  Currently, connectomes (e.g., functional or structural brain graphs) can be
estimated in humans at $\approx 1~mm^3$ scale using a combination of diffusion
weighted magnetic resonance imaging, functional magnetic resonance imaging and
structural magnetic resonance imaging scans. This manuscript summarizes a
novel, scalable implementation of open-source algorithms to rapidly estimate
magnetic resonance connectomes, using both anatomical regions of interest
(ROIs) and voxel-size vertices. To assess the reliability of our pipeline, we
develop a novel nonparametric non-Euclidean reliability metric. Here we provide
an overview of the methods used, demonstrate our implementation, and discuss
available user extensions. We conclude with results showing the efficacy and
reliability of the pipeline over previous state-of-the-art.
