BrainNet Viewer

BrainNet Viewer: a network visualization tool for human brain connectomics. The human brain is a complex system whose topological organization can be represented using connectomics. 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. 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. However, given the huge complexity of this methodology, toolboxes for graph-based network visualization are still lacking. 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. Within this toolbox, several combinations of defined files with connectome information can be loaded to display different combinations of brain surface, nodes and edges. 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. Moreover, BrainNet Viewer draws the brain surface, nodes and edges in sequence and displays brain networks in multiple views, as required by the user. The figure can be manipulated with certain interaction functions to display more detailed information. Furthermore, the figures can be exported as commonly used image file formats or demonstration video for further use. 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 (

References in zbMATH (referenced in 11 articles )

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  1. Fardin Ghorbani, Soheil Hashemi, Ali Abdolali, Mohammad Soleimani: EEGsig machine learning-based toolbox for End-to-End EEG signal processing (2020) arXiv
  2. He, Ping; Xu, Xiaohua; Ding, Jie; Fan, Baichuan: Low-rank nonnegative matrix factorization on Stiefel manifold (2020)
  3. Hövel, Philipp; Viol, Aline; Loske, Philipp; Merfort, Leon; Vuksanović, Vesna: Synchronization in functional networks of the human brain (2020)
  4. Paul, Subhadeep; Chen, Yuguo: A random effects stochastic block model for joint community detection in multiple networks with applications to neuroimaging (2020)
  5. Wang, Miaoyan; Li, Lexin: Learning from binary multiway data: probabilistic tensor decomposition and its statistical optimality (2020)
  6. Yao, Peng; Li, Xiang: Toward optimizing control signal paths in functional brain networks (2019)
  7. Alam, Md. Ashad; Calhoun, Vince D.; Wang, Yu-Ping: Identifying outliers using multiple kernel canonical correlation analysis with application to imaging genetics (2018)
  8. Lea Waller; Anastasia Brovkin; Lena Dorfschmidt; Danilo Bzdok; Henrik Walter; Johann Daniel Kruschwitz: GraphVar 2.0: A user-friendly toolbox for machine learning on functional connectivity measures (2018) arXiv
  9. Le, Can M.; Levin, Keith; Levina, Elizaveta: Estimating a network from multiple noisy realizations (2018)
  10. Lum, Oliver; Golden, Bruce; Wasil, Edward: An open-source desktop application for generating arc-routing benchmark instances (2018)
  11. Sizemore, Ann E.; Giusti, Chad; Kahn, Ari; Vettel, Jean M.; Betzel, Richard F.; Bassett, Danielle S.: Cliques and cavities in the human connectome (2018)