XU Pengfei, TIAN Ge, ZUO Ran, ZHU Ning, LUO Yanlin. VisConnectome: An Independent and Graph-Theory Based Software for Visualizing the Human Brain Connectome[J]. Chinese Journal of Electronics, 2019, 28(3): 475-481. doi: 10.1049/cje.2019.03.006
Citation: XU Pengfei, TIAN Ge, ZUO Ran, ZHU Ning, LUO Yanlin. VisConnectome: An Independent and Graph-Theory Based Software for Visualizing the Human Brain Connectome[J]. Chinese Journal of Electronics, 2019, 28(3): 475-481. doi: 10.1049/cje.2019.03.006

VisConnectome: An Independent and Graph-Theory Based Software for Visualizing the Human Brain Connectome

doi: 10.1049/cje.2019.03.006
Funds:  This work is supported by the National key R&D program of China (No.2016YFF0203000), and the National Social Science Foundation of China (No.BCA150050).
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  • Corresponding author: LUO Yanlin (corresponding author) is an associate professor in the Department of Computer Science at Beijing Normal University. Her research interests include scientific visualization, 3D computer graphics and virtual reality. She has a PhD in applied mathematics from Zhejiang University. As a postdoctor, she visited Tokyo institute of Technology of Japan from Oct. 2001 to Oct. 2003, and George Mason University of USA from Sep. 2007 to Oct. 2008. (Email:luoyl@bnu.edu.cn)
  • Received Date: 2018-04-08
  • Publish Date: 2019-05-10
  • As a complex system, the topology of human's brain network can be analyzed by graph theory for the further study of brain's structural and functional mechanism. In order to construct and analyze the graph-based network efficiently and intuitively, it is necessary to develop flexible and independent visualization software. For this purpose, we developed an innovative software called VisConnectome. It runs on Windows system and does not rely on Matlab. It provides a friendly Graphical user interface (GUI) including the tool bar, the tool window, double slider, filter double slider, etc. It allows visualizing the brain network with the balland-stick geometric model, modifying its properties such as size and color, filtering nodes and connection as a simplification, blending with the brain surface as a context, etc. By experiments and comparison, we conclude that the VisConnectome is a flexible and independent visualization system with high speed and high quality.
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