A BAYESIAN HIERARCHICAL FRAMEWORK FOR SPATIAL MODELING OF fMRI DATA

Functional neuroimaging techniques enable investigations into the neural basis of human cognition, emotions, and behaviors. In practice, applications of functional magnetic resonance imaging (fMRI) have provided novel insights into the neuropathophysiology of major psychiatric,neurological, and substance abuse disorders, as well as into the neural responses to their treatments. Modern activation studies often compare localized task-induced changes in brain activity between experimental groups. One may also extend voxel-level analyses by simultaneously considering the ensemble of voxels constituting an anatomically defined region of interest (ROI) or by considering means or quantiles of the ROI. In this work we present a Bayesian extension of voxel-level analyses that offers several notable benefits. First, it combines whole-brain voxel-by-voxel modeling and ROI analyses within a unified framework. Secondly, an unstructured variance/covariance for regional mean parameters allows for the study of inter-regional functional connectivity, provided enough subjects are available to allow for accurate estimation. Finally, an exchangeable correlation structure within regions allows for the consideration of intra-regional functional connectivity. We perform estimation for our model using Markov Chain Monte Carlo (MCMC) techniques implemented via Gibbs sampling which, despite the high throughput nature of the data, can be executed quickly (less than 30 minutes). We apply our Bayesian hierarchical model to two novel fMRI data sets: one considering inhibitory control in cocaine-dependent men and the second considering verbal memory in subjects at high risk for Alzheimer’s disease. The unifying hierarchical model presented in this manuscript is shown to enhance the interpretation content of these data sets.

RANDOM EFFECTS MODELS IN A META-ANALYSIS OF THE ACCURACY OF DIAGNOSTIC TESTS WITHIN A GOLD STANDARD IN THE PRESENCE OF MISSING DATA

In evaluating the accuracy of diagnosis tests, it is common to apply two imperfect tests jointly or sequentially to a study population. In a recent meta-analysis of the accuracy of microsatellite instability testing (MSI) and traditional mutation analysis (MUT) in predicting germline mutations of the mismatch repair (MMR) genes, a Bayesian approach (Chen, Watson, and Parmigiani 2005) was proposed to handle missing data resulting from partial testing and the lack of a gold standard. In this paper, we demonstrate an improved estimation of the sensitivities and specificities of MSI and MUT by using a nonlinear mixed model and a Bayesian hierarchical model, both of which account for the heterogeneity across studies through study-specific random effects. The methods can be used to estimate the accuracy of two imperfect diagnostic tests in other meta-analyses when the prevalence of disease, the sensitivities and/or the specificities of diagnostic tests are heterogeneous among studies. Furthermore, simulation studies have demonstrated the importance of carefully selecting appropriate random effects on the estimation of diagnostic accuracy measurements in this scenario.

Hardware accelerated ray cast of volume data and volume gradient for an optimized splines-based multi-resolution 2D-3D registration

This paper describes a method for DRR generation as well as for volume gradients projection using hardware accelerated 2D texture mapping and accumulation buffering and demonstrates its application in 2D-3D registration of X-ray fluoroscopy to CT images. The robustness of the present registration scheme are guaranteed by taking advantage of a coarse-to-fine processing of the volume/image pyramids based on cubic B-splines. A human cadaveric spine specimen together with its ground truth was used to compare the present scheme with a purely software-based scheme in three aspects: accuracy, speed, and capture ranges. Our experiments revealed an equivalent accuracy and capture ranges but with much shorter registration time with the present scheme. More specifically, the results showed 0.8 mm average target registration error, 55 second average execution time per registration, and 10 mm and 10° capture ranges for the present scheme when tested on a 3.0 GHz Pentium 4 computer.

