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Published on: April 15, 2015
Modular and hierarchically modular organization of brain networks
David Meunier1, Renaud Lambiotte, Edward T Bullmore
1Centre for Speech, Language and the Brain, Department of Experimental Psychology, University of Cambridge Cambridge, UK.
Frontiers in Neuroscience
|December 15, 2010
Summary
Brain networks exhibit modular and hierarchical structures, enhancing robustness and adaptivity. This modularity, analyzed using mathematical concepts, is crucial for understanding brain function from neuroimaging data.
Area of Science:
- Neuroscience
- Network Science
- Computational Biology
Background:
- Brain networks are complex information processing systems.
- These networks exhibit a modular community structure, where highly interconnected nodes form modules with sparse inter-modular connections.
- Brain networks often display hierarchical modularity, with nested modules across multiple scales.
Purpose of the Study:
- To review mathematical concepts for analyzing modularity in brain networks.
- To summarize recent research on modularity in structural and functional brain networks.
- To highlight the advantages of modular and hierarchical organization for brain function.
Main Methods:
- Review of mathematical concepts for quantitative analysis of modularity.
- Synthesis of recent studies on brain network modularity.
- Analysis of human neuroimaging data to derive structural and functional brain networks.
Main Results:
- Modular and hierarchical organization is a fundamental property of brain networks.
- Inter-modular connections in brain networks are often long-distance.
- Modular organization confers advantages such as robustness, adaptivity, and evolvability.
Conclusions:
- Modularity and hierarchical modularity are key organizational principles in brain networks.
- Quantitative analysis of modularity is essential for understanding brain network function.
- Recent neuroimaging studies confirm the prevalence of modularity in human brain networks.
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