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A Set of Functional Brain Networks for the Comprehensive Evaluation of Human Characteristics

Overview of attention for article published in Frontiers in Neuroscience, March 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • Good Attention Score compared to outputs of the same age and source (74th percentile)

Mentioned by

blogs
1 blog
twitter
6 X users
facebook
1 Facebook page

Citations

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8 Dimensions

Readers on

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40 Mendeley
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Title
A Set of Functional Brain Networks for the Comprehensive Evaluation of Human Characteristics
Published in
Frontiers in Neuroscience, March 2018
DOI 10.3389/fnins.2018.00149
Pubmed ID
Authors

Yul-Wan Sung, Yousuke Kawachi, Uk-Su Choi, Daehun Kang, Chihiro Abe, Yuki Otomo, Seiji Ogawa

Abstract

Many human characteristics must be evaluated to comprehensively understand an individual, and measurements of the corresponding cognition/behavior are required. Brain imaging by functional MRI (fMRI) has been widely used to examine brain function related to human cognition/behavior. However, few aspects of cognition/behavior of individuals or experimental groups can be examined through task-based fMRI. Recently, resting state fMRI (rs-fMRI) signals have been shown to represent functional infrastructure in the brain that is highly involved in processing information related to cognition/behavior. Using rs-fMRI may allow diverse information about the brain through a single MRI scan to be obtained, as rs-fMRI does not require stimulus tasks. In this study, we attempted to identify a set of functional networks representing cognition/behavior that are related to a wide variety of human characteristics and to evaluate these characteristics using rs-fMRI data. If possible, these findings would support the potential of rs-fMRI to provide diverse information about the brain. We used resting-state fMRI and a set of 130 psychometric parameters that cover most human characteristics, including those related to intelligence and emotional quotients and social ability/skill. We identified 163 brain regions by VBM analysis using regression analysis with 130 psychometric parameters. Next, using a 163 × 163 correlation matrix, we identified functional networks related to 111 of the 130 psychometric parameters. Finally, we made an 8-class support vector machine classifiers corresponding to these 111 functional networks. Our results demonstrate that rs-fMRI signals contain intrinsic information about brain function related to cognition/behaviors and that this set of 111 networks/classifiers can be used to comprehensively evaluate human characteristics.

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X Demographics

The data shown below were collected from the profiles of 6 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 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 40 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 23%
Student > Master 6 15%
Researcher 6 15%
Student > Bachelor 3 8%
Other 2 5%
Other 3 8%
Unknown 11 28%
Readers by discipline Count As %
Psychology 10 25%
Neuroscience 3 8%
Engineering 2 5%
Computer Science 2 5%
Medicine and Dentistry 2 5%
Other 7 18%
Unknown 14 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 22 June 2020.
All research outputs
#3,195,428
of 25,382,440 outputs
Outputs from Frontiers in Neuroscience
#2,285
of 11,542 outputs
Outputs of similar age
#64,349
of 351,830 outputs
Outputs of similar age from Frontiers in Neuroscience
#65
of 256 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,542 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one has done well, scoring higher than 80% 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 351,830 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% of its contemporaries.
We're also able to compare this research output to 256 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.