81225012, YH), and Beijing Natural Science Foundation (Grant No. 8103000667), the National Science Fund for Distinguished Young Scholars (Grant No. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.įunding: This study was supported by the Natural Science Foundation (Grant Nos. Received: ApAccepted: JPublished: July 4, 2013Ĭopyright: © 2013 Xia et al. PLoS ONE 8(7):Įditor: Peter Csermely, Semmelweis University, Hungary
WANG 2011 HUMAN BRAIN MAPPING FMRI SOFTWARE
BrainNet Viewer helps researchers to visualize brain networks in an easy, flexible and quick manner, and this software is freely available on the NITRC website ( Citation: Xia M, Wang J, He Y (2013) BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics. Furthermore, the figures can be exported as commonly used image file formats or demonstration video for further use. The figure can be manipulated with certain interaction functions to display more detailed information. Moreover, BrainNet Viewer draws the brain surface, nodes and edges in sequence and displays brain networks in multiple views, as required by the user. In addition, display properties, such as the color and size of network elements or the layout of the figure, can be adjusted within a comprehensive but easy-to-use settings panel. Within this toolbox, several combinations of defined files with connectome information can be loaded to display different combinations of brain surface, nodes and edges. Here, using MATLAB with a graphical user interface (GUI), we developed a graph-theoretical network visualization toolbox, called BrainNet Viewer, to illustrate human connectomes as ball-and-stick models. However, given the huge complexity of this methodology, toolboxes for graph-based network visualization are still lacking. These methods reveal the intriguing topological architectures of human brain networks in healthy populations and explore the changes throughout normal development and aging and under various pathological conditions. Recent studies have shown that human connectomes can be constructed using various neuroimaging technologies and further characterized using sophisticated analytic strategies, such as graph theory. The human brain is a complex system whose topological organization can be represented using connectomics.