Modular and hierarchically modular organization of brain networks
Brain networks are increasingly understood as one of a large class of information processing systems that share important organizational principles in common, including the property of a modular community structure. A module is topologically defined as a subset of highly inter-connected nodes which...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2010-12-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnins.2010.00200/full |
| _version_ | 1819266884059529216 |
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| author | David eMeunier Renaud eLambiotte Edward T Bullmore |
| author_facet | David eMeunier Renaud eLambiotte Edward T Bullmore |
| author_sort | David eMeunier |
| collection | DOAJ |
| description | Brain networks are increasingly understood as one of a large class of information processing systems that share important organizational principles in common, including the property of a modular community structure. A module is topologically defined as a subset of highly inter-connected nodes which are relatively sparsely connected to nodes in other modules. In brain networks, topological modules are often made up of anatomically neighbouring and/or functionally related cortical regions, and inter-modular connections tend to be relatively long distance. Moreover, brain networks and many other complex systems demonstrate the property of hierarchical modularity, or modularity on several topological scales: within each module there will be a set of sub-modules, and within each sub-module a set of sub-sub-modules, etc. There are several general advantages to modular and hierarchically modular network organization, including greater robustness, adaptivity and evolvability of network function. In this context, we review some of the mathematical concepts available for quantitative analysis of (hierarchical) modularity in brain networks and we summarise some of the recent work investigating modularity of structural and functional brain networks derived from analysis of human neuroimaging data. |
| first_indexed | 2024-12-23T21:08:21Z |
| format | Article |
| id | doaj.art-d844d363ef394e7280d4ddf2be5e6024 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-23T21:08:21Z |
| publishDate | 2010-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-d844d363ef394e7280d4ddf2be5e60242022-12-21T17:31:09ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2010-12-01410.3389/fnins.2010.002007572Modular and hierarchically modular organization of brain networksDavid eMeunier0Renaud eLambiotte1Edward T Bullmore2University of CambridgeImperial CollegeUniversity of CambridgeBrain networks are increasingly understood as one of a large class of information processing systems that share important organizational principles in common, including the property of a modular community structure. A module is topologically defined as a subset of highly inter-connected nodes which are relatively sparsely connected to nodes in other modules. In brain networks, topological modules are often made up of anatomically neighbouring and/or functionally related cortical regions, and inter-modular connections tend to be relatively long distance. Moreover, brain networks and many other complex systems demonstrate the property of hierarchical modularity, or modularity on several topological scales: within each module there will be a set of sub-modules, and within each sub-module a set of sub-sub-modules, etc. There are several general advantages to modular and hierarchically modular network organization, including greater robustness, adaptivity and evolvability of network function. In this context, we review some of the mathematical concepts available for quantitative analysis of (hierarchical) modularity in brain networks and we summarise some of the recent work investigating modularity of structural and functional brain networks derived from analysis of human neuroimaging data.http://journal.frontiersin.org/Journal/10.3389/fnins.2010.00200/fullfractalCortexgraphnear-decomposabilitypartition |
| spellingShingle | David eMeunier Renaud eLambiotte Edward T Bullmore Modular and hierarchically modular organization of brain networks Frontiers in Neuroscience fractal Cortex graph near-decomposability partition |
| title | Modular and hierarchically modular organization of brain networks |
| title_full | Modular and hierarchically modular organization of brain networks |
| title_fullStr | Modular and hierarchically modular organization of brain networks |
| title_full_unstemmed | Modular and hierarchically modular organization of brain networks |
| title_short | Modular and hierarchically modular organization of brain networks |
| title_sort | modular and hierarchically modular organization of brain networks |
| topic | fractal Cortex graph near-decomposability partition |
| url | http://journal.frontiersin.org/Journal/10.3389/fnins.2010.00200/full |
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