Back to articles
JIST-first
Volume: 30 | Article ID: art00007
Image
RemBrain: Exploring Dynamic Biospatial Networks with Mosaic Matrices and Mirror Glyphs
  DOI :  10.2352/J.ImagingSci.Technol.2017.61.6.060404  Published OnlineNovember 2017
Abstract

We introduce a web-based visual comparison approach for the systematic exploration of dynamic activation networks across biological datasets. Understanding the dynamics of such networks in the context of demographic factors like age is a fundamental problem in computational systems biology and neuroscience. We design visual encodings for the dynamic and community characteristics of these temporal networks. Our multi-scale approach blends nested mosaic matrices that capture temporal characteristics of the data, spatial views of the network data, Kiviat diagrams and mirror glyphs that detail the temporal behavior and community assignment of specific nodes. A top design specifically targeted at pairwise visual comparison further supports the comparative analysis of multiple dataset activations. We demonstrate the effectiveness of this approach through a case study on mouse brain network data. Domain expert feedback indicates this approach can help identify trends and anomalies in the data. © 2017 Society for Imaging Science and Technology.

Subject Areas :
Views 9
Downloads 0
 articleview.views 9
 articleview.downloads 0
  Cite this article 

Chihua Ma, Filippo Pellolio, Daniel A. Llano, Kevin Ambrose Stebbings, Robert V. Kenyon, G. Elisabeta Marai, "RemBrain: Exploring Dynamic Biospatial Networks with Mosaic Matrices and Mirror Glyphsin Proc. IS&T Int’l. Symp. on Electronic Imaging: Visualization and Data Analysis,  2017,  pp 060404-1 - 060404-13,  https://doi.org/10.2352/J.ImagingSci.Technol.2017.61.6.060404

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2018
72010604
Electronic Imaging
2470-1173
Society for Imaging Science and Technology