Boosting in Nonlinear Regression Models with an Application to DCE-MRI Data.

BACKGROUND: For the statistical analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data, compartment models are a commonly used tool. By these models, the observed uptake of contrast agent in some tissue over time is linked to physiologic properties like capillary permeability and blood flow. Up to now, models of different complexity have been used, and it is still unclear which model should be used in which situation. In previous studies, it has been found that for DCE-MRI data, the number of compartments differs for different types of tissue, and that in cancerous tissue, it might actually differ over a region of voxels of one DCE-MR image. OBJECTIVES: To find the appropriate number of compartments and estimate the parameters of a regression model for each voxel in an DCE-MR image. With that, tumors in an DCE-MR image can be located, and for example therapy success can be assessed. METHODS: The observed uptake of contrast agent in a voxel of an image of some tissue is described by a concentration time curve. This curve can be modeled using a nonlinear regression model. We present a boosting approach with nonlinear regression as base procedure, which allows us to estimate the number of compartments and the related parameters for each voxel of an DCE-MR image. In addition, a spatially regularized version of this approach is proposed. RESULTS: With the proposed approach, the number of compartments - and with that the complexity of the model - per voxel is not fixed but data-driven, which allows us to fit models of adequate complexity to the concentration time curves of all voxels. The parameters of the model remain nevertheless interpretable because of the underlying compartment model. CONCLUSIONS: The proposed boosting approaches outperform all competing methods considered in this paper regarding the correct localization of tumors in DCE-MR images as well as the spatial homogeneity of the estimated number of compartments across the image, and the definition of the tumor edge.

Image analysis and statistical inference in neuroimaging with R.

R is a language and environment for statistical computing and graphics. It can be considered an alternative implementation of the S language developed in the 1970s and 1980s for data analysis and graphics (Becker and Chambers, 1984; Becker et al., 1988). The R language is part of the GNU project and offers versions that compile and run on almost every major operating system currently available. We highlight several R packages built specifically for the analysis of neuroimaging data in the context of functional MRI, diffusion tensor imaging, and dynamic contrast-enhanced MRI. We review their methodology and give an overview of their capabilities for neuroimaging. In addition we summarize some of the current activities in the area of neuroimaging software development in R.

Selective disruption of cadherin/catenin complexes by oxidative stress in precision-cut mouse liver slices.

Previous work has shown that chemically induced oxidative stress disrupts the protein interactions of the E-cadherin/beta-catenin/alpha-catenin complex in precision-cut mouse liver slices (Parrish et al., 1999, Toxicol. Sci. 51, 80-86). Although these data suggest a role for oxidative stress in disruption of hepatic cadherin/catenin complexes, multiple complexes are co-expressed in the liver. Both E- and N- cadherin are co-expressed in hepatocytes, as well as beta-catenin and gamma-catenin; thus four distinct complexes mediate cell-cell adhesion in the liver: E-cadherin/beta-catenin/alpha-catenin, E-cadherin/gamma-catenin/alpha-catenin, N-cadherin/beta-catenin/alpha-catenin, and N-cadherin/gamma-catenin/alpha-catenin. Taking advantage of the retention of normal organ architecture and cellular heterogeneity offered by precision-cut mouse liver slices, the current study was designed to examine the impact of chemically induced oxidative stress on cadherin/catenin complexes. Precision-cut mouse liver slices were challenged with diamide (25-250 microM; 6 h) or tert-butylhydroperoxide (5-50 microM; 6 h). A polyclonal antibody against beta- or gamma-catenin was used to immunoprecipitate proteins prior to Western-blot analysis with monoclonal antibodies to E- or N-cadherin. Although a decrease in E-cadherin:beta-catenin co-immunoprecipitation was seen, interactions between beta-catenin and N-cadherin were not disrupted by chemical challenge. In addition, no effect on protein interactions of gamma-catenin with either cadherin was observed. Indirect immunofluorescence was used to co-localize catenins and cadherins following chemical challenge. Consistent with the biochemical observations, a heterogeneous reduction in co-localization of E-cadherin and beta-catenin was seen in precision-cut liver slices, but not other cadherin/catenin complexes. Taken together, these data suggest that oxidative stress selectively disrupts E-cadherin/beta-catenin complexes in the liver. This response is dictated, in part, by the protein composition of the cell-adhesion complex.