CHEN Weifeng, SHI Lei, CHEN Wei, “A Survey of Macroscopic Brain Network Visualization Technology,” Chinese Journal of Electronics, vol. 27, no. 5, pp. 889-899, 2018, doi: 10.1049/cje.2018.04.007
Citation: CHEN Weifeng, SHI Lei, CHEN Wei, “A Survey of Macroscopic Brain Network Visualization Technology,” Chinese Journal of Electronics, vol. 27, no. 5, pp. 889-899, 2018, 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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