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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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1379 Dimensions

Readers on

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2842 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 839 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 2,842 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 1%
Unknown 2674 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 715 25%
Researcher 630 22%
Student > Master 315 11%
Student > Bachelor 199 7%
Student > Doctoral Student 149 5%
Other 569 20%
Unknown 265 9%
Readers by discipline Count As %
Neuroscience 762 27%
Psychology 427 15%
Medicine and Dentistry 284 10%
Agricultural and Biological Sciences 277 10%
Engineering 175 6%
Other 396 14%
Unknown 521 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 2238. 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 30 August 2020.
All research outputs
#1,311
of 15,885,275 outputs
Outputs from Nature
#180
of 75,819 outputs
Outputs of similar age
#30
of 267,199 outputs
Outputs of similar age from Nature
#6
of 959 outputs
Altmetric has tracked 15,885,275 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 75,819 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 85.9. 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 267,199 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 959 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.