High-Resolution Directed Human Connectomes and the Consensus Connectome\n Dynamics

Here we show a method of directing the edges of the connectomes, prepared\nfrom diffusion tensor imaging (DTI) datasets from the human brain. Before the\npresent work, no high-definition directed braingraphs (or connectomes) were\npublished, because the tractography methods in use are not capable of assigning\ndirections to the neural tracts discovered. Previous work on the functional\nconnectomes applied low-resolution functional MRI-detected statistical\ncausality for the assignment of directions of connectomes of typically several\ndozens of vertices. Our method is based on the phenomenon of the "Consensus\nConnectome Dynamics" (CCD), described earlier by our research group. In this\ncontribution, we apply the method to the 423 braingraphs, each with 1015\nvertices, computed from the public release of the Human Connectome Project, and\nwe also made the directed connectomes publicly available at the site\n\\url{http://braingraph.org}. We also show the robustness of our edge directing\nmethod in four independently chosen connectome datasets: we have found that\n86\\% of the edges, which were present in all four datasets, get the very same\ndirections in all datasets; therefore the direction method is robust, it does\nnot depend on the particular choice of the dataset. We think that our present\ncontribution opens up new possibilities in the analysis of the high-definition\nhuman connectome: from now on we can work with a robust assignment of\ndirections of the connections of the human brain.\n