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2017 IEEE Pacific Visualization Symposium (PacificVis) (2017)
Seoul, South Korea
April 18, 2017 to April 21, 2017
ISSN: 2165-8773
ISBN: 978-1-5090-5739-9
pp: 250-259
Takanori Fujiwara , University of California, Davis, United States of America
Jia-Kai Chou , University of California, Davis, United States of America
Andrew M. McCullough , University of California, Davis, United States of America
Charan Ranganath , University of California, Davis, United States of America
Kwan-Liu Ma , University of California, Davis, United States of America
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.
INDEX TERMS
Data visualization, Correlation, Tools, Stress, Complexity theory, Symmetric matrices, Data analysis,I.3.8 [Computer Graphics]: Applications,
CITATION
Takanori Fujiwara, Jia-Kai Chou, Andrew M. McCullough, Charan Ranganath, Kwan-Liu Ma, "A visual analytics system for brain functional connectivity comparison across individuals, groups, and time points", 2017 IEEE Pacific Visualization Symposium (PacificVis), vol. 00, no. , pp. 250-259, 2017, doi:10.1109/PACIFICVIS.2017.8031601
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