Multigraph Visualization for Feature Classification of Brain Network Data

Wang, Jiachen; Fang, Shiaofen; Li, Huang; Goñi, Joaquín; Saykin, Andrew J.; Shen, Li

Multigraph Visualization for Feature Classification of Brain Network Data

Files

061-065.pdf (3.8 MB)

Date

2016

Authors

Wang, Jiachen
Fang, Shiaofen
Li, Huang
Goñi, Joaquín
Saykin, Andrew J.
Shen, Li

Publisher

The Eurographics Association

Abstract

A Multigraph is a set of graphs with a common set of nodes but different sets of edges. Multigraph visualization has not received much attention so far. In this paper, we introduce a multigraph application in brain network data analysis that has a strong need for multigraph visualization. In this application, multigraph is used to represent brain connectome networks of multiple human subjects. A volumetric data set is constructed from the matrix representation of the multigraph. A volume visualization tool is then developed to assist the user to interactively and iteratively detect network features that may contribute to certain neurological conditions. We apply this technique to a brain connectome dataset for feature detection in the classification of Alzheimer's Disease (AD) patients. Preliminary results show significant improvements when interactively selected features are used.

        @inproceedings{10.2312:eurova.20161126
,
booktitle = {EuroVis Workshop on Visual Analytics (EuroVA)
},
editor = {Natalia Andrienko and Michael Sedlmair
},
title = {{Multigraph Visualization for Feature Classification of Brain Network Data
}},
author = {Wang, Jiachen and 
Fang, Shiaofen and 
Li, Huang and 
Goñi, Joaquín and 
Saykin, Andrew J. and 
Shen, Li
},
year = {2016
},
publisher = {The Eurographics Association
},
ISSN = {-
},
ISBN = {978-3-03868-016-1
},
DOI = {10.2312/eurova.20161126
}
}