A multi-modal, asymmetric, weighted, and signed description of anatomical connectivity.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
12 Jul 2024
12 Jul 2024
Historique:
received:
08
08
2023
accepted:
01
07
2024
medline:
13
7
2024
pubmed:
13
7
2024
entrez:
12
7
2024
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 38997282
doi: 10.1038/s41467-024-50248-6
pii: 10.1038/s41467-024-50248-6
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
5865Informations de copyright
© 2024. The Author(s).
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