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Spectral Signatures of Reorganised Brain Networks in Disorders of Consciousness

Overview of attention for article published in PLoS Computational Biology, October 2014
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#37 of 9,065)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

news
28 news outlets
blogs
13 blogs
twitter
173 X users
facebook
57 Facebook pages
googleplus
19 Google+ users
reddit
1 Redditor

Citations

dimensions_citation
188 Dimensions

Readers on

mendeley
390 Mendeley
citeulike
8 CiteULike
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Title
Spectral Signatures of Reorganised Brain Networks in Disorders of Consciousness
Published in
PLoS Computational Biology, October 2014
DOI 10.1371/journal.pcbi.1003887
Pubmed ID
Authors

Srivas Chennu, Paola Finoia, Evelyn Kamau, Judith Allanson, Guy B. Williams, Martin M. Monti, Valdas Noreika, Aurina Arnatkeviciute, Andrés Canales-Johnson, Francisco Olivares, Daniela Cabezas-Soto, David K. Menon, John D. Pickard, Adrian M. Owen, Tristan A. Bekinschtein

Abstract

Theoretical advances in the science of consciousness have proposed that it is concomitant with balanced cortical integration and differentiation, enabled by efficient networks of information transfer across multiple scales. Here, we apply graph theory to compare key signatures of such networks in high-density electroencephalographic data from 32 patients with chronic disorders of consciousness, against normative data from healthy controls. Based on connectivity within canonical frequency bands, we found that patient networks had reduced local and global efficiency, and fewer hubs in the alpha band. We devised a novel topographical metric, termed modular span, which showed that the alpha network modules in patients were also spatially circumscribed, lacking the structured long-distance interactions commonly observed in the healthy controls. Importantly however, these differences between graph-theoretic metrics were partially reversed in delta and theta band networks, which were also significantly more similar to each other in patients than controls. Going further, we found that metrics of alpha network efficiency also correlated with the degree of behavioural awareness. Intriguingly, some patients in behaviourally unresponsive vegetative states who demonstrated evidence of covert awareness with functional neuroimaging stood out from this trend: they had alpha networks that were remarkably well preserved and similar to those observed in the controls. Taken together, our findings inform current understanding of disorders of consciousness by highlighting the distinctive brain networks that characterise them. In the significant minority of vegetative patients who follow commands in neuroimaging tests, they point to putative network mechanisms that could support cognitive function and consciousness despite profound behavioural impairment.

X Demographics

X Demographics

The data shown below were collected from the profiles of 173 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 390 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 8 2%
United Kingdom 4 1%
France 2 <1%
Germany 2 <1%
Poland 2 <1%
Canada 2 <1%
Uruguay 1 <1%
Austria 1 <1%
Chile 1 <1%
Other 7 2%
Unknown 360 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 85 22%
Researcher 69 18%
Student > Master 56 14%
Student > Bachelor 24 6%
Professor > Associate Professor 23 6%
Other 80 21%
Unknown 53 14%
Readers by discipline Count As %
Neuroscience 68 17%
Psychology 67 17%
Agricultural and Biological Sciences 54 14%
Medicine and Dentistry 38 10%
Computer Science 32 8%
Other 56 14%
Unknown 75 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 460. 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 17 May 2023.
All research outputs
#61,085
of 25,889,720 outputs
Outputs from PLoS Computational Biology
#37
of 9,065 outputs
Outputs of similar age
#459
of 269,351 outputs
Outputs of similar age from PLoS Computational Biology
#3
of 139 outputs
Altmetric has tracked 25,889,720 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,065 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has done particularly well, scoring higher than 99% 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 269,351 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 99% of its contemporaries.
We're also able to compare this research output to 139 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 97% of its contemporaries.