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Multivariate Modeling of Longitudinal MRI in Early Brain Development with Confidence Measures

1Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA.
2Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA.
ISBI 2013
Publication Date:
Proc IEEE Int Symp Biomed Imaging.
Proc IEEE Int Symp Biomed Imaging. 2013 Apr; 1400-3.
PubMed ID:
Appears in Collections:
R01 MH070890/MH/NIMH NIH HHS/United States
P50 MH064065/MH/NIMH NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Utah Science Technology and Research (USTAR) initiative at the University of Utah
Generated Citation:
Sadeghi N., Prastawa M.W., Fletcher P.T., Vachet C., Wang B., Gilmore J., Gerig G. Multivariate Modeling of Longitudinal MRI in Early Brain Development with Confidence Measures. Proc IEEE Int Symp Biomed Imaging. 2013 Apr; 1400-3. PMID: 23959506. PMCID: PMC3744330.
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The human brain undergoes rapid organization and structuring early in life. Longitudinal imaging enables the study of these changes over a developmental period within individuals through estimation of population growth trajectory and its variability. In this paper, we focus on maturation of white and gray matter depicted in structural and diffusion MRI of healthy subjects with repeated scans. We provide a framework for joint analysis of both structural MRI and DTI (Diffusion Tensor Imaging) using multivariate nonlinear mixed effect modeling of temporal changes. Our framework constructs normative growth models for all the modalities, taking into account the correlation among the modalities and individuals, along with estimation of the variability of the population trends. We apply our method to study early brain development, and to our knowledge this is the first multimodel longitudinal modeling of diffusion and signal intensity changes for this growth stage. Results show the potential of our framework to study growth trajectories, as well as neurodevelopmental disorders through comparison against the constructed normative models of multimodal 4D MRI.

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