Neurosurgery Network Pattern Analysis with 2nd Generation Ensembles

Evolving measuring and computing capabilities, along with increasingly complex problems or models, are resulting in a new type of dataset: second-generation ensemble data. Like first-generation ensemble data, these data consist of large-scale, repeated measurements of the same process or phenomenon, and often have a spatial component. Unlike older datasets, they typically require the extraction and aggregation of novel complex features, which may be generated through direct measurements rather than simulations, and appear in a wider range of application domains, including neuroscience. We describe an interactive visual analysis solution for this type of second-generation ensemble data, related to the study and planning of surgical interventions in epilepsy treatment. As part of this solution, we introduce a dynamic community abstraction in conjunction with analysis algorithms for feature extraction and aggregation, registration techniques to correlate and project sample data, and custom visual encodings to support the analysis of conserved network patterns. A quantitative and qualitative evaluation with domain experts at four sites demonstrates the effectiveness of this solution. We discuss this approach and solution in the context of second-generation ensemble data analysis, along with the challenges of working with this type of data.