EEG-based functional brain networks: does the network size matter?
Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. These networks--whose nodes can vary from tens to hundreds--are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which various g...
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doaj-8e1739ad84ad43a1ac0aaa0020284b9e2021-03-04T00:49:38ZengPublic Library of Science (PLoS)PLoS ONE1932-62032012-01-0174e3567310.1371/journal.pone.0035673EEG-based functional brain networks: does the network size matter?Amir JoudakiNiloufar SalehiMahdi JaliliMaria G KnyazevaFunctional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. These networks--whose nodes can vary from tens to hundreds--are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which various graph metrics depend upon the network size. To this end, EEGs from 32 normal subjects were recorded and functional networks of three different sizes were extracted. A state-space based method was used to calculate cross-correlation matrices between different brain regions. These correlation matrices were used to construct binary adjacency connectomes, which were assessed with regards to a number of graph metrics such as clustering coefficient, modularity, efficiency, economic efficiency, and assortativity. We showed that the estimates of these metrics significantly differ depending on the network size. Larger networks had higher efficiency, higher assortativity and lower modularity compared to those with smaller size and the same density. These findings indicate that the network size should be considered in any comparison of networks across studies.https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22558196/?tool=EBI |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Amir Joudaki Niloufar Salehi Mahdi Jalili Maria G Knyazeva |
spellingShingle |
Amir Joudaki Niloufar Salehi Mahdi Jalili Maria G Knyazeva EEG-based functional brain networks: does the network size matter? PLoS ONE |
author_facet |
Amir Joudaki Niloufar Salehi Mahdi Jalili Maria G Knyazeva |
author_sort |
Amir Joudaki |
title |
EEG-based functional brain networks: does the network size matter? |
title_short |
EEG-based functional brain networks: does the network size matter? |
title_full |
EEG-based functional brain networks: does the network size matter? |
title_fullStr |
EEG-based functional brain networks: does the network size matter? |
title_full_unstemmed |
EEG-based functional brain networks: does the network size matter? |
title_sort |
eeg-based functional brain networks: does the network size matter? |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2012-01-01 |
description |
Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. These networks--whose nodes can vary from tens to hundreds--are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which various graph metrics depend upon the network size. To this end, EEGs from 32 normal subjects were recorded and functional networks of three different sizes were extracted. A state-space based method was used to calculate cross-correlation matrices between different brain regions. These correlation matrices were used to construct binary adjacency connectomes, which were assessed with regards to a number of graph metrics such as clustering coefficient, modularity, efficiency, economic efficiency, and assortativity. We showed that the estimates of these metrics significantly differ depending on the network size. Larger networks had higher efficiency, higher assortativity and lower modularity compared to those with smaller size and the same density. These findings indicate that the network size should be considered in any comparison of networks across studies. |
url |
https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22558196/?tool=EBI |
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