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Volume: 61 | Article ID: jist0394
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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
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.

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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 Journal of Imaging Science and Technology,  2017,  pp 060404-1 - 060404-13,  https://doi.org/10.2352/J.ImagingSci.Technol.2017.61.6.060404

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Copyright © Society for Imaging Science and Technology 2017
  Article timeline 
  • received June 2017
  • accepted October 2017
  • PublishedNovember 2017

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