CHEN Weifeng, SHI Lei, CHEN Wei. A Survey of Macroscopic Brain Network Visualization Technology[J]. Chinese Journal of Electronics, 2018, 27(5): 889-899. doi: 10.1049/cje.2018.04.007
Citation: CHEN Weifeng, SHI Lei, CHEN Wei. A Survey of Macroscopic Brain Network Visualization Technology[J]. Chinese Journal of Electronics, 2018, 27(5): 889-899. doi: 10.1049/cje.2018.04.007

A Survey of Macroscopic Brain Network Visualization Technology

doi: 10.1049/cje.2018.04.007
Funds:  This work is supported by the National Key Basic Research and Development Program of China (973 Program) (No.2014CB340301) and National Natural Science Foundation of China (No.61772504, No.61772456, No.61761136020).
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  • Corresponding author: SHI Lei (corresponding author) received the B.S., M.S., and Ph.D. degrees from Tsinghua University, in 2003, 2006, and 2008, respectively. He is a professor in SKLCS, Institute of Software, Chinese Academy of Sciences. His research interests include visual analytics and data mining. He is the recipient of the IBM Research Accomplishment Award on Visual Analytics and the VAST Challenge Award twice in 2010 and 2012. (
  • Received Date: 2018-02-24
  • Rev Recd Date: 2018-03-21
  • Publish Date: 2018-09-10
  • Brain science, as an important branch of Neuroscience, is a discipline that studies the structure and function of brain nervous system of human and other mammals. With the invention of new technologies such as brain imaging, light microscopes and brain electromagnetics, brain science is gradually unraveling the mysteries of human emotion, intelligence and behavior. In this wave, data visualization punctuates the landmark advances in brain science since its beginning. This survey reviews the recent literature on brain network visualization (aka connectome) from the fields of both connectomics and visualization. In particular, we focus on the macroscopic-level brain network visualization techniques, that reveal the structural and functional connectivity of the whole brain, in comparison to microsopic-level neuronal connectivities. We also discuss the interactive visualization tools currently available for viewing the brain networks. Finally, we conclude with a number of ongoing challenges in macroscopic brain network visualization.
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  • H. Pfister, V. Kaynig, C.P. Botha, et al., “Visualization in connectomics”, Scientific Visualization, Springer, pp.221-245, 2014.
    O. Sporns and J.D. Zwi, “The small world of the cerebral cortex”, Neuroinformatics, Vol.2, No.2, pp.145-162, 2004.
    K.J. Friston, “Functional and effective connectivity in neuroimaging: A synthesis”, Human Brain Mapping, Vol.2, No.1-2, pp.56-78, 1994.
    D.L. Schomer and F.L. Da Silva, Niedermeyer’s Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, Lippincott Williams & Wilkins, 2012.
    M. Hämäläinen, R. Hari, R. J. Ilmoniemi, et al., “Magneto encephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain”, Reviews of Modern Physics, Vol.65, No.2, p.413, 1993.
    N.K. Logothetis, J. Pauls, M. Augath, et al., “Neurophysiological investigation of the basis of the fMRI signal”, Nature, Vol.412, No.6843, p.150, 2001.
    D.L. Bailey, D.W. Townsend, P.E. Valk, et al., Positron Emission Tomography, Springer, 2005.
    D.W. McRobbie, E.A. Moore and M.J. Graves, MRI from Picture to Proton, Cambridge University Press, 2017.
    D.S. Margulies, J. Böttger, A. Watanabe, et al., “Visualizing the human connectome”, NeuroImage, Vol.80, pp.445-461, 2013.
    M. Zockler, D. Stalling and H.-C. Hege, “Interactive visualization of 3D-vector fields using illuminated stream lines”, Proc. IEEE Visualization, pp.107-113, 1996.
    V. Petrovic, J. Fallon and F. Kuester, “Visualizing wholebrain DTI tractography with GPU-based tuboids and LoD management”, IEEE Transactions on Visualization and Computer Graphics, Vol.13, No.6, pp.1488-1495, 2007.
    D. Merhof, M. Sonntag, F. Enders, et al., “Hybrid visualization for white matter tracts using triangle strips and point sprites”, IEEE Transactions on Visualization and Computer Graphics, Vol.12, No.5, pp.1181-1188, 2006.
    T. Peeters, A. Vilanova and R. ter Haar Romeny, “Visualization of DTI fibers using hair-rendering techniques”, Proc. ASCI, pp.66-73, 2006.
    S. Zhang, C. Demiralp and D.H. Laidlaw, “Visualizing diffusion tensor MR images using streamtubes and streamsurfaces”, IEEE Transactions on Visualization and Computer Graphics, Vol.9, No.4, pp.454-462, 2003.
    J. Klein, F. Ritter, H.K. Hahn, et al., “Brain structure visualization using spectral fiber clustering”, SIGGRAPH Research Posters, Article No.168, 2006.
