A Visualization Toolbox for Brain Connectivity Research
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README.md

Interactive Visual Analysis of Functional Brain Connectivity

Wiki: https://github.com/sugeerth/BrainModulyzer/wiki

Please click to watch the overview video.

ScreenShot

Brain Modulyzer

We present Brain Modulyzer, an interactive visual exploration tool for functional magnetic resonance imaging (fMRI) brain scans, aimed at analyzing the correlation between different brain regions when resting or when performing mental tasks. Brain Modulyzer combines multiple coordinated views---such as heat maps, node link diagrams and anatomical views---using brushing and linking to provide an anatomical context for brain connectivity data. Integrating methods from graph theory and analysis, e.g., community detection and derived graph measures, makes it possible to explore the modular and hierarchical organization of functional brain networks. Providing immediate feedback by displaying analysis results instantaneously while changing parameters gives neuroscientists a powerful means to comprehend complex brain structure more effectively and efficiently and supports forming hypotheses that can then be validated via statistical analysis.

Required Dependencies

numpy
networkx
nibabel
pydot
community
pygraphviz
vtk
PySide
decorator

Getting Started

Note: Tested on OS X 10.11.6 and Ubuntu 14.04

# Conda installation, if you dont have conda, then install via (http://conda.pydata.org/docs/install/quick.html)
conda install python=2.7
conda install anaconda-client
conda config --add channels anaconda
conda config --add channels pdrops  
conda config --add channels allank
conda config --add channels asmeurer 
conda config --add channels menpo
conda config --add channels conda-forge

# Special Case with vtk it might you give you an error AttributeError: 'module' object has no attribute 'vtkNIFTIImageReader'
# Be sure to install vtk 7 with qt for optimal performance  
conda install -c clinicalgraphics vtk

conda install -c sugeerth brainm=1.0.3
git clone https://github.com/sugeerth/BrainModulyzer.git

cd BrainModulyzer/src 

and then goto the Modulyzersrc> ./RunProjectMain.py

For non-conda users you can install the libraries manually

#For mac switch to macports python for installation (as installation libraries are in macports):
sudo port install python27
port select --list python
sudo port select --set python python27
	
#For mac set the pip for macports: 
sudo port install py27-pip
sudo port select --list pip 
sudo port select --set pip pip27

sudo pip install numpy==1.11.0
sudo pip install networkx==1.11
sudo pip install nibabel==2.0.2
sudo pip install pydotplus
sudo pip install python-louvain

To install Graphviz:
	**Install latest Graphviz** version through the graphviz website--(http://www.graphviz.org/Download_macos.php)
	
	for linux
		sudo apt-get install graphviz
		sudo pip install pygraphviz==1.3.1 --install-option="--include-path=/usr/include/graphviz" --install-option="--library-path=/usr/lib/graphviz/"
	for mac
		sudo pip install pygraphviz==1.3.1

To install QT:
	for linux
		sudo apt-get install libqt4-dev
		sudo easy_install -U PySide
	for mac
		sudo port install qt4-mac
		sudo port install py27-pyside

To install VTK (vtk version > 5):
	for linux
		#This is a bit tricky, follow this step by step, in the future this will become easier
		#Download from source VTK-7.0.0
		[VTK7](http://www.vtk.org/files/release/7.0/VTK-7.0.0.zip)
		unzip VTK-7.0.0.zip
		cd VTK-7.0.0
		mkdir Build
		cd Build 
		ccmake .. (in GUI turn two flags BUILD_SHARED_LIBS ON and PYTHON_WRAPPING, press 'c' twice and when the config completes press 'g')
		make -j 8
		sudo make install
		#Now all you have to do add paths in PYTHONPATHS
		export PYTHONPATH=/PATH_TO_VTK-7.0.0/Build/lib/
		export PYTHONPATH=$PYTHONPATH:/PATH_TO_VTK-7.0.0/Build/Wrapping/Python/
	for mac
		#port			
		sudo port install vtk

####Major Files In case any of the pre-existing library installation does not work, please download and install the affected libraries from source:

[Numpy](https://sourceforge.net/projects/numpy/files/NumPy/1.11.0/numpy-1.11.0.tar.gz/download )
[Networkx](https://pypi.python.org/packages/c2/93/dbb41b03cf7c878a7409c8e92226531f840a423c9309ea534873a83c9192/networkx-1.11.tar.gz#md5=6ef584a879e9163013e9a762e1cf7cd1 )
[Nibabel](http://nipy.org/nibabel/installation.html#installation ) 
[Louvain](https://pypi.python.org/packages/5d/81/497a95ba9d79d5bf04f9318256d1c0102329dd6a77b9d1e4dd84871e1089/python-louvain-0.5.tar.gz )
[PyDot](https://pypi.python.org/pypi/pydot3/1.0.8 )
[PySide](https://pypi.python.org/pypi/PySide/1.2.4)
[PyGraphViz](https://pypi.python.org/packages/98/bb/a32e33f7665b921c926209305dde66fe41003a4ad934b10efb7c1211a419/pygraphviz-1.3.1.tar.gz#md5=7f690295dfe77edaa9e552d09d98d279 )

Note: If after installing, "import pygraphviz" returns an error, then uninstall and reinstall using: 	
pip install pygraphviz --install-option="--include-path=/usr/local/include/graphviz" --install-option="--library-path=/usr/local/lib/graphviz/" (https://github.com/pygraphviz/pygraphviz/issues/72)

[VTK and PyVTK](http://www.it.uu.se/edu/course/homepage/vetvis/ht10/vtk/instructions_vtk_OSX.pdf) 

Happy Analysis!

####Major Files BrainViewerDataPaths.py -- path for the dataset

**RunMainProject.py** -- path for running the application

##Running the Tool modulyzerdir/src> RunMainProject.py

In the following figure,

    A) the community graph highlights the subcommunities associated with the community selected in the dendrogram view.
    B) Configuration options provide various choices for interactivity, such as toggling hovering/clicking, choosing graph layout and varying the opacity of highlighted nodes. 
    C) The brain region graph displays highlighted nodes associated with selected sub communities.
    D) The dendrogram view displays the hierarchy of communities. The communities that do that not have any edges emanating from them are grayed out.
    E) A table view lists important graph properties of the graph shown in Fig. C.

ScreenShot

Contributing

See Contributing

Citation Information###

Please cite Brain Modulyzer in your publications if it helps your research:

@article{muru2016modulyzer,
  Author = {S. Murugesan and K. Bouchard and J. A. Brown and B. Hamann and W. W. Seeley and A. Trujillo and G. H. Weber},
  Journal = {IEEE/ACM transactions on computational biology and bioinformatics/IEEE, ACM},
  Title = {Brain Modulyzer: Interactive Visual Analysis of Functional Brain Connectivity},
  Year = {2016},
  doi={10.1109/TCBB.2016.2564970}, 
}

License Information

Brain Modulyzer is released under the BSD license.

Brain Modulyzer Copyright (c) 2016, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved.

If you have questions about your rights to use or distribute this software, please contact Berkeley Lab's Innovation and Partnerships department at IPO@lbl.gov referring to " Brain Modulyzer (2016-149),."

NOTICE. This software was developed under funding from the U.S. Department of Energy. As such, the U.S. Government has been granted for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the Software to reproduce, prepare derivative works, and perform publicly and display publicly. Beginning five (5) years after the date permission to assert copyright is obtained from the U.S. Department of Energy, and subject to any subsequent five (5) year renewals, the U.S. Government is granted for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the Software to reproduce, prepare derivative works, distribute copies to the public, perform publicly and display publicly, and to permit others to do so.