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Graph theoretical analysis of complex networks in the brain

Overview of attention for article published in EPJ Nonlinear Biomedical Physics , July 2007
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)

Mentioned by

blogs
1 blog
twitter
3 X users
facebook
1 Facebook page
googleplus
1 Google+ user
reddit
2 Redditors

Citations

dimensions_citation
857 Dimensions

Readers on

mendeley
1118 Mendeley
citeulike
9 CiteULike
connotea
4 Connotea
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Title
Graph theoretical analysis of complex networks in the brain
Published in
EPJ Nonlinear Biomedical Physics , July 2007
DOI 10.1186/1753-4631-1-3
Pubmed ID
Authors

Cornelis J Stam, Jaap C Reijneveld

Abstract

Since the discovery of small-world and scale-free networks the study of complex systems from a network perspective has taken an enormous flight. In recent years many important properties of complex networks have been delineated. In particular, significant progress has been made in understanding the relationship between the structural properties of networks and the nature of dynamics taking place on these networks. For instance, the 'synchronizability' of complex networks of coupled oscillators can be determined by graph spectral analysis. These developments in the theory of complex networks have inspired new applications in the field of neuroscience. Graph analysis has been used in the study of models of neural networks, anatomical connectivity, and functional connectivity based upon fMRI, EEG and MEG. These studies suggest that the human brain can be modelled as a complex network, and may have a small-world structure both at the level of anatomical as well as functional connectivity. This small-world structure is hypothesized to reflect an optimal situation associated with rapid synchronization and information transfer, minimal wiring costs, as well as a balance between local processing and global integration. The topological structure of functional networks is probably restrained by genetic and anatomical factors, but can be modified during tasks. There is also increasing evidence that various types of brain disease such as Alzheimer's disease, schizophrenia, brain tumours and epilepsy may be associated with deviations of the functional network topology from the optimal small-world pattern.

X Demographics

X Demographics

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 1,118 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 26 2%
Netherlands 13 1%
Germany 12 1%
United Kingdom 12 1%
Brazil 10 <1%
Spain 6 <1%
India 5 <1%
Canada 4 <1%
France 2 <1%
Other 30 3%
Unknown 998 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 319 29%
Researcher 188 17%
Student > Master 173 15%
Student > Bachelor 66 6%
Student > Doctoral Student 54 5%
Other 188 17%
Unknown 130 12%
Readers by discipline Count As %
Neuroscience 165 15%
Engineering 163 15%
Agricultural and Biological Sciences 123 11%
Medicine and Dentistry 113 10%
Computer Science 110 10%
Other 250 22%
Unknown 194 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 18 November 2018.
All research outputs
#2,432,069
of 25,483,400 outputs
Outputs from EPJ Nonlinear Biomedical Physics
#2
of 39 outputs
Outputs of similar age
#5,331
of 78,687 outputs
Outputs of similar age from EPJ Nonlinear Biomedical Physics
#1
of 4 outputs
Altmetric has tracked 25,483,400 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 39 research outputs from this source. They receive a mean Attention Score of 4.7. This one scored the same or higher as 37 of them.
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 78,687 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them