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, Richard F. Betzel

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number5865
JournalNature communications
Volume15
Issue number1
DOIs
StatePublished - Dec 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© The Author(s) 2024.

PubMed: MeSH publication types

  • Journal Article

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