A visual analytics system for brain functional connectivity comparison across individuals, groups, and time points

Takanori Fujiwara, Jia Kai Chou, Andrew M. McCullough, Charan Ranganath, Kwan-Liu Ma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

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.

Original languageEnglish (US)
Title of host publication2017 IEEE Pacific Visualization Symposium, PacificVis 2017 - Proceedings
EditorsYingcai Wu, Daniel Weiskopf, Tim Dwyer
PublisherIEEE Computer Society
Pages250-259
Number of pages10
ISBN (Electronic)9781509057382
DOIs
StatePublished - Sep 11 2017
Event10th IEEE Pacific Visualization Symposium, PacificVis 2017 - Seoul, Korea, Republic of
Duration: Apr 18 2017Apr 21 2017

Other

Other10th IEEE Pacific Visualization Symposium, PacificVis 2017
CountryKorea, Republic of
CitySeoul
Period4/18/174/21/17

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Keywords

  • Applications
  • I.3.8 [Computer Graphics]

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Software

Cite this

Fujiwara, T., Chou, J. K., McCullough, A. M., Ranganath, C., & Ma, K-L. (2017). A visual analytics system for brain functional connectivity comparison across individuals, groups, and time points. In Y. Wu, D. Weiskopf, & T. Dwyer (Eds.), 2017 IEEE Pacific Visualization Symposium, PacificVis 2017 - Proceedings (pp. 250-259). [8031601] IEEE Computer Society. https://doi.org/10.1109/PACIFICVIS.2017.8031601