brainnets: An opensource graph theoretical analysis library and application
Creators: name: Avraam Marimpis
name: Goebel, Rainer
Publisher: name: F1000 Research Limited
Year: 2021

description: Graph theoretical analysis enjoys increasing attention in recent years, highlighting its importance and usefulness in neuroimaging, specifically, to brain connectivity [1, 2]. Through this new battery of tools, researchers drew astonishing conclusions about the underlying dynamics governing the brain [3] and even proposed network models that help understand the complex organization of the brain [4, 5].<br> <br> In this work we present, “brainnets”, a new, open-source C++ library for graph analysis, specifically tailored for exploring functional brain connectivity. Functional connectivity refers to the statistical analysis, of usually distinct, multiple brain regions [6]. Functional connectivity analysis based on graph analysis gains increasing popularity in the field of neuroimaging with applications in a wide variety of fMRI studies [7].<br> <br> In order to support effective and easy graph-based analysis of functional connectivity, we developed the<br> brainnets and providing an easy-to-use high-level library for extracting graph features from functional<br> connectivity matrices. The developed tool focuses on the analysis and visualization of graph distances. The latter are well-established mathematical methods[8, 9, 10] that enable us to make comparisons between functional connectivity matrices computed between subjects (or even the same subject but different condition/task). We plan to employ brainnets as a framework to further study and develop these methods.<br> <br> While the open-source brainnets library can be used in combination with any neuroimaging software, here we demonstrate its usage and efficacy in combination with BrainVoyager (Brain Innovation, Maastricht) [11] by using it as a plugin with a developed graphical user interface (GUI). The plugin provides a graphical, intuitive way to load and plan a graph theoretical study on a subject or group’s connectivity matrices. The workflow is as follows. Load the data, estimate the connectivity (correlation or partial correlation) and specify parameters of the connectivity matrix (absolute values, positive, and so forth). Optionally, one can find important links by utilizing the Minimum Spanning Tree. Subsequently, the options for interactive visualization (standard block matrix or circular plot) and graph analysis become available. The implemented features (both global and nodal) are accessible under their dedicated tab alongside descriptions and more visualization options. In the case of multiple subjects, the user can perform group analysis, using the corresponding options.<br> <br> brainnets is built on top of other well-established, mature, and community-driven scientific projects, namely Eigen C++ and Boost. We use the Eigen library for efficient linear algebra implementations and exploit Boost for some of its graph-related features. The source code is freely available at<br> https://github.com/makism/brainnets.<br> <br> <br> <b>References</b>: Rubinov, M., &amp; Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. Neuroimage, 52(3), 1059-1069. Fallani, F. D. V., Richiardi, J., Chavez, M., &amp; Achard, S. (2014). Graph analysis of functional brain networks: practical issues in translational neuroscience. Phil. Trans. R. Soc. B, 369(1653), 20130521. Sporns, O. (2011). The non-random brain: efficiency, economy, and complex dynamics. Frontiers in computational neuroscience, 5, 5. Watts, D. J., &amp; Strogatz, S. H. (1998). Collective dynamics of ‘small-world’networks. nature, 393(6684), 440. Bassett, D. S., &amp; Bullmore, E. D. (2006). Small-world brain networks. The neuroscientist, 12(6), 512-523. Biswal, B., Zerrin Yetkin, F., Haughton, V. M., &amp; Hyde, J. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo‐planar MRI. Magnetic resonance in medicine, 34(4), 537-541. Lang, E. W., Tomé, A. M., Keck, I. R., Górriz-Sáez, J. M., &amp; Puntonet, C. G. (2012). Brain connectivity analysis: a short survey. Computational intelligence and neuroscience, 2012, 8. Wilson, R. C., &amp; Zhu, P. (2008). A study of graph spectra for comparing graphs and trees. Pattern Recognition, 41(9), 2833-2841. Jakobson, D., &amp; Rivin, I. (2000). Extremal metrics on graphs I. arXiv preprint math/0001169. Pincombe, B. (2007). Detecting changes in time series of network graphs using minimum mean squared error and cumulative summation. ANZIAM Journal, 48, 450-473. Goebel, R., Esposito, F., &amp; Formisano, E. (2006). Analysis of functional image analysis contest (FIAC) data with BrainVoyager QX: From single‐subject to cortically aligned group general linear model analysis and self‐organizing group independent component analysis. Human brain mapping, 27(5), 392-401.