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Classification of Alzheimer’s Disease Based on Abnormal Hippocampal Functional Connectivity and Machine Learning

Overview of attention for article published in Frontiers in Aging Neuroscience, February 2022
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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 (89th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

news
1 news outlet
twitter
13 X users

Citations

dimensions_citation
16 Dimensions

Readers on

mendeley
35 Mendeley
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Title
Classification of Alzheimer’s Disease Based on Abnormal Hippocampal Functional Connectivity and Machine Learning
Published in
Frontiers in Aging Neuroscience, February 2022
DOI 10.3389/fnagi.2022.754334
Pubmed ID
Authors

Qixiao Zhu, Yonghui Wang, Chuanjun Zhuo, Qunxing Xu, Yuan Yao, Zhuyun Liu, Yi Li, Zhao Sun, Jian Wang, Ming Lv, Qiang Wu, Dawei Wang

Timeline

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X Demographics

X Demographics

The data shown below were collected from the profiles of 13 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 35 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 6 17%
Student > Master 3 9%
Student > Bachelor 3 9%
Student > Postgraduate 2 6%
Librarian 1 3%
Other 3 9%
Unknown 17 49%
Readers by discipline Count As %
Unspecified 6 17%
Computer Science 3 9%
Medicine and Dentistry 2 6%
Nursing and Health Professions 1 3%
Agricultural and Biological Sciences 1 3%
Other 4 11%
Unknown 18 51%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 18 March 2022.
All research outputs
#1,914,921
of 23,371,053 outputs
Outputs from Frontiers in Aging Neuroscience
#539
of 4,944 outputs
Outputs of similar age
#46,515
of 442,045 outputs
Outputs of similar age from Frontiers in Aging Neuroscience
#26
of 312 outputs
Altmetric has tracked 23,371,053 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,944 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.3. This one has done well, scoring higher than 89% 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 442,045 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 89% of its contemporaries.
We're also able to compare this research output to 312 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 91% of its contemporaries.