Dear DPABI users, (apologies for cross-post)
I am pleased to announce the first major release of my R package "brainGraph" (v1.0.0), a collection of functions for performing graph theory analyses of brain MRI data. I chose R because it is free and is, in my opinion, the best choice for just about any statistical analysis. It is available on CRAN at https://cran.r-project.org/web/packages/brainGraph/index.html and development versions will be on my Github page (link below).
You can use it for gray matter covariance networks (cortical thickness, volume, surface area, or LGI), DTI tractography data (FSL's "probtrackx2", PANDA, TrackVis, etc.), and for resting-state fMRI (DPABI/DPARSF).
It is very heavily dependent on the fantastic R package "igraph" (see igraph.org), which is based on C code and is quite a bit faster than many other R applications.
My Github page for the package is https://github.com/cwatson/brainGraph . At the bottom is the "README.md" file which provides some basic information. Most important is the link to the User Guide, which has extensive installation and usage information (warning: it is a direct PDF link). It is very long but should be helpful. You will find code for getting your data into R, and I have documented many analysis steps and include multiple figures. I hope this is intuitive for both veteran and novice R users. Additionally, there are links for help learning R, and links to other R packages relevant to neuroimaging applications.
Some features that should be of interest include:
I am pleased to announce the first major release of my R package "brainGraph" (v1.0.0), a collection of functions for performing graph theory analyses of brain MRI data. I chose R because it is free and is, in my opinion, the best choice for just about any statistical analysis. It is available on CRAN at https://cran.r-project.org/web
You can use it for gray matter covariance networks (cortical thickness, volume, surface area, or LGI), DTI tractography data (FSL's "probtrackx2", PANDA, TrackVis, etc.), and for resting-state fMRI (DPABI/DPARSF).
It is very heavily dependent on the fantastic R package "igraph" (see igraph.org), which is based on C code and is quite a bit faster than many other R applications.
My Github page for the package is https://github.com/cwatson/bra
Some features that should be of interest include:
* calculation of a large number of graph/vertex/edge measures (particularly those most common in neuroimaging)
* between-group vertex-wise analysis using the GLM
* implementation of the network-based statistic (NBS)
* bootstrapping & permutation testing
* random graph generation, small-worldness, and global/local/nodal efficiency
* rich-club calculations
* robustness ("targeted attack" or "random failure") & vulnerability
* a basic GUI to explore your networks (up to 2 groups/subject at a time)
Please see the NEWS.md file (https://github.com/cwatson/brainGraph/blob/master/NEWS.md ) for the changelog.
This remains a work-in-progress, so I am very happy to receive bug reports, feature requests, general questions asking for help with code, (constructive) criticism, etc.
Please join the Google Group that I set up for those purposes: https://groups.google.com/forum/?hl=en#!forum/brainGraph-help
Chris Watson
* random graph generation, small-worldness, and global/local/nodal efficiency
* rich-club calculations
* robustness ("targeted attack" or "random failure") & vulnerability
* a basic GUI to explore your networks (up to 2 groups/subject at a time)
Please see the NEWS.md file (https://github.com/cwatson/br
This remains a work-in-progress, so I am very happy to receive bug reports, feature requests, general questions asking for help with code, (constructive) criticism, etc.
Please join the Google Group that I set up for those purposes: https://groups.google.com/foru
Chris Watson
Forums
Congratulations!
Congratulations!
Best,
Chao-Gan