On the macroscale level of description, diffusion-weighted magnetic resonance imaging (MRI) is the main imaging technology employed for mapping the structural connectivity of the human connectome (Hagmann, 2005; Sporns et al., 2005; Sporns, 2011) . Magnetic resonance connectomics is increasingly recognized as a tool for basic and clinical neuroscience. Several methodological advances in image acquisition, reconstruction, and tractography Johansen-Berg and Behrens, 2009) suggest that automated processing pipelines will make it possible to generate comprehensive in vivo whole brain statistical connectomes. Despite the big differences in spatial scale and data size, both levels of connectome mapping consist of similar stages. Connectome mapping workflows involve image acquisition, registration and segmentation, data organization and sharing, high-throughput pipelining, analysis, and visualization. Advanced neuroinformatics tools will be required to meet challenges that each stage presents. In this article, we will focus on the development of neuroinformatics tools in the emerging field of macroscale connectomics. The efficiency of sharing data and source code would benefit if a transdisciplinary lingua franca for programming was available. Especially in the neurosciences, where researchers with varying degrees of scientific knowledge and programming skills meet, a common programming language helps to bridge gaps between theoretical and experimental worlds of investigation. Moreover, the programming language must be high-level, cross-platform,
Gerhard et al. introduce the Connectome Viewer Toolkit for connectome mapping, analysis, and visualization REF .
The Connectome Viewer Toolkit: An Open Source Framework to Manage, Analyze, and Visualize Connectomes
Front. Neuroinform.
Computer Science Medicine