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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/).
BrainNet Viewer is free software available on the NITRC website (www.nitrc.org/projects/bnv/), which runs with MATLAB under Windows, Linux and Mac OS, with either 32- or 64-bit systems. The latest version is 1.41, released September 18, 2012. The main window includes the menu bar, toolbar and contact information.
We developed BrainNet Viewer as a free software for visualizing macro-scale brain networks (or connectomics), which achieved the following major functions: 1) display brain networks in multi-views; 2) display combinations of brain surface, nodes and edges; 3) adjust properties of network elements (i.e., nodes and edges); 4) map the volume image to brain surface; 5) support various types of image format exporting and video making; and 6) provide interactive operations, such as zoom and rotate. In addition, we constructed functional brain networks from a public dataset and further analyzed and visualized the topological properties of the resultant brain networks.
We thank Professor Alan Evans for providing us the ICBM152 brain surface. We thank people who provided valuable suggestions during the software development in our laboratory, including Gaolang Gong, Ni Shu, Chaogan Yan, Xia Liang, Teng Xie, Qixiang Lin and Zhengjia Dai. We thank Patrick Clark for helping revise our manual. We also thank the developers of the following softwares and toolboxes whose source codes or file formats were referenced during the development process of BrainNet Viewer: Matlab (www.mathworks.com/products/matlab/), SurfStat (www.math.mcgill.ca/keith/surfstat/), FreeSurfer ( ), BrainVISA ( ) and SPM (www.fil.ion.ucl.ac.uk/spm/).
A note the data setsThe 24 data sets we studied in the paper were drawn from the literature, and the proper citations are given in the paper. You can find much more detailed information, including links to download many of the data sets, here.A note about method tutorialsWe do not currently have any tutorial information for installing or using these methods, beyond what we describe in the paper and what is contained in the help files that go with the Matlab and R files themselves. That being said, the InterSciWiki at UC Irvine has a good overview tutorial page that may be of some use, and Willy Lai has created a nice page, with R code, that works through several examples.Updates6 December 2012: added a link to the R package by Colin Gillespie.30 November 2012: replaced plpva.r with an updated version by Neal Walfield.2 August 2012: WARNING, the zeta function implementation used here is unstable for large alpha (>7) (thanks to David Gleich for pointing this out). If you need it for this range, consider using a better library function for the Hurwitz Zeta function.17 January 2012: fixed a minor bug in the way plfit, plvar, plpva parse the nowarn and nosmall arguments.24 August 2011: posted updated version of plfit.r, at the request of its author Laurent Dubroca.4 August 2011: posted Joel Ornstein's Python ports of plfit, plvar, plpva and plplot.8 October 2010: replaced plfit.r with new version, at request of its author Laurent Dubroca.24 January 2010: fixed a minor bug in how plfit.m reports the log-likelihood of the fitted data, for the discrete case, after the selection of xmin is done; posted updated version of Wim Otte's code with the same fix.27 November 2009: posted Wim Otte's C++ implementation of plfit and plvar.1 October 2009: added the option in plfit, plvar and plpva to 'lock' xmin to a specific value (thanks to Paul Willems for the suggestion).27 August 2009: posted Adam Ginsburg's Python implementation of plfit.13 August 2009: created a new page with detailed information about obtaining copies of the 24 empirical data sets we studied.12 August 2009: fixed a minor bug in the R version of plfit that would cause its results to disagree slightly with the results from the matlab version (thanks to Naoki Masuda for pointing it out).17 March 2009: fixed a minor bug in the R version of plfit that would cause the returned KS statistic to be incorrect when xmin=1 (thanks to Jeff Stuckman for pointing it out).7 February 2009: try-catch block in integer portion of plfit now defaults to iterative version if the try block ever fails (thanks to Rajiv Das for the suggestion).25 April 2008: changed randht, plpva and plvar to only initialize the pseudo-random number generator on the first time they are called.5 March 2008: in the integer routines, plfit, plvar and plpva now automatically switch to a slower but more memory efficient estimation routine when the vectorized default routine fails (e.g., Out of Memory error when max(x) is extremely large).29 February 2008: posted Laurent Dubroca's R implementation of plfit.17 February 2008: posted the plplot.m function for plotting the fitted power-law distributions against the empirical data.30 January 2008: corrected typo in plpva when using hidden 'sample' option, and reordered the commands for 'limit' and 'sample' throughout (thanks to Klaas Dellschaft for suggestions).28 September 2007: corrected typo in argument parsing for randht.m, significant efficiency improvements to xmin estimation routine in plfit.m, plpva.m and plvar.m (thanks to Jim Bagrow for suggestions).7 September 2007: corrected interim reporting in plpva.m; changed plfit.m, plvar.m and plpva.m to reshape input vector to column format, and to prevent using continuous approximation in small-sample regime for discrete data.25 July 2007: corrected a typo in plvar.m, typo in pareto.R, typo in log-likelihood for discrete cut-off powerlaw and fixed small bug in a plotting routine.29 June 2007: corrected a typo in plpva.m, ty