High-Resolution Directed Human Connectomes and the Consensus Connectome Dynamics
Bal\'azs Szalkai, Csaba Kerepesi, B\'alint Varga, Vince, Grolmusz

TL;DR
This paper introduces a novel method to assign directions to high-resolution human brain connectomes derived from diffusion tensor imaging, enabling more detailed analysis of neural pathways.
Contribution
The authors present a new approach based on Consensus Connectome Dynamics to direct edges in high-definition connectomes, which was not previously possible with existing tractography methods.
Findings
86% of common edges have consistent directions across datasets
Directed connectomes are publicly available for further research
Method is robust across multiple independent datasets
Abstract
Here we show a method of directing the edges of the connectomes, prepared from diffusion tensor imaging (DTI) datasets from the human brain. Before the present work, no high-definition directed braingraphs (or connectomes) were published, because the tractography methods in use are not capable of assigning directions to the neural tracts discovered. Previous work on the functional connectomes applied low-resolution functional MRI-detected statistical causality for the assignment of directions of connectomes of typically several dozens of vertices. Our method is based on the phenomenon of the "Consensus Connectome Dynamics" (CCD), described earlier by our research group. In this contribution, we apply the method to the 423 braingraphs, each with 1015 vertices, computed from the public release of the Human Connectome Project, and we also made the directed connectomes publicly available at…
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