A multi-modal, asymmetric, weighted, and signed description of anatomical connectivity

Jacob Tanner, Joshua Faskowitz, Andreia Sofia Teixeira, Caio Seguin, Ludovico Coletta, Alessandro Gozzi, Bratislav Mišić and Richard F. Betzel ()
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Jacob Tanner: Indiana University
Joshua Faskowitz: Indiana University
Andreia Sofia Teixeira: Universidade de Lisboa
Caio Seguin: Indiana University
Ludovico Coletta: Fondazione Bruno Kessler
Alessandro Gozzi: Center for Neuroscience and Cognitive Systems
Bratislav Mišić: McGill University
Richard F. Betzel: Indiana University

Nature Communications, 2024, vol. 15, issue 1, 1-18

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-50248-6

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