mixed-effects models in S and S-PLUS. Springer (2000)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Avantika Vardhan
    • 1
  • Marcel Prastawa
    • 2
  • Neda Sadeghi
    • 1
  • Clement Vachet
    • 1
  • Joseph Piven
    • 3
  • Guido Gerig
    • 1
  1. 1.SCI InstituteUniversity of UtahSalt Lake CityUSA
  2. 2.GE Global ResearchNew YorkUSA
  3. 3.Department of PsychiatryUniversity of North CarolinaChapel HillUSA

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