BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics

By Mingrui Xia, Jinhui Wang, Yong He. PLoS ONE, 2013

4,057 citations recorded by the source corpus.

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 (www.nitrc.org/projects/bnv/).

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/a55df99552d6093b6150fe2ca79d017644b73935

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Xi-Nian Zuo
2. Yong He
3. D. Bassett
4. Jinhui Wang
5. Alan C. Evans
6. Olaf Sporns
7. Chaogan Yan
8. Yufeng Zang

In Radiology, Nuclear Medicine and Imaging

1. Xi-Nian Zuo
2. Yong He
3. Jinhui Wang
4. Chaogan Yan
5. Alan C. Evans
6. D. Bassett
7. Yufeng Zang
8. Jonathan D. Power

Citations in the corpus

• How Visual Is the Visual Cortex? Comparing Connectional and Functional Fingerprints between Congenitally Blind and Sighted Individuals (2015, methodology): “org/ projects/bnv/) (Xia et al., 2013).”
• Benchmarking of participant‐level confound regression strategies for the control of motion artifact in studies of functional connectivity (2017, methodology): “3 (Wickham, 2009); brain renderings were prepared in BrainNet Viewer (Xia et al., 2013).”
• GRETNA: a graph theoretical network analysis toolbox for imaging connectomics (2015, methodology): “Specifically, we have previously developed PANDA (Cui et al., 2013) for the construction of structural brain networks based on diffusion imaging data and BrainNet Viewer (Xia et al., 2013) toolkits for the visualization of brain networks.”
• A Novel Transfer Learning Approach to Enhance Deep Neural Network Classification of Brain Functional Connectomes (2018, methodology): “In Figure 5 , out of 4,005 FCs, we highlight the top 10 most discriminative ones identified by our DTL-NN and DNN approaches using BrainNet Viewer (Xia et al., 2013).”
• Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia (2015, neutral): “org/projects/bnv) toolbox (Xia et al., 2013) and (b) the “circularGraph” toolbox available at MATLAB Central (www.”
• State-space model with deep learning for functional dynamics estimation in resting-state fMRI (2016, positive): “We used the BrainNet Viewer (Xia et al., 2013) to generate these images.”
• DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging (2016, methodology): “The viewer also provides an interface to the surface displaying tool, BrainNet Viewer (Xia et al., 2013), thus producing a volume map and a corresponding surface map with the same color and range settings.”
• Cliques and cavities in the human connectome (2016, positive): “This and all brain networks are drawn with BrainNetViewer (Xia et al. 2013).”
• Topographic Factor Analysis: A Bayesian Model for Inferring Brain Networks from Neural Data (2014, methodology): “) This panel was created using BrainNet viewer [24].”
• Distributed theta networks support the control of working memory: Evidence from scalp and intracranial EEG (2025, unknown): “BrainNet Viewer was used to visualize channels on the MNI-152 template brain (Xia et al., 2013) (see Fig.”
• Intracranial EEG signals disentangle multi-areal neural dynamics of vicarious pain perception (2024, methodology): “For visualization, we fi rstly normalized the MRI images into the standard Montreal Neurological Institute (MNI) space using theFieldtriptoolbox 76,88 and thenrendered channellocations(the MNI coordinates of these channels) from all participants onto the Colin27 template brain 89 based on a high-reso”