%0 Journal Article
%T A multi-modal, asymmetric, weighted, and signed description of anatomical connectivity.
%A Tanner J
%A Faskowitz J
%A Teixeira AS
%A Seguin C
%A Coletta L
%A Gozzi A
%A Mišić B
%A Betzel RF
%J Nat Commun
%V 15
%N 1
%D 2024 Jul 12
%M 38997282
%F 17.694
%R 10.1038/s41467-024-50248-6
%X 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.