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A Visual Analytics System for Brain Functional Connectivity Comparison across Individuals, Groups, and Time Points

• Takanori Fujiwara tfujiwara (at) ucdavis.edu
  University of California, Davis
• Jia-Kai Chou jkchou (at) ucdavis.edu
  University of California, Davis
• Andrew McCullough amccullough (at) ucdavis.edu
  University of California, Davis
• Charan Ranganath cranganath (at) ucdavis.edu
  University of California, Davis
• Kwan-Liu Ma ma (at) cs.ucdavis.edu
  University of California, Davis

Abstract

Neuroscientists study brain functional connectivity in order to obtain a deeper understanding of how the brain functions. Current studies are mainly based on analyzing the averaged brain connectivity of a group (or groups) due to the high complexity of the collected data in terms of dimensionality, variability, and volume. While it is more desirable for the researchers to explore the potential variability between individual subjects or groups, a data analysis solution meeting this need is absent. In this paper, we present the design and capabilities of such a visual analytics system, which enables neuroscientists to visually compare the differences of brain networks between individual subjects as well as group averages, to explore a large dataset and examine sub-groups of participants that may not have been expected a priori to be of interest, to review detailed information as needed, and to manipulate the data and views to fit their analytical needs with easy interactions. We demonstrate the utility and strengths of this system with case studies using a representative functional connectivity dataset. \