Validation of network communicability metrics for the analysis of brain structural networks.
Computational network analysis provides new methods to analyze the brain's structural organization based on diffusion imaging tractography data. Networks are characterized by global and local metrics that have recently given promising insights into diagnosis and the further understanding of psy...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2014-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC4280193?pdf=render |
| _version_ | 1819146439993851904 |
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| author | Jennifer Andreotti Kay Jann Lester Melie-Garcia Stéphanie Giezendanner Eugenio Abela Roland Wiest Thomas Dierks Andrea Federspiel |
| author_facet | Jennifer Andreotti Kay Jann Lester Melie-Garcia Stéphanie Giezendanner Eugenio Abela Roland Wiest Thomas Dierks Andrea Federspiel |
| author_sort | Jennifer Andreotti |
| collection | DOAJ |
| description | Computational network analysis provides new methods to analyze the brain's structural organization based on diffusion imaging tractography data. Networks are characterized by global and local metrics that have recently given promising insights into diagnosis and the further understanding of psychiatric and neurologic disorders. Most of these metrics are based on the idea that information in a network flows along the shortest paths. In contrast to this notion, communicability is a broader measure of connectivity which assumes that information could flow along all possible paths between two nodes. In our work, the features of network metrics related to communicability were explored for the first time in the healthy structural brain network. In addition, the sensitivity of such metrics was analysed using simulated lesions to specific nodes and network connections. Results showed advantages of communicability over conventional metrics in detecting densely connected nodes as well as subsets of nodes vulnerable to lesions. In addition, communicability centrality was shown to be widely affected by the lesions and the changes were negatively correlated with the distance from lesion site. In summary, our analysis suggests that communicability metrics that may provide an insight into the integrative properties of the structural brain network and that these metrics may be useful for the analysis of brain networks in the presence of lesions. Nevertheless, the interpretation of communicability is not straightforward; hence these metrics should be used as a supplement to the more standard connectivity network metrics. |
| first_indexed | 2024-12-22T13:13:57Z |
| format | Article |
| id | doaj.art-0c56dfcb105641d5a19ecd51fe74a4b4 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-22T13:13:57Z |
| publishDate | 2014-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-0c56dfcb105641d5a19ecd51fe74a4b42022-12-21T18:24:40ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-01912e11550310.1371/journal.pone.0115503Validation of network communicability metrics for the analysis of brain structural networks.Jennifer AndreottiKay JannLester Melie-GarciaStéphanie GiezendannerEugenio AbelaRoland WiestThomas DierksAndrea FederspielComputational network analysis provides new methods to analyze the brain's structural organization based on diffusion imaging tractography data. Networks are characterized by global and local metrics that have recently given promising insights into diagnosis and the further understanding of psychiatric and neurologic disorders. Most of these metrics are based on the idea that information in a network flows along the shortest paths. In contrast to this notion, communicability is a broader measure of connectivity which assumes that information could flow along all possible paths between two nodes. In our work, the features of network metrics related to communicability were explored for the first time in the healthy structural brain network. In addition, the sensitivity of such metrics was analysed using simulated lesions to specific nodes and network connections. Results showed advantages of communicability over conventional metrics in detecting densely connected nodes as well as subsets of nodes vulnerable to lesions. In addition, communicability centrality was shown to be widely affected by the lesions and the changes were negatively correlated with the distance from lesion site. In summary, our analysis suggests that communicability metrics that may provide an insight into the integrative properties of the structural brain network and that these metrics may be useful for the analysis of brain networks in the presence of lesions. Nevertheless, the interpretation of communicability is not straightforward; hence these metrics should be used as a supplement to the more standard connectivity network metrics.http://europepmc.org/articles/PMC4280193?pdf=render |
| spellingShingle | Jennifer Andreotti Kay Jann Lester Melie-Garcia Stéphanie Giezendanner Eugenio Abela Roland Wiest Thomas Dierks Andrea Federspiel Validation of network communicability metrics for the analysis of brain structural networks. PLoS ONE |
| title | Validation of network communicability metrics for the analysis of brain structural networks. |
| title_full | Validation of network communicability metrics for the analysis of brain structural networks. |
| title_fullStr | Validation of network communicability metrics for the analysis of brain structural networks. |
| title_full_unstemmed | Validation of network communicability metrics for the analysis of brain structural networks. |
| title_short | Validation of network communicability metrics for the analysis of brain structural networks. |
| title_sort | validation of network communicability metrics for the analysis of brain structural networks |
| url | http://europepmc.org/articles/PMC4280193?pdf=render |
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