Distance-dependent distribution thresholding in probabilistic tractography

Abstract

Tractography is widely used in human studies of connectivity with respect to every brain region, function, and is explored developmentally, in adulthood, aging, 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. The 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 expected short- and long-distance structural connectivity in the close and distant regions within the language network. The finding demonstrates that the DDD approach can be used for both individual and group thresholding. Critically, it offers a standard method that can be applied to various probabilistic tracking datasets.