    M.H. Everts, H. Bekker, J.B. Roerdink, et al., “Depthdependent halos: Illustrative rendering of dense line data”, IEEE Transactions on Visualization and Computer Graphics, Vol.15, No.6, pp.1299-1306, 2009.
    S. Zhukov, A. Iones and G. Kronin, “An ambient light illumination model”, G. Drettakis and N. Max Eds., Rendering Techniques’98, Vienna: Springer Vienna, pp.45-55, 1998.
    J. Díaz García and P.-P. Vázquez, “Fast illustrative visualization of fiber tracts”, G. Bebis, R. Boyle, B. Parvin, et al., Eds., Advances in Visual Computing, Berlin, Heidelberg: Springer Berlin Heidelberg, pp.698-707, 2012.
    S. Eichelbaum, M. Hlawitschka and G. Scheuermann, “Lineao-improved three-dimensional line rendering”, IEEE Transactions on Visualization and Computer Graphics, Vol.19, No.3, pp.433-445, 2013.
    G.J. Parker, H.A. Haroon and C.A. Wheeler-Kingshott, “A framework for a streamline-based probabilistic index of connectivity (PICo) using a structural interpretation of MRI diffusion measurements”, Journal of Magnetic Resonance Imaging, Vol.18, No.2, pp.242-254, 2003.
    A. von Kapri, T. Rick, S. Caspers, et al., “Evaluating a visualization of uncertainty in probabilistic tractography”, Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling, International Society for Optics and Photonics, Vol.7625, p.762534, 2010.
    P. Hermosilla, R. Brecheisen, P.-P. Vázquez, et al., “Uncertainty visualization of brain fibers”, I. Navazo and G. Patow, Eds., Spanish Computer Graphics Conference, the Eurographics Association, 2012.
    T. Rick, A. von Kapri, S. Caspers, et al., “Visualization of probabilistic fiber tracts in virtual reality”, Studies in Health Technology & Informatics, Vol.163, pp.486-492, 2011.
    F. Enders, N. Sauber, D. Merhof, et al., “Visualization of white matter tracts with wrapped streamlines”, Visualization VIS 05. IEEE, pp.51-58, 2005.
    D. Röttger, D. Merhof and S. Müller, “The BundleExplorer: A focus and context rendering framework for complex fiber distributions”, T. Ropinski, A. Ynnerman, C. Botha, et al., Eurographics Workshop on Visual Computing for Biology and Medicine, the Eurographics Association, pp.1-8, 2012.
    R. Otten, A. Vilanova and H. Van De Wetering, “Illustrative white matter fiber bundles”, Computer Graphics Forum, Wiley Online Library, Vol.29, No.3, pp.1013-1022, 2010.
    M.H. Everts, E. Begue, H. Bekker, et al., “Exploration of the brain’s white matter structure through visual abstraction and multi-scale local fiber tract contraction”, IEEE Transactions on Visualization and Computer Graphics, Vol.21, No.7, pp.808-821, 2015.
    W. Chen, Z. Ding, S. Zhang, et al., “A novel interface for interactive exploration of DTI fibers”, IEEE Trans. on Visualization and Computer Graphics, Vol.15, No.6, pp.1433-1440, 2009.
    J. Blaas, C.P. Botha, B. Peters, et al., “Fast and reproducible fiber bundle selection in DTI visualization”, Visualization (VIS 05), IEEE, pp.59-64, 2005.
    T. Schultz, N. Sauber, A. Anwander, et al., “Virtual klingler dissection: Putting fibers into context”, Computer Graphics Forum, Wiley Online Library, Vol.27, No.3, pp.1063-1070, 2008.
    E. Bullmore and O. Sporns, “Complex brain networks: Graph theoretical analysis of structural and functional systems”, Nature Reviews Neuroscience, Vol.10, No.3, p.186, 2009.
    G.D. Battista, P. Eades, R. Tamassia, et al., Graph Drawing: Algorithms for the Visualization of Graphs, Prentice Hall PTR, 1998.
    M. Xia, J. Wang and Y. He, “Brainnet viewer: Anetwork visualization tool for human brain connectomics”, PloS One, Vol.8, No.7, doi: 10.1371/journal.pone.0068910, 2013.
    K.J. Worsley, J.-I. Chen, J. Lerch, et al., “Comparing functional connectivity via thresholding correlations and singular value decomposition”, Philosophical Trans. of the Royal Society of London B: Biological Sciences, Vol.360, No.1457, pp.913-920, 2005.
    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, Vol.26, No.1, pp.63-72, 2006.
    B. Alper, B. Bach, N. Henry Riche, et al., “Weighted graph comparison techniques for brain connectivity analysis”, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM, pp.483-492, 2013.
