The paper “A multi-modal, asymmetric, weighted, and signed description of anatomical connectivity”, co-authored by LASIGE’s integrated researcher Andreia Sofia Teixeira, was published in Nature Communications (SCIMAGO Q1). This study was led by researchers at Indiana University.

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. In this paper, the researchers explore a multi-modal, regression-based model that endows reconstructed fiber tracts with directed and signed weights. They 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, this approach 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.

The paper is available here.