Integrative analysis of diffusion-weighted MRI and genomic data to inform treatment of glioblastoma.

Gene expression profiling from glioblastoma (GBM) patients enables characterization of cancer into subtypes that can be predictive of response to therapy. An integrative analysis of imaging and gene expression data can potentially be used to obtain novel biomarkers that are closely associated with the genetic subtype and gene signatures and thus provide a noninvasive approach to stratify GBM patients. In this retrospective study, we analyzed the expression of 12,042 genes for 558 patients from The Cancer Genome Atlas (TCGA). Among these patients, 50 patients had magnetic resonance imaging (MRI) studies including diffusion weighted (DW) MRI in The Cancer Imaging Archive (TCIA). We identified the contrast enhancing region of the tumors using the pre- and post-contrast T1-weighted MRI images and computed the apparent diffusion coefficient (ADC) histograms from the DW-MRI images. Using the gene expression data, we classified patients into four molecular subtypes, determined the number and composition of genes modules using the gap statistic, and computed gene signature scores. We used logistic regression to find significant predictors of GBM subtypes. We compared the predictors for different subtypes using Mann-Whitney U tests. We assessed detection power using area under the receiver operating characteristic (ROC) analysis. We computed Spearman correlations to determine the associations between ADC and each of the gene signatures. We performed gene enrichment analysis using Ingenuity Pathway Analysis (IPA). We adjusted all p values using the Benjamini and Hochberg method. The mean ADC was a significant predictor for the neural subtype. Neural tumors had a significantly lower mean ADC compared to non-neural tumors ([Formula: see text]), with mean ADC of [Formula: see text] and [Formula: see text] for neural and non-neural tumors, respectively. Mean ADC showed an area under the ROC of 0.75 for detecting neural tumors. We found eight gene modules in the GBM cohort. The mean ADC was significantly correlated with the gene signature related with dendritic cell maturation ([Formula: see text], [Formula: see text]). Mean ADC could be used as a biomarker of a gene signature associated with dendritic cell maturation and to assist in identifying patients with neural GBMs, known to be resistant to aggressive standard of care.

Effect of odanacatib on bone turnover markers, bone density and geometry of the spine and hip of ovariectomized monkeys: a head-to-head comparison with alendronate.

Odanacatib (ODN) is a selective and reversible Cathepsin K (CatK) inhibitor currently being developed as a once weekly treatment for osteoporosis. Here, effects of ODN compared to alendronate (ALN) on bone turnover, DXA-based areal bone mineral density (aBMD), QCT-based volumetric BMD (vBMD) and geometric parameters were studied in ovariectomized (OVX) rhesus monkeys. Treatment was initiated 10 days after ovariectomy and continued for 20 months. The study consisted of four groups: L-ODN (2 mg/kg, daily p.o.), H-ODN (8/4 mg/kg daily p.o.), ALN (15 µg/kg, twice weekly, s.c.), and VEH (vehicle, daily, p.o.). L-ODN and ALN doses were selected to approximate the clinical exposures of the ODN 50-mg and ALN 70-mg once-weekly, respectively. L-ODN and ALN effectively reduced bone resorption markers uNTx and sCTx compared to VEH. There was no additional efficacy with these markers achieved with H-ODN. Conversely, ODN displayed inversely dose-dependent reduction of bone formation markers, sP1NP and sBSAP, and L-ODN reduced formation to a lesser degree than ALN. At month 18 post-OVX, L-ODN showed robust increases in lumbar spine aBMD (11.4%, p<0.001), spine trabecular vBMD (13.7%, p<0.001), femoral neck (FN) integral (int) vBMD (9.0%, p<0.001) and sub-trochanteric proximal femur (SubTrPF) int vBMD, (6.4%, p<0.001) compared to baseline. L-ODN significantly increased FN cortical thickness (Ct.Th) and cortical bone mineral content (Ct.BMC) by 22.5% (p<0.001) and 21.8% (p<0.001), respectively, and SubTrPF Ct.Th and Ct.BMC by 10.9% (p<0.001) and 11.3% (p<0.001) respectively. Compared to ALN, L-ODN significantly increased FN Ct. BMC by 8.7% (p<0.05), and SubTrPF Ct.Th by 7.6% (p<0.05) and Ct.BMC by 6.2% (p<0.05). H-ODN showed no additional efficacy compared to L-ODN in OVX-monkeys in prevention mode. Taken together, the results from this study have demonstrated that administration of ODN at levels which approximate clinical exposure in OVX-monkeys had comparable efficacy to ALN in DXA-based aBMD and QCT-based vBMD. However, FN cortical mineral content clearly demonstrated superior efficacy of ODN versus ALN in this model of estrogen-deficient non-human primates.