The exchange of radiotherapy data as part of an electronic patient-referral system

PURPOSE: To describe the implementation and use of an electronic patient-referral system as an aid to the efficient referral of patients to a remote and specialized treatment center. METHODS AND MATERIALS: A system for the exchange of radiotherapy data between different commercial planning systems and a specially developed planning system for proton therapy has been developed through the use of the PAPYRUS diagnostic image standard as an intermediate format. To ensure the cooperation of the different TPS manufacturers, the number of data sets defined for transfer has been restricted to the three core data sets of CT, VOIs, and three-dimensional dose distributions. As a complement to the exchange of data, network-wide application-sharing (video-conferencing) technologies have been adopted to provide methods for the interactive discussion and assessment of treatments plans with one or more partner clinics. RESULTS: Through the use of evaluation plans based on the exchanged data, referring clinics can accurately assess the advantages offered by proton therapy on a patient-by-patient basis, while the practicality or otherwise of the proposed treatments can simultaneously be assessed by the proton therapy center. Such a system, along with the interactive capabilities provided by video-conferencing methods, has been found to be an efficient solution to the problem of patient assessment and selection at a specialized treatment center, and is a necessary first step toward the full electronic integration of such centers with their remotely situated referral centers.

Interaction between rheumatoid arthritis and pregnancy: correlation of molecular data with clinical disease activity measures

OBJECTIVE: The factors that induce remission of RA during pregnancy and the relapse occurring after delivery remain an enigma. In a previous study, we investigated gene-expression profiles of peripheral blood mononuclear cells (PBMC) in patients with RA and healthy women in late pregnancy and postpartum. Profiles of samples from both groups were similar in late pregnancy with elevated monocyte and decreased lymphocyte signatures. Postpartum, in RA PBMC the high level of monocyte transcripts persisted. Further increase was observed in adhesion, migration and signalling processes related to monocytes but also in lymphocytes despite similar clinical activity due to intensified drug treatment. This prompted us to investigate correlations between clinical parameters of disease activity and gene profiles. METHODS: Transcriptome data were correlated with RADAI, CRP, monocyte and lymphocyte counts. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotations, monocytes and lymphocytes signatures were used as reference information. RESULTS: Comparative analysis of PBMC expression profiles from RA patients during and after pregnancy with RADAI and CRP revealed a correlation of these disease activity parameters predominantly with monocyte transcripts. Genes related to cellular programs of adhesion, migration and response to infections were upregulated. Comparing clinically active and not-active RA patients postpartum revealed a cluster of 19 genes that could also identify active disease during pregnancy. CONCLUSION: The data suggest that an increase of the RADAI and an elevation of CRP is a consequence of molecular activation of monocytes. Furthermore, they indicate that molecular activation of T lymphocytes may remain clinically unrecognized postpartum. It is conceivable that a set of 19 genes may qualify as molecular disease activity marker.

Investigation of the effect of using data collection technology on students' attitudes to science instruction

The purpose of this project was to investigate the effect of using of data collection technology on student attitudes towards science instruction. The study was conducted over the course of two years at Madison High School in Adrian, Michigan, primarily in college preparatory physics classes, but also in one college preparatory chemistry class and one environmental science class. A preliminary study was conducted at a Lenawee County Intermediate Schools student summer environmental science day camp. The data collection technology used was a combination of Texas Instruments TI-84 Silver Plus graphing calculators and Vernier LabPro data collection sleds with various probeware attachments, including motion sensors, pH probes and accelerometers. Students were given written procedures for most laboratory activities and were provided with data tables and analysis questions to answer about the activities. The first year of the study included a pretest and posttest measuring student attitudes towards the class they were enrolled in. Pre-test and post-test data were analyzed to determine effect size, which was found to be very small (Coe, 2002). The second year of the study focused only on a physics class and used Keller’s ARCS model for measuring student motivation based on the four aspects of motivation: Attention, Relevance, Confidence and Satisfaction (Keller, 2010). According to this model, it was found that there were two distinct groups in the class, one of which was motivated to learn and the other that was not. The data suggest that the use of data collection technology in science classes should be started early in a student’s career, possibly in early middle school or late elementary. This would build familiarity with the equipment and allow for greater exploration by the student as they progress through high school and into upper level science courses.

