{Reference Type}: Journal Article
{Title}: A multi-modal, asymmetric, weighted, and signed description of anatomical connectivity.
{Author}: Tanner J;Faskowitz J;Teixeira AS;Seguin C;Coletta L;Gozzi A;Mišić B;Betzel RF;
{Journal}: Nat Commun
{Volume}: 15
{Issue}: 1
{Year}: 2024 Jul 12
{Factor}: 17.694
{DOI}: 10.1038/s41467-024-50248-6
{Abstract}: The macroscale connectome is the network of physical, white-matter tracts between brain areas. The connections are generally weighted and their values interpreted as measures of communication efficacy. In most applications, weights are either assigned based on imaging features-e.g. diffusion parameters-or inferred using statistical models. In reality, the ground-truth weights are unknown, motivating the exploration of alternative edge weighting schemes. Here, we explore a multi-modal, regression-based model that endows reconstructed fiber tracts with directed and signed weights. We find that the model fits observed data well, outperforming a suite of null models. The estimated weights are subject-specific and highly reliable, even when fit using relatively few training samples, and the networks maintain a number of desirable features. In summary, we offer a simple framework for weighting connectome data, demonstrating both its ease of implementation while benchmarking its utility for typical connectome analyses, including graph theoretic modeling and brain-behavior associations.