Generative discriminative models for multivariate inference and statistical mapping in medical imaging

This paper presents a general framework for obtaining interpretable multivariate discriminative models that allow efficient statistical inference for neuroimage analysis. The framework, termed generative discriminative machine (GDM), augments discriminative models with a generative regularization term. We demonstrate that the proposed formulation can be optimized in closed form and in dual space, allowing efficient computation for high dimensional neuroimaging datasets. Furthermore, we provide an analytic estimation of the null distribution of the model parameters, which enables efficient statistical inference and p-value computation without the need for permutation testing. We compared the proposed method with both purely generative and discriminative learning methods in two large structural magnetic resonance imaging (sMRI) datasets of Alzheimer's disease (AD) (n=415) and Schizophrenia (n=853). Using the AD dataset, we demonstrated the ability of GDM to robustly handle confounding variations. Using Schizophrenia dataset, we demonstrated the ability of GDM to handle multi-site studies. Taken together, the results underline the potential of the proposed approach for neuroimaging analyses.Comment: To appear in MICCAI 2018 proceeding

Contamination Control and Assay Results for the Majorana Demonstrator Ultra Clean Components

The MAJORANA DEMONSTRATOR is a neutrinoless double beta decay experiment utilizing enriched Ge-76 detectors in 2 separate modules inside of a common solid shield at the Sanford Underground Research Facility. The DEMONSTRATOR has utilized world leading assay sensitivities to develop clean materials and processes for producing ultra-pure copper and plastic components. This experiment is now operating, and initial data provide new insights into the success of cleaning and processing. Post production copper assays after the completion of Module 1 showed an increase in U and Th contamination in finished parts compared to starting bulk material. A revised cleaning method and additional round of surface contamination studies prior to Module 2 construction have provided evidence that more rigorous process control can reduce surface contamination. This article describes the assay results and discuss further studies to take advantage of assay capabilities for the purpose of maintaining ultra clean fabrication and process design.Comment: Proceedings of Low Radioactivity Techniques (LRT May 2017, Seoul