Deep brain stimulation (DBS) is an established therapy for treating patients with movement disorders such as Parkinson's disease, and is being investigated for a range of other neurological and psychiatric conditions. Patient-specific computational modeling and visualization have been shown to play a key role in surgical and therapeutic decisions for DBS. The computational models use brain imaging, such as magnetic resonance (MR) and computed tomography (CT), to determine the DBS electrode position within the patient's head. The finite resolution of brain imaging, however, introduces uncertainty in electrode position. The DBS stimulation settings for optimal patient response are sensitive to the relative position of DBS electrode with regard to specific brain nuclei. In our contribution, we propose a statistical framework for studying uncertainty in electrode position derived from imaging with finite resolution. We leverage the physical dimensions of the DBS electrode and the resolution of post-operative CT imaging to quantify the probabilistic spread of the electrode contacts. We propose two techniques, namely, contact-center probability and contact-volume probability, to gain insight into the positional uncertainty of the DBS electrode. The former represents the likelihood that the voxel contains a contact center, whereas the latter represents the likelihood that the voxel contains any point within the volume of contact. We justify the choice of a uniform noise model for characterizing positional uncertainty in electrode contacts. Finally, we study the sensitivity of spatial spread of electrode contacts to the image resolution through visualization. We show that the uncertainty in electrode position can be substantial and should be considered for accurate predictions of the effects of DBS.

Modern science is inundated with ever increasing data sizes as computational capabilities and image acquisition techniques continue to improve. For example, simulations are tackling ever larger domains with higher delity, and high-throughput microscopy techniques generate larger data that are fundamental to gather biologically and medically relevant insights. As the image sizes exceed memory, and even sometimes local disk space, each step in a scientific workflow is impacted. Current software solutions enable data exploration with limited interactivity for visualization and analytic tasks. Furthermore analysis on HPC systems often require complex hand-written parallel implementations of algorithms that suffer from poor portability and maintainability.

We present a software infrastructure that simplifies end-to-end visualization and analysis of massive data. First, a hierarchical streaming data access layer enables interactive exploration of remote data, with fast data fetching to test analytics on subsets of the data. Second, a library simplifies the process of developing new analytics algorithms, allowing users to rapidly prototype new approaches and deploy them in an HPC setting. Third, a scalable runtime system automates mapping analysis algorithms to whatever computational hardware is available, reducing the complexity of developing scaling algorithms. We demonstrate the usability and performance of our system using a use case from neuroscience: filtering, registration, and visualization of tera-scale microscopy data. We evaluate the performance of our system using a leadership-class supercomputer, Shaheen II.