Statistical shape analysis of the corpus callosum in Schizophrenia
Laboratory of Neuro Imaging, University of California, Los Angeles, CA 90095-7334, USA. Electronic address: . NeuroImage
(Impact Factor: 6.36).
09/2012; 64(1):547–559. DOI: 10.1016/j.neuroimage.2012.09.024
We present a statistical shape-analysis framework for characterizing and comparing morphological variation of the corpus callosum. The midsagittal boundary of the corpus callosum is represented by a closed curve and analyzed using an invariant shape representation. The shape space of callosal curves is endowed with a Riemannian metric. Shape distances are given by the length of shortest paths (geodesics) that are invariant to shape-confounding transformations. The statistical framework enables computation of shape averages and covariances on the shape space in an intrinsic manner (unique to the shape space). The statistical framework makes use of the tangent principal component approach to achieve dimension reduction on the space of corpus callosum shapes. The advantages of this approach are — it is fully automatic, invariant, and avoids the use of landmarks to define shapes.We applied our method to determine the effects of sex, age, schizophrenia and schizophrenia-related genetic liability on callosal shape in a large sample of patients and controls and their first-degree relatives (N = 218). Results showed significant age, sex, and schizophrenia effects on both global and local callosal shape structure.
Available from: Olivier Collignon
- "Additionally, its functional differentiation along an elongated sagittal axis has allowed researchers to focus on 2D analyses of the mid-sagittal section. The structural MRI based CC structure has been used to study a variety of human development and diseases including Autism (Vidal et al. 2006; Tepest et al. 2010), Schizophrenia (Joshi et al. 2013; Adamson et al. 2011), Huntington's disease (Di Paola et al. 2012) and others. Starting from our prior work on volumetric Laplace-Beltrami operator and mTBM (Wang et al. 2004a, 2010), here we show that we may integrate two different sets of shape features efficiently for 3D CC structural analysis. "
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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
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.
Neuroinformatics 02/2015; 13(3). DOI:10.1007/s12021-014-9259-9 · 2.83 Impact Factor
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ABSTRACT: Groupwise statistical analysis of the morphometry of brain structures plays an important role in neuroimaging studies. Nevertheless, most morphometric measurements are often limited to volume and surface area, as further morphological characterization of anatomical structures poses a significant challenge. In this paper, we present a method that allows the detection, localization, and quantification of statistically significant morphological differences in complex brain structures between populations. This is accomplished by a novel level-set framework for shape morphing and a multishape dissimilarity-measure derived by a modified version of the Hausdorff distance. The proposed method does not require explicit one-to-one point correspondences and is fast, robust, and easy to implement regardless of the topological complexity of the anatomical surface under study. The proposed model has been applied to well-defined regions of interest using both synthetic and real data sets. This includes the corpus callosum, striatum, caudate, amygdala-hippocampal complex, and superior temporal gyrus. These structures were selected for their importance with respect to brain regions implicated in a variety of neurological disorders. The synthetic databases allowed quantitative evaluations of the method. Results obtained with real clinical data of Williams syndrome and schizophrenia patients agree with published findings in the psychiatry literature.
SIAM Journal on Imaging Sciences 08/2014; 7(3):1645-1668. DOI:10.1137/13093978X · 2.27 Impact Factor
Available from: Valerie Burke DeLeon
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ABSTRACT: Neuroanatomic phenotypes are often assessed using volumetric analysis. Although powerful and versatile, this approach is limited in that it is unable to quantify changes in shape, to describe how regions are interrelated, or to determine whether changes in size are global or local. Statistical shape analysis using coordinate data from biologically relevant landmarks is the preferred method for testing these aspects of phenotype. To date, approximately fifty landmarks have been used to study brain shape. Of the studies that have used landmark-based statistical shape analysis of the brain, most have not published protocols for landmark identification or the results of reliability studies on these landmarks. The primary aims of this study were two-fold: (1) to collaboratively develop detailed data collection protocols for a set of brain landmarks, and (2) to complete an intra- and inter-observer validation study of the set of landmarks. Detailed protocols were developed for 29 cortical and subcortical landmarks using a sample of 10 boys aged 12 years old. Average intra-observer error for the final set of landmarks was 1.9 mm with a range of 0.72 mm-5.6 mm. Average inter-observer error was 1.1 mm with a range of 0.40 mm-3.4 mm. This study successfully establishes landmark protocols with a minimal level of error that can be used by other researchers in the assessment of neuroanatomic phenotypes.
PLoS ONE 01/2014; 9(1):e86005. DOI:10.1371/journal.pone.0086005 · 3.23 Impact Factor
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