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Related Experiment Video

Updated: Jan 19, 2026

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Inferring neural signalling directionality from undirected structural connectomes.

Caio Seguin¹, Adeel Razi^2,3,4, Andrew Zalesky^5,6

¹Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, VIC, 3010, Australia. caioseguin@gmail.com.

Nature Communications

|September 21, 2019

Summary

Researchers can now infer the direction of neural communication in the brain using network analysis of structural connectomes. This new method reveals a cortical hierarchy and is supported by cross-species data.

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Last Updated: Jan 19, 2026

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

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Published on: November 8, 2012

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Area of Science:

Neuroscience
Network Science
Computational Biology

Background:

Neural information flow is directional, but non-invasive neuroimaging cannot resolve axonal directionality.
Investigating directional communication in the human structural connectome remains a challenge.

Purpose of the Study:

To develop a method for inferring directional neural signaling from undirected brain network topology and geometry.
To characterize cortical regions based on their communication roles (sender, receiver, neutral).

Main Methods:

Applied decentralized network communication measures to undirected brain network topology and geometry.
Introduced the concept of send-receive communication asymmetry.
Validated findings using spectral dynamic causal modeling and cross-species connectomes (fruit fly, mouse, macaque).

Main Results:

Successfully inferred putative directions of large-scale neural signaling.
Revealed a send-receive cortical hierarchy aligning with known sensory-motor and multimodal area gradients.
Demonstrated significant association between send-receive asymmetries and effective connectivity directionality.
Provided cross-species evidence that neural signaling direction is encoded in undirected nervous system architecture.

Conclusions:

Decentralized network measures can infer neural signaling directionality from undirected connectomes.
Send-receive asymmetry provides a framework for understanding cortical communication hierarchies.
The findings suggest a fundamental principle of neural organization across species.