Multivariate tensor-based morphometry on surfaces: Application to mapping ventricular abnormalities in HIV/AIDS

Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA 90095-7332, USA.
NeuroImage (Impact Factor: 6.36). 11/2009; 49(3):2141-57. DOI: 10.1016/j.neuroimage.2009.10.086
Source: PubMed


Here we developed a new method, called multivariate tensor-based surface morphometry (TBM), and applied it to study lateral ventricular surface differences associated with HIV/AIDS. Using concepts from differential geometry and the theory of differential forms, we created mathematical structures known as holomorphic one-forms, to obtain an efficient and accurate conformal parameterization of the lateral ventricular surfaces in the brain. The new meshing approach also provides a natural way to register anatomical surfaces across subjects, and improves on prior methods as it handles surfaces that branch and join at complex 3D junctions. To analyze anatomical differences, we computed new statistics from the Riemannian surface metrics-these retain multivariate information on local surface geometry. We applied this framework to analyze lateral ventricular surface morphometry in 3D MRI data from 11 subjects with HIV/AIDS and 8 healthy controls. Our method detected a 3D profile of surface abnormalities even in this small sample. Multivariate statistics on the local tensors gave better effect sizes for detecting group differences, relative to other TBM-based methods including analysis of the Jacobian determinant, the largest and smallest eigenvalues of the surface metric, and the pair of eigenvalues of the Jacobian matrix. The resulting analysis pipeline may improve the power of surface-based morphometry studies of the brain.

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    • "It establishes high-order correspondences between 3D surfaces. Finally, we computed various surface statistics based on the registered surface, such as multivariate tensor-based morphometry (mTBM) statistics [Wang et al. (2010)]. 3.4. "

    Full-text · Article · Dec 2015 · The Annals of Applied Statistics
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    • "Compared with prior work (Jones et al. 2000; Adamson et al. 2011), our partial differential equation (PDE) solving computation may achieve sub-voxel accuracy. Also because surfaces are easily computed from tetrahedral meshes, our method can be easily integrated with prior surface registration work (Wang et al. 2010, 2011, 2013c). Second, we identify areas that show differences in the structure of the CC between early blinds, late blinds and sighted controls. "
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    ABSTRACT: Blindness represents a unique model to study how visual experience may shape the development of brain organization. Exploring how the structure of the corpus callosum (CC) reorganizes ensuing visual deprivation is of particular interest due to its important functional implication in vision (e.g., via the splenium of the CC). Moreover, comparing early versus late visually deprived individuals has the potential to unravel the existence of a sensitive period for reshaping the CC structure. Here, we develop a novel framework to capture a complete set of shape differences in the CC between congenitally blind (CB), late blind (LB) and sighted control (SC) groups. The CCs were manually segmented from T1-weighted brain MRI and modeled by 3D tetrahedral meshes. We statistically compared the combination of local area and thickness at each point between subject groups. Differences in area are found using surface tensor-based morphometry; thickness is estimated by tracing the streamlines in the volumetric harmonic field. Group differences were assessed on this combined measure using Hotelling’s T 2 test. Interestingly, we observed that the total callosal volume did not differ between the groups. However, our fine-grained analysis reveals significant differences mostly localized around the splenium areas between both blind groups and the sighted group (general effects of blindness) and, importantly, specific dissimilarities between the LB and CB groups, illustrating the existence of a sensitive period for reorganization. The new multivariate statistics also gave better effect sizes for detecting morphometric differences, relative to other statistics. They may boost statistical power for CC morphometric analyses.
    Full-text · Article · Feb 2015 · Neuroinformatics
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    • "detJ represents the difference in surface area without directional information. 3. The logged deformation tensors (log ffiffiffiffiffiffiffi JJ T p ) (Wang et al. 2011a), as in our prior mTBM analyses (Lepore et al. 2008; Wang et al. 2010a, 2011a). These tensors describe the directional differences in regional surface area between each subject and the template. "
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    ABSTRACT: Finding the neuroanatomical correlates of prematurity is vital to understanding which structures are affected, and to designing efficient prevention and treatment strategies. Converging results reveal that thalamic abnormalities are important indicators of prematurity. However, little is known about the localization of the abnormalities within the subnuclei of the thalamus, or on the association of altered thalamic development with other deep gray matter disturbances. Here, we aim to investigate the effect of prematurity on the thalamus and the putamen in the neonatal brain, and further investigate the associated abnormalities between these two structures. Using brain structural magnetic resonance imaging, we perform a novel combined shape and pose analysis of the thalamus and putamen between 17 preterm (41.12 ± 5.08 weeks) and 19 term-born (45.51 ± 5.40 weeks) neonates at term equivalent age. We also perform a set of correlation analyses between the thalamus and the putamen, based on the surface and pose results. We locate significant alterations on specific surface regions such as the anterior and ventral anterior (VA) thalamic nuclei, and significant relative pose changes of the left thalamus and the right putamen. In addition, we detect significant association between the thalamus and the putamen for both surface and pose parameters. The regions that are significantly associated include the VA, and the anterior and inferior putamen. We detect statistically significant surface deformations and pose changes on the thalamus and putamen, and for the first time, demonstrate the feasibility of using relative pose parameters as indicators for prematurity in neonates. Our methods show that regional abnormalities of the thalamus are associated with alterations of the putamen, possibly due to disturbed development of shared pre-frontal connectivity. More specifically, the significantly correlated regions in these two structures point to frontal-subcortical pathways including the dorsolateral prefrontal-subcortical circuit, the lateral orbitofrontal-subcortical circuit, the motor circuit, and the oculomotor circuit. These findings reveal new insight into potential subcortical structural covariates for poor neurodevelopmental outcomes in the preterm population.
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