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Cited by 96Functional Brain Connectivity StudiesAdvanced Neuroimaging Techniques and ApplicationsComplex Network Analysis Techniques

Inferring neural signalling directionality from undirected structural connectomes

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Seguin, Caio, Razi, Adeel, Zalesky, Andrew·2019·Nature Communications
DOI 10.1038/s41467-019-12201-wPMC PMC6753104

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Abstract

Neural information flow is inherently directional. To date, investigation of directional communication in the human structural connectome has been precluded by the inability of non-invasive neuroimaging methods to resolve axonal directionality. Here, we demonstrate that decentralized measures of network communication, applied to the undirected topology and geometry of brain networks, can infer putative directions of large-scale neural signalling. We propose the concept of send-receive communication asymmetry to characterize cortical regions as senders, receivers or neutral, based on differences between their incoming and outgoing communication efficiencies. Our results reveal a send-receive cortical hierarchy that recapitulates established organizational gradients differentiating sensory-motor and multimodal areas. We find that send-receive asymmetries are significantly associated with the directionality of effective connectivity derived from spectral dynamic causal modeling. Finally, using fruit fly, mouse and macaque connectomes, we provide further evidence suggesting that directionality of neural signalling is significantly encoded in the undirected architecture of nervous systems.

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  • Graph measures computed on the undirected human connectome can reveal patterns of asymmetric communication, as evidenced by associations with effective connectivity directionality from dynamic causal modeling.

    “For instance, despite the inherent limitations of dMRI tractography in resolving axonal fiber directionality, graph measures computed on the undirected human connectome can reveal patterns of asymmetric communication, as evidenced by significant associations to the directionality of effective connectivity computed using dynamic causal modeling (Seguin et al., 2019).”

    From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Seguin et al., 2019

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Cited by

Sorted by citations

Authors
Seguin, C., Razi, A., & Zalesky, A.
Affiliations
  • The University of Melbourne (AU)
  • NED University of Engineering and Technology (PK)
Corresponding author
Seguin, Caio
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2019-09-19
Volume
10
Issue
1
Pages
4289-4289
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-019-12201-w
PMC
PMC6753104
OpenAlex
W2973995848
Cited by
96
References
10
Citations by year
2026: 2 · 2025: 16 · 2024: 18 · 2023: 20 · 2022: 15 · 2021: 14 · 2020: 9 · 2019: 2
FWCI
5.81
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Minerva Access (University of Melbourne) (submittedVersion, cc-by)
  • Monash University Research Portal (Monash University) (submittedVersion, other-oa)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • UCL Discovery (University College London) (submittedVersion)
Funders
  • National Institutes of Health (5u54mh091657-05, 1U54‐MH‐091657)
  • McDonnell Center for Systems Neuroscience (1U54MH091657)
  • Medical Research Council (1U54MH091657, 1136649)
  • Australian Research Council (DE170100128, 1U54MH091657)
  • National Health and Medical Research Council (1136649)
  • National Institute of Mental Health (U54-MH091657)
  • NIH Blueprint for Neuroscience Research (1U54-MH-091657)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
Keywords
structural connectome, communication efficiency, cortical hierarchy, effective connectivity, dynamic causal modeling, brain network topology
MeSH terms
Adult, Animals, Brain, Cell Communication, Drosophila, Female, Humans, Macaca, Male, Models, Neurological, Nerve Net, Neural Pathways, Signal Transduction, Mice, Young Adult, Connectome

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