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Related Concept Videos

Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Circuit Terminology01:14

Circuit Terminology

An electrical network is a system composed of interconnected elements, such as resistors, capacitors, inductors, and voltage or current sources. Unlike a circuit, an electrical network does not necessarily form a closed path. In other words, while all circuits can be considered networks due to their interconnected nature, not every network qualifies as a circuit.
A circuit, on the other hand, is also an interconnected system of electrical elements but must contain one or more closed paths.

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Related Experiment Video

Updated: May 8, 2026

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

Extracting labeled topological patterns from samples of networks.

Christoph Schmidt1, Thomas Weiss, Thomas Lehmann

  • 1Bernstein Group for Computational Neuroscience Jena, Institute of Medical Statistics, Computer Sciences and Documentation, Jena University Hospital, Friedrich Schiller University Jena, Jena, Germany. christoph.schmidt@med.uni-jena.de

Plos One
|August 17, 2013
PubMed
Summary

This study introduces a novel graph theory method to analyze network patterns, simplifying complex data for better functional interpretation. This approach helps differentiate network samples and understand brain connectivity changes during pain in major depression.

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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Area of Science:

  • Graph theory
  • Network analysis
  • Neuroscience

Background:

  • Analyzing samples of directed networks with fixed vertex labels presents challenges due to complex topological information.
  • Existing methods often struggle with direct evaluation and interpretation of intricate network patterns.

Purpose of the Study:

  • To introduce an advanced graph theoretical approach for functional interpretation of directed network samples.
  • To simplify complex topological information and characterize network samples using characteristic topological patterns.
  • To enable differentiation between network samples based on these identified patterns.

Main Methods:

  • Developed a graph theoretical approach to identify and characterize locatable, sample-specific network motifs with vertex labeling.
  • Utilized a null model to assign statistical significance to identified topological patterns.
  • Applied the approach to analyze brain connectivity networks in patients with major depression and healthy subjects before and during painful stimulation.

Main Results:

  • The new approach successfully simplifies complex topological information within network samples.
  • Characteristic topological patterns were identified, serving as discriminators between different network populations.
  • The method enabled a functional interpretation of altered neuronal processing of pain in major depression patients and healthy controls.

Conclusions:

  • The advanced graph theoretical approach provides a robust method for analyzing and interpreting complex network data.
  • This technique offers insights into the underlying functional mechanisms by identifying significant topological patterns.
  • The study demonstrates the utility of this approach in understanding neurobiological processes, specifically altered pain processing in major depression.