Surface fluid registration of conformal representation: Application to detect disease burden and genetic influence on hippocampus

School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
NeuroImage (Impact Factor: 6.36). 04/2013; 78:111-134. DOI: 10.1016/j.neuroimage.2013.04.018


In this paper, we develop a new automated surface registration system based on surface conformal parameterization by holomorphic 1-forms, inverse consistent surface fluid registration, and multivariate tensor-based morphometry (mTBM). First, we conformally map a surface onto a planar rectangle space with holomorphic 1-forms. Second, we compute surface conformal representation by combining its local conformal factor and mean curvature and linearly scale the dynamic range of the conformal representation to form the feature image of the surface. Third, we align the feature image with a chosen template image via the fluid image registration algorithm, which has been extended into the curvilinear coordinates to adjust for the distortion introduced by surface parameterization. The inverse consistent image registration algorithm is also incorporated in the system to jointly estimate the forward and inverse transformations between the study and template images. This alignment induces a corresponding deformation on the surface. We tested the system on Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline dataset to study AD symptoms on hippocampus. In our system, by modeling a hippocampus as a 3D parametric surface, we nonlinearly registered each surface with a selected template surface. Then we used mTBM to analyze the morphometry difference between diagnostic groups. Experimental results show that the new system has better performance than two publicly available subcortical surface registration tools: FIRST and SPHARM. We also analyzed the genetic influence of the Apolipoprotein E∈4 allele (ApoE4), which is considered as the most prevalent risk factor for AD. Our work successfully detected statistically significant difference between ApoE4 carriers and non-carriers in both patients of mild cognitive impairment (MCI) and healthy control subjects. The results show evidence that the ApoE genotype may be associated with accelerated brain atrophy so that our work provides a new MRI analysis tool that may help presymptomatic AD research.

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    • "Hippocampal surfaces were constructed with a topology-preserving level set method based on the binary segmentations (Han et al., 2003) and triangular surface meshes were obtained based on the marching cubes algorithm (Lorensen and Cline, 1987). After mesh refinement (Shi et al., 2013a), we obtained smooth surfaces that are suitable for generating conformal grids as shown in Figure 1b. Finally each of the smoothed meshes was aligned into the MNI standard space using a global affine transformation with a nine-parameter (three parameters for translation, three parameters for rotation, and three parameters for scaling) matrix that was computed by FIRST (Patenaude et al., 2011). "
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    ABSTRACT: The apolipoprotein E (APOE) e4 allele is the most prevalent genetic risk factor for Alzheimer's disease (AD). Hippocampal volumes are generally smaller in AD patients carrying the e4 allele compared to e4 noncarriers. Here we examined the effect of APOE e4 on hippocampal morphometry in a large imaging database—the Alzheimer's Disease Neuroimaging Initiative (ADNI). We automatically segmented and constructed hippocampal surfaces from the baseline MR images of 725 subjects with known APOE genotype information including 167 with AD, 354 with mild cognitive impairment (MCI), and 204 normal controls. High-order correspondences between hippocampal surfaces were enforced across subjects with a novel inverse consistent surface fluid registration method. Multivariate statistics consisting of multivariate tensor-based morphometry (mTBM) and radial distance were computed for surface deformation analysis. Using Hotelling's T2 test, we found significant morphological deformation in APOE e4 carriers relative to noncarriers in the entire cohort as well as in the nondemented (pooled MCI and control) subjects, affecting the left hippocampus more than the right, and this effect was more pronounced in e4 homozygotes than heterozygotes. Our findings are consistent with previous studies that showed e4 carriers exhibit accelerated hippocampal atrophy; we extend these findings to a novel measure of hippocampal morphometry. Hippocampal morphometry has significant potential as an imaging biomarker of early stage AD. Hum Brain Mapp, 2014. © 2014 Wiley Periodicals, Inc.
    Full-text · Article · Aug 2014 · Human Brain Mapping
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    • "We aim to detect surface-based regional differences in shape between the two groups. Our method involves conformal grid generation on the surface [31], surface fluid registration [22] and a multivariate tensor-based morphometry statistical analysis [33]; it has been shown to increase detection power in our neonatal studies [35], [38], and in adults [33]. We focus exclusively on preterm neonates with no visible evidence of white matter injury as determined by an expert neuroradiologist, in order to determine whether subtle brain injuries are still present in those subjects, and whether our pipeline is sensitive enough to detect smaller changes. "
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    ABSTRACT: Many disorders that affect the brain can cause shape changes in subcortical structures, and these may provide biomarkers for disease detection and progression. Automatic tools are needed to accurately identify and characterize these alterations. In recent work, we developed a surface multivariate tensor-based morphometry analysis (mTBM) to detect morphological group differences in subcortical structures, and we applied this method to study HIV/AIDS, William's syndrome, Alzheimer's disease and prematurity. Here we will focus more specifically on mTBM in neonates, which, in its current form, starts with manually segmented subcortical structures from MRI images of a two subject groups, places a conformal grid on each of their surfaces, registers them to a template through a constrained harmonic map and provides statistical comparisons between the two groups, at each vertex of the template grid. We improve this pipeline in two ways: first by replacing the constrained harmonic map with a new fluid registration algorithm that we recently developed. Secondly, by optimizing the pipeline to study the putamen in newborns. Our analysis is applied to the comparison of the putamen in premature and term born neonates. Recent whole-brain volumetric studies have detected differences in this structure in babies born preterm. Here we add to the literature on this topic by zooming in on this structure, and by generating the first surface-based maps of these changes. To do so, we use a dataset of manually segmented putamens from T1-weighted brain MR images from 17 preterm and 18 term-born neonates. Statistical comparisons between the two groups are performed via four methods: univariate and multivariate tensor-based morphometry, the commonly used medial axis distance, and a combination of the last two statistics. We detect widespread statistically significant differences in morphology between the two groups that are consistent across statistics, but more extensive for multivariate m- asures.
    Full-text · Article · Jan 2012
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    ABSTRACT: Recently, some studies have applied the graph theory in brain network analysis in Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI). However, relatively little research has specifically explored the properties of the metabolic network in apolipoprotein E (APOE) ε4 allele carriers. In our study, all the subjects, including ADs, MCIs and NCs (normal controls) were divided into 165 APOE ε4 carriers and 165 APOE ε4 noncarriers. To establish the metabolic network for all brain regions except the cerebellum, cerebral glucose metabolism data obtained from FDG-PET (18F-fluorodeoxyglu-cose positron emission tomography) were segmented into 90 areas with automated anatomical labeling (AAL) template. Then, the properties of the networks were computed to explore the between-group differences. Our results suggested that both APOE ε4 carriers and noncarriers showed the small-world properties. Besides, compared with APOE ε4 noncarriers, the carriers showed a lower clustering coefficient. In addition, significant changes in 6 hub brain regions were found in between-group nodal centrality. Namely, compared with APOE ε4 noncarriers, significant decreases of the nodal centrality were found in left insula, right insula, right anterior cingulate, right paracingulate gyri, left cuneus, as well as significant increases in left paracentral lobule and left heschl gyrus in APOE ε4 carriers. Increased local short distance interregional correlations and disrupted long distance interregional correlations were found, which may support the point that the APOE ε4 carriers were more similar with AD or MCI in FDG uptake. In summary, the organization of metabolic network in APOE ε4 carriers indicated a less optimal pattern and APOE ε4 might be a risk factor for AD.
    Full-text · Article · Jul 2015 · PLoS ONE
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