Joint Longitudinal Modeling of Brain Appearance in Multimodal MRI for the Characterization of Early Brain Developmental Processes - Longitudinal Registration and Shape Modeling - page 46

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

B

Avantika Vardhan 1(

) , Marcel Prastawa 2 , Neda Sadeghi 1 , Clement Vachet 1 ,
Joseph Piven 3 ,andGuidoGerig 1

1 SCI Institute, University of Utah, Salt Lake City, UT, USA
[e-mail]
2 GE Global Research, New York, USA
3 Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA

Abstract. Early brain maturational processes such as myelination man-
ifest 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 appear-
ance 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 multi-
variate nonlinear mixed effects (NLME) model is used for joint statis-
tical modeling of this contrast measure across multiple imaging modal-
ities. 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 quanti-
tative 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 CON-
TDIR (Contrast Direction) which measures the direction of the gradient
at each time point relative to that in the adult-like scan adds a direc-
tional 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

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.