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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  • S.L. Bressler, V. Menon, "Large-scale brain networks in cognition:Emerging methods and principles", Trends in Cognitive Sciences, Vol.14, No.6, pp.277-290, 2010.
    M. Cao, N. Shu, Q. Cao, et al., "Imaging functional and structural brain connectomics in attention-deficit/hyperactivity disorder", Molecular Neurobiology, Vol.50, No.3, pp.1111-1123, 2014.
    Y. He, J. Wang, L. Wang, et al., "Uncovering intrinsic modular organization of spontaneous brain activity in humans", PloS one, Vol.4, No.4, pp.1-18, 2009.
    R. Salvador, J. Suckling, M.R. Coleman, et al., "Neurophysiological architecture of functional magnetic resonance images of human brain", Cerebral Cortex, Vol.15, No.9, pp.1332-1342, 2005.
    S. Achard, R. Salvador, B. Whitcher, et al., "A resilient, lowfrequency, small-world human brain functional network with highly connected association cortical hubs", Journal of Neuroscience the Official Journal of the Society for Neuroscience, Vol.26, No.1, pp.63-72, 2006.
    P. Hagmann, L. Cammoun, X. Gigandet, et al., "Mapping the Structural Core of Human Cerebral Cortex", Plos Biology, Vol.6, No.7, Article ID e159, 2012.
    G. Gong, Y. He, L. Concha, et al., "Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography", Cerebral Cortex, Vol.19, No.3, pp.524-536, 2009.
    R. Baltadjieva, N. Giladi, L. Gruendlinger, et al., "Marked alterations in the gait timing and rhythmicity of patients with de novo Parkinson's disease", European Journal of Neuroscience, Vol.24, No.6, pp.1815-1820, 2006.
    A. Diosdado, "A non linear analysis of human gait time series based on multifractal analysis and cross correlations", Journal of Physics:Conference Series, Vol.23, No.1, pp.87, 2005.
    G. Widman, K. Lehnertz, P. Jansen, et al., "A fast general purpose algorithm for the computation of auto-and crosscorrelation integrals from single channel data", Physica D:Nonlinear Phenomena, Vol.121, No.1-2, pp.65-74, 1998.
    J.J. Zhuang, X.B. Ning, X.D. Yang, et al., "Decrease in Hurst exponent of human gait with aging and neurodegenerative diseases", Chinese Physics B, Vol.17, No.3, pp.852-856, 2008.
    J. Wang, X. Zuo and Y. He, "Graph-based network analysis of resting-state functional MRI", Frontiers in Systems Neuroscience, Vol.4, No.16, Article ID 16, 2010.
    E. Bullmore and O. Sporns, "Complex brain networks:Graph theoretical analysis of structural and functional systems", Nature Reviews Neuroscience, Vol.10, No.3, pp.186-198, 2009.
    L.A.N. Amaral, A. Scala, M. Barthelemy, et al., "Classes of small-world networks", Proceedings of the National Academy of Sciences, Vol.97, No.21, pp.11149-11152, 2000.
    D.J. Watts and S.H. Strogatz, "Collective dynamics of ‘smallworld’ networks", Nature, Vol.393, No.6684, pp.440-442, 1998.
    A. Fornito, A. Zalesky, C. Pantelis, et al., "Schizophrenia, neuroimaging and connectomics", Neuroimage, Vol.62, No.4, PP.2296-2314, 2012.
    W.W. Seeley, R.K. Crawford, J. Zhou J, et al., "Neurodegenerative diseases target large-scale human brain networks", Neuron, Vol.62, No.1, pp.42-52, 2009.
    A. Leemans and D.K. Jones, "The B-matrix must be rotated when correcting for subject motion in DTI data", Magnetic Resonance in Medicine, Vol.61, No.6, pp.1336-1349, 2009.
    J.C. Gee and D.C. Alexander, "Diffusion-tensor image registration", Visualization and Processing of Tensor Fields, Springer, Berlin, Heidelberg, pp.327-342, 2006.
    N. Tzourio-Mazoyer, B. Landeau, D. Papathanassiou, et al., "Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI singlesubject brain", Neuroimage, Vol.15, No.1, pp.273-289, 2002.
    S. Mori, B.J. Crain, V.P. Chacko, et al., "Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging", Annals of Neurology, Vol.45, No.2, pp.265-269, 1999.
    M. Daianu, N. Jahanshad, T.M. Nir, et al., "Breakdown of brain connectivity between normal aging and Alzheimer's disease:A structural k-core network analysis", Brain Connectivity, Vol.3, No.4, pp.407-422, 2013.
    Z. Cui, S. Zhong, P. Xu, Y. He and G. Gong, "PANDA:A pipeline toolbox for analyzing brain diffusion images", Front Hum Neurosci, Vol.7, No.42, Article ID 42, 2013.
    M. Rubinov and O. Sporns, "Complex network measures of brain connectivity:Uses and interpretations", Neuroimage, Vol.52, No.3, pp.1059-1069, 2010.
    S.M. Hosseini, F. Hoeft and S.R. Kesler, "GAT:A graphtheoretical analysis toolbox for analyzing between-group differences in large-scale structural and functional brain networks", PLoS One, Vol.7, No.7, Article ID e40709, 2012.
    B. He, Y. Dai, L. Astolfi, F. Babiloni, H. Yuan, et al., "eConnectome:A MATLAB toolbox for mapping and imaging of brain functional connectivity", J Neurosci Methods, Vol.195, No.2, pp.261-269, 2011.
    M. Xia, J. Wang and Y. He, "BrainNet viewer:A network visualization tool for human brain connectomics", PloS One, Vol.8, No.7, Article ID e68910, 2013.
    S. Gerhard, A. Daducci, A. Lemkaddem, R. Meuli, J.P. Thiran, et al., "The connectome viewer toolkit:An open source framework to manage, analyze, and visualize connectomes", Frontiers in Neuroinformatics, Vol.5, No.3, pp.1-15, 2011.
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