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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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  • 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)

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1314 Mendeley
citeulike
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, Glasser, Matthew F, Coalson, Timothy S, Robinson, Emma C, Hacker, Carl D, Harwell, John, Yacoub, Essa, Ugurbil, Kamil, Andersson, Jesper, Beckmann, Christian F, Jenkinson, Mark, Smith, Stephen M, Van Essen, David C

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.

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Mendeley readers

The data shown below were compiled from readership statistics for 1,314 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 42 3%
Germany 20 2%
United Kingdom 19 1%
Italy 7 <1%
Spain 6 <1%
Canada 5 <1%
Brazil 5 <1%
Netherlands 5 <1%
Switzerland 4 <1%
Other 44 3%
Unknown 1157 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 376 29%
Researcher 337 26%
Student > Master 158 12%
Student > Bachelor 89 7%
Professor > Associate Professor 80 6%
Other 279 21%
Readers by discipline Count As %
Psychology 284 22%
Neuroscience 271 21%
Agricultural and Biological Sciences 243 18%
Medicine and Dentistry 193 15%
Engineering 80 6%
Other 248 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 2230. 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 June 2017.
All research outputs
#204
of 7,947,684 outputs
Outputs from Nature
#58
of 46,775 outputs
Outputs of similar age
#15
of 256,687 outputs
Outputs of similar age from Nature
#5
of 943 outputs
Altmetric has tracked 7,947,684 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 46,775 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 72.4. 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 256,687 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 943 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.