Systemic analysis of microarray data sets towards identifying genes regulating root development

Nitrogen and water are essential for plant growth and development. In this study, we designed experiments to produce gene expression data of poplar roots under nitrogen starvation and water deprivation conditions. We found low concentration of nitrogen led first to increased root elongation followed by lateral root proliferation and eventually increased root biomass. To identify genes regulating root growth and development under nitrogen starvation and water deprivation, we designed a series of data analysis procedures, through which, we have successfully identified biologically important genes. Differentially Expressed Genes (DEGs) analysis identified the genes that are differentially expressed under nitrogen starvation or drought. Protein domain enrichment analysis identified enriched themes (in same domains) that are highly interactive during the treatment. Gene Ontology (GO) enrichment analysis allowed us to identify biological process changed during nitrogen starvation. Based on the above analyses, we examined the local Gene Regulatory Network (GRN) and identified a number of transcription factors. After testing, one of them is a high hierarchically ranked transcription factor that affects root growth under nitrogen starvation. It is very tedious and time-consuming to analyze gene expression data. To avoid doing analysis manually, we attempt to automate a computational pipeline that now can be used for identification of DEGs and protein domain analysis in a single run. It is implemented in scripts of Perl and R.

Determination of carbohydrate-deficient transferrin in human serum by two capillary zone electrophoresis methods and a direct immunoassay: comparison of patient data

Data obtained with two CZE assays for determining carbohydrate-deficient transferrin (CDT) in human serum under routine conditions, the CAPILLARYS CDT and the high-resolution CEofix (HR-CEofix) CDT methods, are in agreement with patient sera that do not exhibit interferences, high trisialo-transferrin (Tf) levels or genetic variants. HR-CEofix CDT levels are somewhat higher compared to those obtained with the CAPILLARYS method and this bias corresponds to the difference of the upper reference values of the two assays. The lower resolution between disialo-Tf and trisialo-Tf observed in the CAPILLARYS system (mean: 1.24) compared to HR-CEofix (mean: 1.74) is believed to be the key for this difference. For critical sera with high trisialo-Tf levels, genetic variants, or certain interferences in the beta-region, the HR-CEofix approach is demonstrated to perform better than CAPILLARYS. However, the determination of CDT with the HR-CEofix method can also be hampered with interferences. Results with disialo-Tf values larger than 3% in the absence of asialo-Tf should be evaluated with immunosubtraction of Tf and possibly also confirmed with another CZE method or by HPLC. Furthermore, data gathered with the N Latex CDT direct immunonephelometric assay suggest that this assay can be used for screening purposes. To reduce the number of false negative results, CDT data above 2.0% should be confirmed using a separation method.

Stochastic rank correlation: a robust merit function for 2D/3D registration of image data obtained at different energies

In this article, the authors evaluate a merit function for 2D/3D registration called stochastic rank correlation (SRC). SRC is characterized by the fact that differences in image intensity do not influence the registration result; it therefore combines the numerical advantages of cross correlation (CC)-type merit functions with the flexibility of mutual-information-type merit functions. The basic idea is that registration is achieved on a random subset of the image, which allows for an efficient computation of Spearman's rank correlation coefficient. This measure is, by nature, invariant to monotonic intensity transforms in the images under comparison, which renders it an ideal solution for intramodal images acquired at different energy levels as encountered in intrafractional kV imaging in image-guided radiotherapy. Initial evaluation was undertaken using a 2D/3D registration reference image dataset of a cadaver spine. Even with no radiometric calibration, SRC shows a significant improvement in robustness and stability compared to CC. Pattern intensity, another merit function that was evaluated for comparison, gave rather poor results due to its limited convergence range. The time required for SRC with 5% image content compares well to the other merit functions; increasing the image content does not significantly influence the algorithm accuracy. The authors conclude that SRC is a promising measure for 2D/3D registration in IGRT and image-guided therapy in general.

Multi-Mode Tensor Representation of Motion Data

In this paper, we investigate how a multilinear model can be used to represent human motion data. Based on technical modes (referring to degrees of freedom and number of frames) and natural modes that typically appear in the context of a motion capture session (referring to actor, style, and repetition), the motion data is encoded in form of a high-order tensor. This tensor is then reduced by using N-mode singular value decomposition. Our experiments show that the reduced model approximates the original motion better then previously introduced PCA-based approaches. Furthermore, we discuss how the tensor representation may be used as a valuable tool for the synthesis of new motions.