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Updated: Sep 11, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
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Edge-centric network control on the human brain structural network.

Huili Sun1, Matthew Rosenblatt1, Javid Dadashkarimi2,3

  • 1Department of Biomedical Engineering, Yale University, New Haven, CT, United States.

Imaging Neuroscience (Cambridge, Mass.)
|August 13, 2025
PubMed
Summary
This summary is machine-generated.

Edge-centric network control theory reveals how brain connectivity influences cognitive states. This approach offers a more energy-efficient understanding of brain dynamics and predicts individual differences in executive function.

Keywords:
brain structurediffusion MRIedge-centric networknetwork control

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Area of Science:

  • Neuroscience
  • Network Science
  • Computational Biology

Background:

  • Network control theory models brain state transitions via gray matter regions and white matter connections.
  • Existing models primarily use node-centric approaches, neglecting the role of network connections (edges).

Purpose of the Study:

  • To introduce and apply edge-centric network control theory (E-NCT) to understand the role of brain connectivity in governing brain dynamics.
  • To assess how edge controllability relates to phenotypic information and brain energy consumption.

Main Methods:

  • Applied E-NCT to diffusion MRI data from the Human Connectome Project.
  • Validated edge controllability against null models, node controllability, and structural/functional connectomes.
  • Estimated brain energy consumption for network activation using E-NCT.

Main Results:

  • Edge controllability predicted individual differences in phenotypic information.
  • E-NCT revealed that activating a complex, whole-brain network for executive function is more energy-efficient than canonical network pairs.
  • Edge controllability captured control energy patterns and brain-behavior phenotypes.

Conclusions:

  • E-NCT provides a novel edge-centric perspective on brain network control mechanisms.
  • This framework enhances the understanding of brain dynamics, control energy, and brain-behavior relationships.