Gender differences in reward-related decision processing under stress.

Recent research indicates gender differences in the impact of stress on decision behavior, but little is known about the brain mechanisms involved in these gender-specific stress effects. The current study used functional magnetic resonance imaging (fMRI) to determine whether induced stress resulted in gender-specific patterns of brain activation during a decision task involving monetary reward. Specifically, we manipulated physiological stress levels using a cold pressor task, prior to a risky decision making task. Healthy men (n = 24, 12 stressed) and women (n = 23, 11 stressed) completed the decision task after either cold pressor stress or a control task during the period of cortisol response to the cold pressor. Gender differences in behavior were present in stressed participants but not controls, such that stress led to greater reward collection and faster decision speed in males but less reward collection and slower decision speed in females. A gender-by-stress interaction was observed for the dorsal striatum and anterior insula. With cold stress, activation in these regions was increased in males but decreased in females. The findings of this study indicate that the impact of stress on reward-related decision processing differs depending on gender.

Non-Rigid Image Registration Using Gaussian Mixture Models.

Non-rigid mutual information (MI) based image registration is prone to converge to local optima due to Parzen or histogram based density estimation used in conjunction with estimation of a high dimensional deformation field. We describe an approach for non-rigid registration that uses the log-likelihood of the target image given the deformed template as a similarity metric, wherein the distribution is modeled using a Gaussian mixture model (GMM). Using GMMs reduces the density estimation step to that of estimating the parameters of the GMM, thus being more computationally efficient and requiring fewer number of samples for accurate estimation. We compare the performance of our approach (GMM-Cond) with that of MI with Parzen density estimation (Parzen-MI), on inter-subject and inter-modality (CT to MR) mouse images. Mouse image registration is challenging because of the presence of a rigid skeleton within non-rigid soft tissue, and due to major shape and posture variability in inter-subject registration. The results show that GMM-Cond has higher registration accuracy than Parzen-MI in terms of sum of squared difference in intensity and dice coefficients of overall and skeletal overlap. The GMM-Cond approach is a general approach that can be considered a semi-parametric approximation to MI based registration, and can be used an alternative to MI for high dimensional non-rigid registration.

PET image reconstruction using information theoretic anatomical priors.

We describe a nonparametric framework for incorporating information from co-registered anatomical images into positron emission tomographic (PET) image reconstruction through priors based on information theoretic similarity measures. We compare and evaluate the use of mutual information (MI) and joint entropy (JE) between feature vectors extracted from the anatomical and PET images as priors in PET reconstruction. Scale-space theory provides a framework for the analysis of images at different levels of detail, and we use this approach to define feature vectors that emphasize prominent boundaries in the anatomical and functional images, and attach less importance to detail and noise that is less likely to be correlated in the two images. Through simulations that model the best case scenario of perfect agreement between the anatomical and functional images, and a more realistic situation with a real magnetic resonance image and a PET phantom that has partial volumes and a smooth variation of intensities, we evaluate the performance of MI and JE based priors in comparison to a Gaussian quadratic prior, which does not use any anatomical information. We also apply this method to clinical brain scan data using F(18) Fallypride, a tracer that binds to dopamine receptors and therefore localizes mainly in the striatum. We present an efficient method of computing these priors and their derivatives based on fast Fourier transforms that reduce the complexity of their convolution-like expressions. Our results indicate that while sensitive to initialization and choice of hyperparameters, information theoretic priors can reconstruct images with higher contrast and superior quantitation than quadratic priors.

Information theoretic regularization in diffuse optical tomography.

Diffuse optical tomography (DOT) retrieves the spatially distributed optical characteristics of a medium from external measurements. Recovering the parameters of interest involves solving a nonlinear and highly ill-posed inverse problem. This paper examines the possibility of regularizing DOT via the introduction of a priori information from alternative high-resolution anatomical modalities, using the information theory concepts of mutual information (MI) and joint entropy (JE). Such functionals evaluate the similarity between the reconstructed optical image and the prior image while bypassing the multimodality barrier manifested as the incommensurate relation between the gray value representations of corresponding anatomical features in the two modalities. By introducing structural information, we aim to improve the spatial resolution and quantitative accuracy of the solution. We provide a thorough explanation of the theory from an imaging perspective, accompanied by preliminary results using numerical simulations. In addition we compare the performance of MI and JE. Finally, we have adopted a method for fast marginal entropy evaluation and optimization by modifying the objective function and extending it to the JE case. We demonstrate its use on an image reconstruction framework and show significant computational savings.