1. nView Overview
We developed this tool as part of a team of visualization and biomedical researchers to better understand the physiology of DBS and patient outcome.
nuView is being actively developed simultaneously with the development of the probabilistic atlas model and VTA simluation studies, allowing results from the simulation to be explored within nuView, and the insights gleaned from nuView to be incorporated back into the DBS model.
While our results to date are still in the experimental phase, we have already had some success within this collaboration.

2. nView DBS Visualization
Goal: Visualize the variance in clinical outcomes (such as tremor arrest) that can be attributed to deep brain
stimulation (DBS) lead location or stimulation location
Associated with the ECG simulation and experimental studies mentioned in the last slide, there is a
need to visualize and analyze uncertainties of these simulations and experiments.
Through a recent collaboration, we created mu-View, which is a software framework for uncertainty visualization and analysis.
Shown here are examples of mu-View to visualize results from a simulation study
of the effects of electrical conductivity uncertainty forward and inverse electrocardiographic
Simulations.
Here we show the multi-window linked view of μView.
- A three-dimensional view,
Multiple two-dimensional views,
A Feature space view using principal component analysis on the PDFs, and
a Parallel coordinates view.
Mu-View is in the early stages of research and development, but we are excited about the possibilities of allowing our
DBP partners to more effectively visualize and analyze uncertainty from their simulations and experiments.
During the poster and demo session, you will see a poster describing mu-View.
The data points are colored categorically using k-means clustering with L2-norm distance metric.
Development can be followed at
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3. mView Visualization Framework
Associated with the ECG simulation and experimental studies mentioned in the last slide, there is a
need to visualize and analyze uncertainties of these simulations and experiments.
Through a recent collaboration, we created mu-View, which is a software framework for uncertainty visualization and analysis.
Shown here are examples of mu-View to visualize results from a simulation study
of the effects of electrical conductivity uncertainty forward and inverse electrocardiographic
Simulations.
Here we show the multi-window linked view of μView.
- A three-dimensional view,
Multiple two-dimensional views,
A Feature space view using principal component analysis on the PDFs, and
a Parallel coordinates view.
Mu-View is in the early stages of research and development, but we are excited about the possibilities of allowing our
DBP partners to more effectively visualize and analyze uncertainty from their simulations and experiments.
During the poster and demo session, you will see a poster describing mu-View.
The data points are colored categorically using k-means clustering with L2-norm distance metric.
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4. Question from Reviewers
μView has a number of visualization approaches, many of which highlight the same features. Each visualization has its own advantages and disadvantages. Clustering has the advantage of highlighting multiple features simultaneously. However, it requires significant effort in visual search to wade through less important features. Using the isovalue visualization limits the number of features visible, making concentration easier, but requiring additional interaction. It is increasingly important to find visualizations that balance these modes of operation and identify which types of visualizations are most efficient from the perspectives of speed, accuracy, and cognitive load. Providing users with choice in visualization is valuable, but too much choice will overwhelm.
Our visual and cognitive channels can be overwhelmed with too much data.
User interfaces depend on the level of expertise of the user.
Optimizing visual interfaces in a challenging research question.

5. DBP: Okun/Foote
Long-Term Goal: Visualize the variance in clinical outcomes
(such as tremor arrest) that can be attributed to deep brain
stimulation (DBS) lead location or stimulation location
DBS Lead Locations for 60 Parkinson’s Disease Patients Among 8 Centers
DBS Lead Locations
Colored by Center
Let me now turn to Visualization challenges in the Okun/Foote DBP.
The science: In the Sim-Est TRD presentation, Dana described how we are using computational models to predict the effects of DBS. In the Vis TRD we will test those predictions and use them to quantify and visualize uncertainty in clinical outcomes.
In the slide, we show DBS lead locations for 60 Parkinson’s disease patients from 8 different centers. Obviously, there is significant variability among the lead locations.
The visualization breakthrough: Our challenge is to create visualizations that intuitively convey this uncertainty and variability information to our clinical colleagues.
The future: Our goal is to create visualizations and associated analysis tools to help our clinical colleagues to focus on DBS settings that are most strongly correlated with therapeutic improvement.
Butson, Foote, Okun et al (2014) Movement Disorders Society Congress 5