This is a collection of functions and scripts for analyzing fMRI data using networks using Python and igraph. Currently the framework is capable of dealing with networks up to thousands of nodes.
Parts of the code is currently 'tailored' for use within the NBE and CS departments at Aalto University. However, with a little effort it can be made to meet the standards of a wider audience.
So please have a look what we've got, and let us know if you're interested in using this code. Then we can make some efforts to make the code and its functions accessible for a wider audience.
For methods related to network coarse-graining, see especially modules compcoms.py and communities.py
Point of contact: [email protected]
Compile documentation with
make docs # requires rst2html command line tool
Then open docs/build/html/index.html with your favourite browser and enjoy the documentation :)
An example brainnets configuration file as well as a running script for producing coarse-graining plots is given under /examples/
In addition to the provided Python code (which is granted the MIT licence, see LICENCE.txt), this software package distributes also open source code from other projects:
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The ClusterPack library by A. Strehl for computing 'consensus communities'
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The generic Louvain algorithm (by E. Lefebvre, adapted by J.-L. Guillaume and R. Campigotto) for providing high-quality partitions of networks.
https://sites.google.com/site/findcommunities/ http://sourceforge.net/projects/louvain/
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R-code from A. Alexander-Bloch.:
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Under ext_data we have also included a background image to be used for visualizations. This file originates from the FSL project: see this projects licensing terms at: http://fsl.fmrib.ox.ac.uk/fsl/ Note that their license may not cover commercial use.
For more information on these pieces of software and their licensing, see the subdirectories of external_code.