Comparison of Antibodies to Detect Uroplakin in Urothelial Carcinomas

Immunohistochemistry for Uroplakin (UP) II and III is used to determine urothelial origin of carcinomas of unknown primary site and are especially valuable to differentiate urothelial carcinomas (UCs) from lung squamous cell carcinomas and prostate carcinomas. In the Nordic immunohistochemical Quality Control assessment scheme, only 45% of the participants obtained a sufficient staining result for UP. Primary antibodies (Abs) against UPII were most successful with a pass rate of 86%. No Abs against UPIII provided sufficient staining results. A comparative study was carried out on a larger cohort of tissue samples with optimized methods for the UPII mouse monoclonal antibody (mmAb) clone BC21, UPIII mmAb clone AU-1, and rabbit monoclonal antibody (rmAb) clone SP73 to evaluate the performance in a standardized way. Tissue microarrays containing 58 UCs, 111 non-UCs, and 20 normal tissues were included. The UP stains were evaluated by using H-score. Based on H-scores, samples were categorized as high-expressor (150 to 300), moderate-expressor (10 to 149), low-expressor (1 to 9), and negative (150 for the UPII Ab. The 2 UPIII Abs gave an analytical specificity of 100% compared with 97% for the UPII Ab being positive in 2 ovarian carcinomas and 1 cervical squamous cell carcinoma

Nonparametric Modeling of Dynamic Functional Connectivity in fMRI Data

Dynamic functional connectivity (FC) has in recent years become a topic of interest in the neuroimaging community. Several models and methods exist for both functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), and the results point towards the conclusion that FC exhibits dynamic changes. The existing approaches modeling dynamic connectivity have primarily been based on time-windowing the data and k-means clustering. We propose a non-parametric generative model for dynamic FC in fMRI that does not rely on specifying window lengths and number of dynamic states. Rooted in Bayesian statistical modeling we use the predictive likelihood to investigate if the model can discriminate between a motor task and rest both within and across subjects. We further investigate what drives dynamic states using the model on the entire data collated across subjects and task/rest. We find that the number of states extracted are driven by subject variability and preprocessing differences while the individual states are almost purely defined by either task or rest. This questions how we in general interpret dynamic FC and points to the need for more research on what drives dynamic FC.Comment: 8 pages, 1 figure. Presented at the Machine Learning and Interpretation in Neuroimaging Workshop (MLINI-2015), 2015 (arXiv:1605.04435

Infinite von Mises-Fisher Mixture Modeling of Whole Brain fMRI Data

Cluster analysis of functional magnetic resonance imaging (fMRI) data is often performed using gaussian mixture models, but when the time series are standardized such that the data reside on a hypersphere, this modeling assumption is questionable. The consequences of ignoring the underlying spherical manifold are rarely analyzed, in part due to the computational challenges imposed by directional statistics. In this letter, we discuss a Bayesian von Mises–Fisher (vMF) mixture model for data on the unit hypersphere and present an efficient inference procedure based on collapsed Markov chain Monte Carlo sampling. Comparing the vMF and gaussian mixture models on synthetic data, we demonstrate that the vMF model has a slight advantage inferring the true underlying clustering when compared to gaussian-based models on data generated from both a mixture of vMFs and a mixture of gaussians subsequently normalized. Thus, when performing model selection, the two models are not in agreement. Analyzing multisubject whole brain resting-state fMRI data from healthy adult subjects, we find that the vMF mixture model is considerably more reliable than the gaussian mixture model when comparing solutions across models trained on different groups of subjects, and again we find that the two models disagree on the optimal number of components. The analysis indicates that the fMRI data support more than a thousand clusters, and we confirm this is not a result of overfitting by demonstrating better prediction on data from held-out subjects. Our results highlight the utility of using directional statistics to model standardized fMRI data and demonstrate that whole brain segmentation of fMRI data requires a very large number of functional units in order to adequately account for the discernible statistical patterns in the data. </jats:p