Distance-dependent distribution thresholding in probabilistic tractography

Tractography is widely used in human studies of connectivity with respect to every brain region, function, and is explored developmentally, in adulthood, ageing, and in disease. However, the core issue of how to systematically threshold, taking into account the inherent differences in connectivity values for different track lengths, and to do this in a comparable way across studies has not been solved. By utilising 54 healthy individuals' diffusion-weighted image data taken from HCP, this study adopted Monte Carlo derived distance-dependent distributions (DDDs) to generate distance-dependent thresholds with various levels of alpha for connections of varying lengths. As a test case, we applied the DDD approach to generate a language connectome. The resulting connectome showed both short- and long-distance structural connectivity in the close and distant regions as expected for the dorsal and ventral language pathways, consistent with the literature. The finding demonstrates that the DDD approach is feasible to generate data-driven DDDs for common thresholding and can be used for both individual and group thresholding. Critically, it offers a standard method that can be applied to various probabilistic tracking datasets.

Automated summary

Tractography is widely used in studying human brain connectivity, but the issue of systematically thresholding and comparing connectivity values for different track lengths across studies remains unsolved. This study utilized Monte Carlo derived distance-dependent distributions (DDDs) to generate thresholds for connections of varying lengths. The approach was applied to generate a language connectome, which showed expected structural connectivity. The findings demonstrate the feasibility and applicability of the DDD approach for thresholding probabilistic tracking datasets.