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Related Experiment Video

Updated: May 11, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Principal networks.

Jonathan D Clayden1, Michael Dayan, Chris A Clark

  • 1Institute of Child Health, University College London, London, United Kingdom. j.clayden@ucl.ac.uk

Plos One
|May 1, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for identifying brain networks using graph theory and eigendecomposition. The principal networks found are stable, reproducible, and meaningful for understanding brain connectivity.

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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

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Last Updated: May 11, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Area of Science:

  • Neuroimaging
  • Network Neuroscience
  • Graph Theory

Background:

  • Brain connectivity research increasingly uses graph representations.
  • Existing methods for subnetwork identification have limitations in neuroimaging.
  • Cognitive functions involve specific, not all, brain regions.

Purpose of the Study:

  • To present a novel, simple approach for decomposing brain connectivity graphs into coherent principal networks.
  • To address limitations of existing methods for subnetwork identification in neuroimaging.
  • To enable calculation of network cost and efficiency for individual subnetworks.

Main Methods:

  • Eigendecomposition of the association matrix to identify principal networks.
  • Application of the technique to cortical thickness and diffusion tractography data.
  • Related to principal components analysis for graph decomposition.

Main Results:

  • The developed technique yields stable, meaningful, and reproducible subnetworks.
  • All available connectivity information is utilized, with vertices potentially belonging to multiple subnetworks.
  • Subject-specific scores for principal networks can be derived and related to other variables.

Conclusions:

  • The eigendecomposition approach offers a robust method for brain network analysis.
  • Principal networks provide a framework for studying network cost and efficiency.
  • This method enhances the understanding of brain connectivity and its relation to cognitive functions.