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A systematic framework for functional connectivity measures

Overview of attention for article published in Frontiers in Neuroscience, December 2014
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

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55 X users
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5 Facebook pages
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1 Google+ user

Citations

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278 Dimensions

Readers on

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504 Mendeley
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Title
A systematic framework for functional connectivity measures
Published in
Frontiers in Neuroscience, December 2014
DOI 10.3389/fnins.2014.00405
Pubmed ID
Authors

Huifang E. Wang, Christian G. Bénar, Pascale P. Quilichini, Karl J. Friston, Viktor K. Jirsa, Christophe Bernard

Abstract

Various methods have been proposed to characterize the functional connectivity between nodes in a network measured with different modalities (electrophysiology, functional magnetic resonance imaging etc.). Since different measures of functional connectivity yield different results for the same dataset, it is important to assess when and how they can be used. In this work, we provide a systematic framework for evaluating the performance of a large range of functional connectivity measures-based upon a comprehensive portfolio of models generating measurable responses. Specifically, we benchmarked 42 methods using 10,000 simulated datasets from 5 different types of generative models with different connectivity structures. Since all functional connectivity methods require the setting of some parameters (window size and number, model order etc.), we first optimized these parameters using performance criteria based upon (threshold free) ROC analysis. We then evaluated the performance of the methods on data simulated with different types of models. Finally, we assessed the performance of the methods against different levels of signal-to-noise ratios and network configurations. A MATLAB toolbox is provided to perform such analyses using other methods and simulated datasets.

X Demographics

X Demographics

The data shown below were collected from the profiles of 55 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 504 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 1%
United Kingdom 5 <1%
Germany 3 <1%
France 3 <1%
Norway 1 <1%
Brazil 1 <1%
Switzerland 1 <1%
Canada 1 <1%
Netherlands 1 <1%
Other 2 <1%
Unknown 479 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 119 24%
Researcher 81 16%
Student > Master 80 16%
Student > Bachelor 30 6%
Student > Doctoral Student 29 6%
Other 92 18%
Unknown 73 14%
Readers by discipline Count As %
Neuroscience 123 24%
Engineering 78 15%
Psychology 46 9%
Agricultural and Biological Sciences 38 8%
Medicine and Dentistry 37 7%
Other 75 15%
Unknown 107 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 34. 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 23 August 2018.
All research outputs
#1,165,128
of 25,374,647 outputs
Outputs from Frontiers in Neuroscience
#509
of 11,542 outputs
Outputs of similar age
#15,058
of 368,352 outputs
Outputs of similar age from Frontiers in Neuroscience
#11
of 126 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,542 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.9. This one has done particularly well, scoring higher than 95% of its peers.
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 368,352 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 95% of its contemporaries.
We're also able to compare this research output to 126 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.