Tensor-Based Morphometry (TBM) is an increasingly popular method for group analysis of brain MRI and DTI data. The main steps in the analysis consist of a nonlinear registration to align each individual scan to a common space, and a subsequent statistical analysis to determine morphometric differences, or difference in fiber structure between groups. Here, we propose a method to improve both the nonlinear registration and statistical analyses for TBM. The traditional nonlinear registration for TBM is performed on T1-weighted MR images, either on the seg- mented 2D cortices alone, or on the whole 3D brain images, followed by corresponding statistical analyses on those domains. To date, neither option provides a satisfactory solution for the entire brain, since 2D cortical TBM ignores the rest of the brain, while 3D volumetric TBM has difficulty matching the cortex and may not match well neuronal fiber structures in the white matter. Here we describe a new statistical nonlinear registration algorithm for 3D volumetric TBM that combines the advantages of cortical matching to those of a 3D statistical fluid registration on the whole brain volume. In addition, we aim to match the underlying fiber structure accurately by adding a distance between diffusion tensors in the cost function derived from diffusion tensor imaging data. Furthermore, we propose to improve the detection power in the statistical analysis in TBM by using all the information available in the Jacobian of the deformation field in a multivariate fashion, and by setting up the inference so that it can be interpreted in terms of both volumetric changes and directions of deformation.

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We improve on Tensor-Based Morphometry for group analysis in two ways, first by using cortical, structural MR and DTI information into a combined cortical and statistical fluid registration algorithm, and secondly by using multivariate statistical methods to analyze the full Jacobian matrix and the volumetric and directional information in it.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Exploratory/Developmental Grants (R21)
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Special Emphasis Panel (ZRG1-SBIB-J (80))
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Pai, Vinay Manjunath
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Children's Hospital of Los Angeles
Los Angeles
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Scherrer, Benoit; Schwartzman, Armin; Taquet, Maxime et al. (2016) Characterizing brain tissue by assessment of the distribution of anisotropic microstructural environments in diffusion-compartment imaging (DIAMOND). Magn Reson Med 76:963-77
Schwartzman, Armin (2016) Lognormal Distributions and Geometric Averages of Symmetric Positive Definite Matrices. Int Stat Rev 84:456-486
Lao, Yi; Wang, Yalin; Shi, Jie et al. (2016) Thalamic alterations in preterm neonates and their relation to ventral striatum disturbances revealed by a combined shape and pose analysis. Brain Struct Funct 221:487-506
Chai, Yaqiong; Coloigner, Julie; Qu, Xiaoping et al. (2015) Tract specific analysis in patients with sickle cell disease. Proc SPIE Int Soc Opt Eng 9681:
Shi, Jie; Collignon, Olivier; Xu, Liang et al. (2015) Impact of Early and Late Visual Deprivation on the Structure of the Corpus Callosum: A Study Combining Thickness Profile with Surface Tensor-Based Morphometry. Neuroinformatics 13:321-336
Tsao, Sinchai; Gajawelli, Niharika; Zhou, Jiayu et al. (2014) Evaluating the Predictive Power of Multivariate Tensor-based Morphometry in Alzheimers Disease Progression via Convex Fused Sparse Group Lasso. Proc SPIE Int Soc Opt Eng 9034:90342L
Tsao, Sinchai; Ma, Samantha J; Michels, Peter A et al. (2014) The Power of Hybrid/Fusion Imaging Metrics in Future PACS Systems: A Case Study into the White Matter Hyperintensity Prenumbra using FLAIR and Diffusion MR. Proc SPIE Int Soc Opt Eng 9039:90390I
Shi, Jie; Leporé, Natasha; Gutman, Boris A et al. (2014) Genetic influence of apolipoprotein E4 genotype on hippocampal morphometry: An N = 725 surface-based Alzheimer's disease neuroimaging initiative study. Hum Brain Mapp 35:3903-18
Wilkins, Bryce; Lee, Namgyun; Rajagopalan, Vidya et al. (2014) Effect of Data Acquisition and Analysis Method on Fiber Orientation Estimation in Diffusion MRI. Comput Diffus MRI Brain Connect (2013) 2013:13-24
Tsao, Sinchai; Gajawelli, Niharika; Hwang, Darryl Hwa et al. (2014) Mapping of ApoE4 Related White Matter Damage using Diffusion MRI. Proc SPIE Int Soc Opt Eng 9039:90390H

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