Introduction: The field of Connectomic research is growing rapidly, resulting from methodological advances in structural neuroimaging on many spatial scales. Especially progress in Diffusion MRI data acquisition and processing made available macroscopic structural connectivity maps in vivo through Connectome Mapping Pipelines (Hagmann et al, 2008) into so-called Connectomes (Hagmann 2005, Sporns et al, 2005). They exhibit both spatial and topological information that constrain functional imaging studies and are relevant in their interpretation. The need for a special-purpose software tool for both clinical researchers and neuroscientists to support investigations of such connectome data has grown.
Methods: We developed the ConnectomeViewer, a powerful, extensible software tool for visualization and analysis in connectomic research. It uses the novel defined container-like Connectome File Format, specifying networks (GraphML), surfaces (Gifti), volumes (Nifti), track data (TrackVis) and metadata. Usage of Python as programming language allows it to by cross-platform and have access to a multitude of scientific libraries.
Results: Using a flexible plugin architecture, it is possible to enhance functionality for specific purposes easily. Following features are already implemented:
* Ready usage of libraries, e.g. for complex network analysis (NetworkX) and data plotting (Matplotlib). More brain connectivity measures will be implemented in a future release (Rubinov et al, 2009).
* 3D View of networks with node positioning based on corresponding ROI surface patch. Other layouts possible.
* Picking functionality to select nodes, select edges, get more node information (ConnectomeWiki), toggle surface representations
* Interactive thresholding and modality selection of edge properties using filters
* Arbitrary metadata can be stored for networks, thereby allowing e.g. group-based analysis or meta-analysis.
* Python Shell for scripting. Application data is exposed and can be modified or used for further post-processing.
* Visualization pipelines using filters and modules can be composed with Mayavi (Ramachandran et al, 2008).
* Interface to TrackVis to visualize track data. Selected nodes are converted to ROIs for fiber filtering
The Connectome Mapping Pipeline (Hagmann et al, 2008) processed 20 healthy subjects into an average Connectome dataset. The Figures show the ConnectomeViewer user interface using this dataset. Connections are shown that occur in all 20 subjects. The dataset is freely available from the homepage (connectomeviewer.org).
Conclusions: The ConnectomeViewer is a cross-platform, open-source software tool that provides extensive visualization and analysis capabilities for connectomic research. It has a modular architecture, integrates relevant datatypes and is completely scriptable. Visit www.connectomics.org to get involved as user or developer.