brainGraph | Graph theory analysis of brain MRI data | Data Visualization library

by cwatson R Version: Current License: No License

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kandi X-RAY | brainGraph Summary

brainGraph is a R library typically used in Analytics, Data Visualization applications. brainGraph has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

brainGraph (RRID: SCR_017260) 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-based functions and the network-based statistic) will work with any graph that is compatible with igraph. The package will perform analyses for structural covariance networks (SCN), DTI tractography (I use probtrackx2 from FSL), and resting-state fMRI covariance (I have used the Matlab-based DPABI toolbox).

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Support

brainGraph has a low active ecosystem.

It has 105 star(s) with 35 fork(s). There are 16 watchers for this library.

It had no major release in the last 6 months.

There are 4 open issues and 19 have been closed. On average issues are closed in 52 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of brainGraph is current.

Quality

brainGraph has no bugs reported.

Security

brainGraph has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

brainGraph does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

brainGraph releases are not available. You will need to build from source code and install.

Installation instructions, examples and code snippets are available.

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brainGraph Key Features

No Key Features are available at this moment for brainGraph.

brainGraph Examples and Code Snippets

No Code Snippets are available at this moment for brainGraph.

Community Discussions

Trending Discussions on brainGraph

Computing graph efficiency metrics of a very large network

QUESTION

Computing graph efficiency metrics of a very large network

Asked 2019-Apr-20 at 20:12

What are strategies for calculating the nodal and global efficiency of very large graphs in R?

I'm attempting to calculate the global efficiency of a very large igraph with brainGraph::efficiency(my_graph, type = "global").

...

ANSWER

Answered 2019-Apr-20 at 20:12

R crashes because brainGraph::efficiency() attempts to calculate an enormous and dense distance matrix, which overwhelms the memory of my machine (32 GB). But I found a solution that chunks out the operation and runs in parallel.

Global efficiency is the mean of all nodal efficiencies in the graph. The nodal efficiency of a vertex i is:

We can sequentially calculate the nodal efficiency for each vertex in the graph, which splits the distance matrix calculation into smaller manageable bits. Because the efficiency of each vertex is independent, we can parallelize the operation so it doesn't take forever.

Source https://stackoverflow.com/questions/55754804

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install brainGraph

There are (primarily) two ways to install this package:. This should install all of the dependencies needed along with the package itself. For more details, see the User Guide (PDF link).
Directly from CRAN: (use one of the following commands)
From the GitHub repo (for development versions). This requires that the devtools package be installed:

Support

For bug reports, feature requests, help with usage/code/etc., please join the Google Group brainGraph-help. You may also consult the User Guide, and you can open an issue here on GitHub.

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