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

Autor: Tanner J; Cognitive Science Program, Indiana University, Bloomington, IN, USA.; School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA., Faskowitz J; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA., Teixeira AS; LASIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal., Seguin C; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA., Coletta L; Fondazione Bruno Kessler, Trento, Italy., Gozzi A; Functional Neuroimaging Lab, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems, Rovereto, Italy., Mišić B; McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada., Betzel RF; Cognitive Science Program, Indiana University, Bloomington, IN, USA. rbetzel@indiana.edu.; School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA. rbetzel@indiana.edu.; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA. rbetzel@indiana.edu.; Program in Neuroscience, Indiana University, Bloomington, IN, USA. rbetzel@indiana.edu.
Jazyk: angličtina
Zdroj: Nature communications [Nat Commun] 2024 Jul 12; Vol. 15 (1), pp. 5865. Date of Electronic Publication: 2024 Jul 12.
DOI: 10.1038/s41467-024-50248-6
Abstrakt: 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
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