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A multi-modal parcellation of human cerebral cortex

Overview of attention for article published in Nature, July 2016
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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 (99th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Citations

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1261 Dimensions

Readers on

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2767 Mendeley
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5 CiteULike
Title
A multi-modal parcellation of human cerebral cortex
Published in
Nature, July 2016
DOI 10.1038/nature18933
Pubmed ID
Authors

Matthew F. Glasser, Timothy S. Coalson, Emma C. Robinson, Carl D. Hacker, John Harwell, Essa Yacoub, Kamil Ugurbil, Jesper Andersson, Christian F. Beckmann, Mark Jenkinson, Stephen M. Smith, David C. Van Essen

Abstract

Understanding the amazingly complex human cerebral cortex requires a map (or parcellation) of its major subdivisions, known as cortical areas. Making an accurate areal map has been a century-old objective in neuroscience. Using multi-modal magnetic resonance images from the Human Connectome Project (HCP) and an objective semi-automated neuroanatomical approach, we delineated 180 areas per hemisphere bounded by sharp changes in cortical architecture, function, connectivity, and/or topography in a precisely aligned group average of 210 healthy young adults. We characterized 97 new areas and 83 areas previously reported using post-mortem microscopy or other specialized study-specific approaches. To enable automated delineation and identification of these areas in new HCP subjects and in future studies, we trained a machine-learning classifier to recognize the multi-modal 'fingerprint' of each cortical area. This classifier detected the presence of 96.6% of the cortical areas in new subjects, replicated the group parcellation, and could correctly locate areas in individuals with atypical parcellations. The freely available parcellation and classifier will enable substantially improved neuroanatomical precision for studies of the structural and functional organization of human cerebral cortex and its variation across individuals and in development, aging, and disease.

Twitter Demographics

The data shown below were collected from the profiles of 836 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 2,767 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 50 2%
Germany 19 <1%
United Kingdom 15 <1%
Italy 9 <1%
Japan 8 <1%
Spain 7 <1%
France 7 <1%
Canada 6 <1%
Brazil 5 <1%
Other 42 2%
Unknown 2599 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 699 25%
Researcher 615 22%
Student > Master 310 11%
Student > Bachelor 194 7%
Student > Doctoral Student 147 5%
Other 559 20%
Unknown 243 9%
Readers by discipline Count As %
Neuroscience 740 27%
Psychology 421 15%
Medicine and Dentistry 279 10%
Agricultural and Biological Sciences 276 10%
Engineering 172 6%
Other 385 14%
Unknown 494 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 2235. 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 27 May 2020.
All research outputs
#1,133
of 15,418,159 outputs
Outputs from Nature
#165
of 74,593 outputs
Outputs of similar age
#30
of 266,097 outputs
Outputs of similar age from Nature
#6
of 964 outputs
Altmetric has tracked 15,418,159 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 74,593 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 83.9. 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 266,097 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 964 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 99% of its contemporaries.