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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (80th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

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1 news outlet
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2 X users

Readers on

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58 Mendeley
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Article details
Title
Characteristic patterns of inter- and intra-hemispheric metabolic connectivity in patients with stable and progressive mild cognitive impairment and Alzheimer’s disease
Published in
Scientific Reports, September 2018
DOI 10.1038/s41598-018-31794-8
Pubmed ID
Authors

Sheng-Yao Huang, Jung-Lung Hsu, Kun-Ju Lin, Ho-Ling Liu, Shiaw-Pying Wey, Ing-Tsung Hsiao, For the Alzheimer’s Disease Neuroimaging Initiative

Abstract

The change in hypometabolism affects the regional links in the brain network. Here, to understand the underlying brain metabolic network deficits during the early stage and disease evolution of AD (Alzheimer disease), we applied correlation analysis to identify the metabolic connectivity patterns using 18F-FDG PET data for NC (normal control), sMCI (stable MCI), pMCI (progressive MCI) and AD, and explore the inter- and intra-hemispheric connectivity between anatomically-defined brain regions. Regions extracted from 90 anatomical structures were used to construct the matrix for measuring the inter- and intra-hemispheric connectivity. The brain connectivity patterns from the metabolic network show a decreasing trend of inter- and intra-hemispheric connections for NC, sMCI, pMCI and AD. Connection of temporal to the frontal or occipital regions is a characteristic pattern for conversion of NC to MCI, and the density of links in the parietal-occipital network is a differential pattern between sMCI and pMCI. The reduction pattern of inter and intra-hemispheric brain connectivity in the metabolic network depends on the disease stages, and is with a decreasing trend with respect to disease severity. Both frontal-occipital and parietal-occipital connectivity patterns in the metabolic network using 18F-FDG PET are the key feature for differentiating disease groups in AD.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 58 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 9 16%
Other 5 9%
Student > Ph. D. Student 5 9%
Student > Master 5 9%
Lecturer > Senior Lecturer 3 5%
Other 11 19%
Unknown 20 34%
Readers by discipline
Readers by discipline Count As %
Neuroscience 10 17%
Medicine and Dentistry 7 12%
Psychology 4 7%
Engineering 4 7%
Computer Science 3 5%
Other 6 10%
Unknown 24 41%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 21 September 2018.
All research outputs
#3,023,053
of 23,305,591 outputs
Outputs from Scientific Reports
#25,951
of 125,960 outputs
Outputs of similar age
#63,446
of 337,917 outputs
Outputs of similar age from Scientific Reports
#756
of 3,540 outputs
Altmetric has tracked 23,305,591 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 125,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.3. This one has done well, scoring higher than 79% 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 337,917 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 80% of its contemporaries.
We're also able to compare this research output to 3,540 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.