Reproducibility and robustness of graph measures of the associative-semantic network
Graph analysis is a promising tool to quantify brain connectivity. However, an essential requirement is that the graph measures are reproducible and robust. We have studied the reproducibility and robustness of various graph measures in group based and in individual binary and weighted networks deri...
| Main Authors: | , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Public Library of Science
2014
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| _version_ | 1797081939050496000 |
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| author | Wang, Y Nelissen, N Adamczuk, K De Weer, A Vandenbulcke, M Sunaert, S Vandenberghe, R Dupont, P |
| author_facet | Wang, Y Nelissen, N Adamczuk, K De Weer, A Vandenbulcke, M Sunaert, S Vandenberghe, R Dupont, P |
| author_sort | Wang, Y |
| collection | OXFORD |
| description | Graph analysis is a promising tool to quantify brain connectivity. However, an essential requirement is that the graph measures are reproducible and robust. We have studied the reproducibility and robustness of various graph measures in group based and in individual binary and weighted networks derived from a task fMRI experiment during explicit associative-semantic processing of words and pictures. The nodes of the network were defined using an independent study and the connectivity was based on the partial correlation of the time series between any pair of nodes. The results showed that in case of binary networks, global graph measures exhibit a good reproducibility and robustness for networks which are not too sparse and these figures of merit depend on the graph measure and on the density of the network. Furthermore, group based binary networks should be derived from groups of sufficient size and the lower the density the more subjects are required to obtain robust values. Local graph measures are very variable in terms of reproducibility and should be interpreted with care. For weighted networks, we found good reproducibility (average test-retest variability <5% and ICC values >0.4) when using subject specific networks and this will allow us to relate network properties to individual subject information. |
| first_indexed | 2024-03-07T01:21:03Z |
| format | Journal article |
| id | oxford-uuid:9057d2c7-8c64-4213-972f-43d98428e037 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T01:21:03Z |
| publishDate | 2014 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | oxford-uuid:9057d2c7-8c64-4213-972f-43d98428e0372022-03-26T23:11:03ZReproducibility and robustness of graph measures of the associative-semantic networkJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:9057d2c7-8c64-4213-972f-43d98428e037EnglishSymplectic Elements at OxfordPublic Library of Science2014Wang, YNelissen, NAdamczuk, KDe Weer, AVandenbulcke, MSunaert, SVandenberghe, RDupont, PGraph analysis is a promising tool to quantify brain connectivity. However, an essential requirement is that the graph measures are reproducible and robust. We have studied the reproducibility and robustness of various graph measures in group based and in individual binary and weighted networks derived from a task fMRI experiment during explicit associative-semantic processing of words and pictures. The nodes of the network were defined using an independent study and the connectivity was based on the partial correlation of the time series between any pair of nodes. The results showed that in case of binary networks, global graph measures exhibit a good reproducibility and robustness for networks which are not too sparse and these figures of merit depend on the graph measure and on the density of the network. Furthermore, group based binary networks should be derived from groups of sufficient size and the lower the density the more subjects are required to obtain robust values. Local graph measures are very variable in terms of reproducibility and should be interpreted with care. For weighted networks, we found good reproducibility (average test-retest variability <5% and ICC values >0.4) when using subject specific networks and this will allow us to relate network properties to individual subject information. |
| spellingShingle | Wang, Y Nelissen, N Adamczuk, K De Weer, A Vandenbulcke, M Sunaert, S Vandenberghe, R Dupont, P Reproducibility and robustness of graph measures of the associative-semantic network |
| title | Reproducibility and robustness of graph measures of the associative-semantic network |
| title_full | Reproducibility and robustness of graph measures of the associative-semantic network |
| title_fullStr | Reproducibility and robustness of graph measures of the associative-semantic network |
| title_full_unstemmed | Reproducibility and robustness of graph measures of the associative-semantic network |
| title_short | Reproducibility and robustness of graph measures of the associative-semantic network |
| title_sort | reproducibility and robustness of graph measures of the associative semantic network |
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