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Changes in Community Structure of Resting State Functional Connectivity in Unipolar Depression

Overview of attention for article published in PLoS ONE, August 2012
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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 (83rd percentile)
  • Good Attention Score compared to outputs of the same age and source (79th percentile)

Mentioned by

twitter
5 tweeters
patent
1 patent
facebook
1 Facebook page

Citations

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82 Dimensions

Readers on

mendeley
166 Mendeley
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Title
Changes in Community Structure of Resting State Functional Connectivity in Unipolar Depression
Published in
PLoS ONE, August 2012
DOI 10.1371/journal.pone.0041282
Pubmed ID
Authors

Anton Lord, Dorothea Horn, Michael Breakspear, Martin Walter

Abstract

Major depression is a prevalent disorder that imposes a significant burden on society, yet objective laboratory-style tests to assist in diagnosis are lacking. We employed network-based analyses of "resting state" functional neuroimaging data to ascertain group differences in the endogenous cortical activity between healthy and depressed subjects.We additionally sought to use machine learning techniques to explore the ability of these network-based measures of resting state activity to provide diagnostic information for depression. Resting state fMRI data were acquired from twenty two depressed outpatients and twenty two healthy subjects matched for age and gender. These data were anatomically parcellated and functional connectivity matrices were then derived using the linear correlations between the BOLD signal fluctuations of all pairs of cortical and subcortical regions.We characterised the hierarchical organization of these matrices using network-based matrics, with an emphasis on their mid-scale "modularity" arrangement. Whilst whole brain measures of organization did not differ between groups, a significant rearrangement of their community structure was observed. Furthermore we were able to classify individuals with a high level of accuracy using a support vector machine, primarily through the use of a modularity-based metric known as the participation index.In conclusion, the application of machine learning techniques to features of resting state fMRI network activity shows promising potential to assist in the diagnosis of major depression, now suggesting the need for validation in independent data sets.

Twitter Demographics

The data shown below were collected from the profiles of 5 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 166 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 4%
United Kingdom 4 2%
Spain 3 2%
Japan 2 1%
Tanzania, United Republic of 2 1%
Finland 1 <1%
China 1 <1%
Mexico 1 <1%
Germany 1 <1%
Other 2 1%
Unknown 143 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 39 23%
Researcher 27 16%
Student > Master 21 13%
Professor > Associate Professor 18 11%
Student > Postgraduate 12 7%
Other 49 30%
Readers by discipline Count As %
Medicine and Dentistry 37 22%
Psychology 34 20%
Agricultural and Biological Sciences 19 11%
Unspecified 17 10%
Neuroscience 16 10%
Other 43 26%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 31 May 2018.
All research outputs
#2,008,656
of 12,196,947 outputs
Outputs from PLoS ONE
#30,630
of 133,747 outputs
Outputs of similar age
#19,503
of 121,154 outputs
Outputs of similar age from PLoS ONE
#720
of 3,624 outputs
Altmetric has tracked 12,196,947 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 133,747 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.6. This one has done well, scoring higher than 76% 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 121,154 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 3,624 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.