    E.J. Sanz-Arigita, M.M. Schoonheim, J.S. Damoiseaux, et al., “Loss of ‘small-world’ networks in Alzheimer’s disease: Graph analysis of FMRI resting-state functional connectivity”, PloS One, Vol.5, No.11, p.e13788, 2010.
    J. McGonigle, A.L. Malizia and M. Mirmehdi, “Visualizing functional connectivity in fMRI using hierarchical edge bundles”, New Phytologist, Vol.206, No.3, pp.1038-1050, 2011.
    X. Yang, L. Shi, M. Daianu, et al., “Blockwise human brain network visual comparison using nodetrix representation”, IEEE Transactions on Visualization and Computer Graphics, Vol.23, No.1, pp.181-190, 2017.
    B. Bach, E. Pietriga and J.-D. Fekete, “Visualizing dynamic networks with matrix cubes”, Proc. of the SIGCHI Conf. on Human Factors in Computing Systems, ACM, pp.877-886, 2014.
    B. Bach, N. Henry-Riche, T. Dwyer, et al., “Small multipiles: Piling time to explore temporal patterns in dynamic networks”, Computer Graphics Forum, Wiley Online Library, Vol.34, No.3, pp.31-40, 2015.
    B. Bach, C. Shi, N. Heulot, et al., “Time curves: Folding time to visualize patterns of temporal evolution in data”, IEEE Transactions on Visualization and Computer Graphics, Vol.22, No.1, pp.559-568, 2016.
    D.A. Fair, A.L. Cohen, J.D. Power, et al., “Functional brain networks develop from a “local to distributed” organization”, PLoS Computational Biology, Vol.5, No.5, p.e1000381, 2009.
    M. Daianu, N. Jahanshad, T.M. Nir, et al., For the Alzheimer’s Disease Neuroimaging Initiative, “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.
    A. Zalesky, A. Fornito and E.T. Bullmore, “Network-based statistic: Identifying differences in brain networks”, Neuroimage, Vol.53, No.4, pp.1197-1207, 2010.
    L. Shi, H. Tong and X. Mu, “Brainquest: Perception-guided brain network comparison”, 2015 IEEE International Conference on Data Mining (ICDM), IEEE, pp.379-388, 2015.
    J. Wang, S. Fang, H. Li, et al., “Multigraph visualization for feature classification of brain network data”, Proceedings of the EuroVis Workshop on Visual Analytics, Eurographics Association, pp.61-65, 2016.
    L. Shi, H. Tong, M. Daianu, et al., “Visual analysis of brain networks using sparse regression models”, ACM Trans. Knowl. Discov. Data, Vol.12, No.1, pp.5:1-5:30, 2018.
    R. Wang, T. Benner, A.G. Sorensen, et al., “Diffusion toolkit: A software package for diffusion imaging data processing and tractography”, Proc. of the 15th Annual Meeting of ISMRM, Vol.15, p.3720, 2007.
    V.J. Wedeen, D.L. Rosene, R. Wang, et al., “The geometric structure of the brain fiber pathways”, Science, Vol.335, No.6076, pp.1628-1634, 2012.
    R. Jianu, C. Demiralp and D.H. Laidlaw, “Exploring brain connectivity with two-dimensional neural maps”, IEEE Transactions on Visualization and Computer Graphics, Vol.18, No.6, pp.978-987, 2012.
    A. Fedorov, R. Beichel, J. Kalpathy-Cramer, et al., “3D slicer as an image computing platform for the quantitative imaging network”, Magnetic Resonance Imaging, Vol.30, No.9, pp.1323-1341, 2012.
    S. Eichelbaum, M. Hlawitschka and G. Scheuermann, “Openwalnut: An open-source tool for visualization of medical and bio-signal data”, Biomedizinische Technik. Biomedical Engineering, Vol.58, No.5, doi: 10.1515/bmt-2013-4183, 2013.
    A. Eklund, O. Friman, M. Andersson, et al., “A GPU accelerated interactive interface for exploratory functional connectivity analysis of fMRI data”, 201118th IEEE International Conference on Image Processing (ICIP), IEEE, pp.1589-1592, 2011.
    F. Heckel, M. Schwier and H.-O. Peitgen, “Object-oriented application development with mevislab and python”, GI Jahrestagung, Vol.154, pp.1338-51, 2009.
    S. Gerhard, A. Daducci, A. Lemkaddem, et al., “The connectome viewer toolkit: An open source framework to manage, analyze, and visualize connectomes”, Frontiers in Neuroinformatics, Vol.5, No.3, doi: 10.3389/fninf.2011.00003, 2011.
    D. Haehn, N. Rannou, B. Ahtam, et al., “Neuroimaging in the browser using the x toolkit”, Frontiers in Neuroinformatics, doi: 10.3389/conf.fninf.2014.08.00101, 2014.
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