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Anatomical networks reveal the musculoskeletal modularity of the human head

Overview of attention for article published in Scientific Reports, February 2015
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

news
5 news outlets
blogs
2 blogs
twitter
35 tweeters
facebook
18 Facebook pages
googleplus
9 Google+ users

Citations

dimensions_citation
34 Dimensions

Readers on

mendeley
93 Mendeley
Title
Anatomical networks reveal the musculoskeletal modularity of the human head
Published in
Scientific Reports, February 2015
DOI 10.1038/srep08298
Pubmed ID
Authors

Borja Esteve-Altava, Rui Diogo, Christopher Smith, Julia C. Boughner, Diego Rasskin-Gutman

Abstract

Mosaic evolution is a key mechanism that promotes robustness and evolvability in living beings. For the human head, to have a modular organization would imply that each phenotypic module could grow and function semi-independently. Delimiting the boundaries of head modules, and even assessing their existence, is essential to understand human evolution. Here we provide the first study of the human head using anatomical network analysis (AnNA), offering the most complete overview of the modularity of the head to date. Our analysis integrates the many biological dependences that tie hard and soft tissues together, arising as a consequence of development, growth, stresses and loads, and motion. We created an anatomical network model of the human head, where nodes represent anatomical units and links represent their physical articulations. The analysis of the human head network uncovers the presence of 10 musculoskeletal modules, deep-rooted in these biological dependences, of developmental and evolutionary significance. In sum, this study uncovers new anatomical and functional modules of the human head using a novel quantitative method that enables a more comprehensive understanding of the evolutionary anatomy of our lineage, including the evolution of facial expression and facial asymmetry.

Twitter Demographics

The data shown below were collected from the profiles of 35 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 93 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Australia 1 1%
Brazil 1 1%
Canada 1 1%
Mexico 1 1%
Argentina 1 1%
United States 1 1%
Poland 1 1%
Unknown 86 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 18%
Researcher 12 13%
Student > Master 10 11%
Other 10 11%
Student > Bachelor 8 9%
Other 26 28%
Unknown 10 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 45 48%
Medicine and Dentistry 11 12%
Biochemistry, Genetics and Molecular Biology 7 8%
Earth and Planetary Sciences 4 4%
Computer Science 3 3%
Other 9 10%
Unknown 14 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 88. 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 14 June 2018.
All research outputs
#236,853
of 15,421,575 outputs
Outputs from Scientific Reports
#2,845
of 79,883 outputs
Outputs of similar age
#4,621
of 287,768 outputs
Outputs of similar age from Scientific Reports
#1
of 7 outputs
Altmetric has tracked 15,421,575 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 79,883 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.9. This one has done particularly well, scoring higher than 96% 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 287,768 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 98% of its contemporaries.
We're also able to compare this research output to 7 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