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Resiliency of EEG-Based Brain Functional Networks

Overview of attention for article published in PLOS ONE, August 2015
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Title
Resiliency of EEG-Based Brain Functional Networks
Published in
PLOS ONE, August 2015
DOI 10.1371/journal.pone.0135333
Pubmed ID
Authors

Mahdi Jalili

Abstract

Applying tools available in network science and graph theory to study brain networks has opened a new era in understanding brain mechanisms. Brain functional networks extracted from EEG time series have been frequently studied in health and diseases. In this manuscript, we studied failure resiliency of EEG-based brain functional networks. The network structures were extracted by analysing EEG time series obtained from 30 healthy subjects in resting state eyes-closed conditions. As the network structure was extracted, we measured a number of metrics related to their resiliency. In general, the brain networks showed worse resilient behaviour as compared to corresponding random networks with the same degree sequences. Brain networks had higher vulnerability than the random ones (P < 0.05), indicating that their global efficiency (i.e., communicability between the regions) is more affected by removing the important nodes. Furthermore, the breakdown happened as a result of cascaded failures in brain networks was severer (i.e., less nodes survived) as compared to randomized versions (P < 0.05). These results suggest that real EEG-based networks have not been evolved to possess optimal resiliency against failures.

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The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
United States 1 2%
Unknown 40 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 31%
Researcher 5 12%
Student > Doctoral Student 4 10%
Professor > Associate Professor 4 10%
Student > Master 4 10%
Other 7 17%
Unknown 5 12%
Readers by discipline Count As %
Psychology 11 26%
Computer Science 4 10%
Medicine and Dentistry 3 7%
Physics and Astronomy 3 7%
Engineering 3 7%
Other 7 17%
Unknown 11 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 August 2015.
All research outputs
#14,989,411
of 23,972,543 outputs
Outputs from PLOS ONE
#126,460
of 204,707 outputs
Outputs of similar age
#141,157
of 269,292 outputs
Outputs of similar age from PLOS ONE
#3,302
of 5,943 outputs
Altmetric has tracked 23,972,543 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 204,707 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.5. This one is in the 35th percentile – i.e., 35% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 269,292 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 5,943 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.