brainGraph is an R package for performing graph theory analyses of brain MRI data. It is most useful in atlas-based analyses (e.g. using an atlas such as AAL or one from Freesurfer); however, many of the computations (e.g., the GLM functions and the network-based statistic) will still work with any graph that is compatible with igraph. I also have used this for tractography data from FSL's probtrackx2 and resting-state fMRI data from DPABI.

• Installation
• Usage
• Graph measures
  • Group analyses
  • Null graph-related measures
  • Other measures
• Visualization
• Getting Help
• Future versions

The package should work "out-of-the-box" on Linux systems (at least on Red Hat-based systems; i.e., CentOS, RHEL, Scientific Linux, etc.) since almost all development (and use, by me) has been on computers running CentOS 6 and (currently) CentOS 7. I have also had success running it (and did some development) on Windows 7, and have heard from users that it works on some versions of Mac OS and on Ubuntu.

There are two ways to install this package:

• Directly from CRAN:

install.packages('brainGraph')

• For development versions, using the devtools package (install first, if necessary):

devtools::install_github('cwatson/brainGraph')

This should install all of the dependencies needed along with the package itself. For more details, see the User Guide (link to PDF in next section).

I have a User Guide that contains extensive code examples for analyses common to brain MRI studies. I also include some code for getting your data into R from Freesurfer, FSL, and DPABI, and some suggestions for workflow organization. To access the User Guide, please use this link. (NOTE: you will be asked to download the PDF)

In addition to the extensive list of measures available in igraph, I have functions for calculating/performing:

• Between-group differences in vertex- or graph-level measures (e.g., degree, betweenness centrality, global efficiency, etc.) using the General Linear Model. See Chapter 7 of the User Guide, which was modeled after the GLM help page on FSL's wiki
• The multi-threshold permutation correction (MTPC) method for statistical inference (see Drakesmith et al., 2015)
• The network-based statistic (NBS) (see Zalesky et al., 2010)
• Bootstrapping of graph-level metrics (e.g., modularity)
• Permutation analysis of between-group differences in vertex- or graph-level measures
• "Individual contributions (leave-one-out [LOO] and add-one-patient [AOP]; see Saggar et al., 2015)

• Null/random graph generation (both the "standard" method, and also a method controlling for clustering; see Bansal et al., 2009)
• Small-worldness (the "original" of Watts & Strogatz, 1998 and Humphries et al., 2008; and "omega" introduced in Telesford et al., 2011)
• Rich-club coefficients and normalization (see Zhou & Mondragon, 2004; and Colizza et al., 2006)

• Efficiency (global, nodal, and local; see Latora & Marchiori, 2001)
• The "rich-core" (see Ma & Mondragon, 2015)
• Leverage centrality (see Joyce et al., 2010)
• Asymmetry index
• Robustness ("targeted attack" and "random failure") and vulnerability
• Euclidean distances of edges
• Participation coefficient and within-module degree z-score (see Guimera & Amaral, 2005)
• Gateway coefficient (see Vargas & Wahl, 2014)
• Communicability and communicability betweenness (see Estrada & Hatano, 2008; Estrada et al., 2009; Crofts & Higham, 2009)
• Vertex s-core membership (see Eidsaa & Almaas, 2013)

There is a plotting GUI for fast and easy data exploration that will not work without data from a standard atlas (ideally to be fixed some time in the future). You may use a custom atlas if you follow the same format as the other atlases in the package (see Chapter 4 of the User Guide). A screenshot of the GUI is here:

For bug reports, feature requests, help with usage/code/etc., please join the Google Group brainGraph-help.

An incomplete list of features/functionality I plan on adding to future versions:

• Mediation analysis at both the graph- and vertex-level (currently only simple linear models)
• Thresholding and graph creation using the minimum spanning tree as a base
• Create methods for objects of class brainGraph, making it simpler to, for example, show a text summary of a graph or to plot the graph over the MNI slice, etc.
  • plot_brainGraph_mni will be removed as it will be a redundant step
• Use methods for GLM-related functions/objects. For example, I am writing a function for GLM diagnostics which will mimic the functionality of the base R function plot.lm