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Machine learning approaches and non-linear processing of extracted components in frontal region to predict rTMS treatment response in major depressive disorder

Overview of attention for article published in Frontiers in Systems Neuroscience, March 2023
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (67th percentile)
  • Good Attention Score compared to outputs of the same age and source (73rd percentile)

Mentioned by

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7 X users

Readers on

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31 Mendeley
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Title
Machine learning approaches and non-linear processing of extracted components in frontal region to predict rTMS treatment response in major depressive disorder
Published in
Frontiers in Systems Neuroscience, March 2023
DOI 10.3389/fnsys.2023.919977
Pubmed ID
Authors

Elias Ebrahimzadeh, Farahnaz Fayaz, Lila Rajabion, Masoud Seraji, Fatemeh Aflaki, Ahmad Hammoud, Zahra Taghizadeh, Mostafa Asgarinejad, Hamid Soltanian-Zadeh

Timeline

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X Demographics

X Demographics

The data shown below were collected from the profiles of 7 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 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 31 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 16%
Other 3 10%
Student > Ph. D. Student 3 10%
Student > Master 2 6%
Lecturer 1 3%
Other 2 6%
Unknown 15 48%
Readers by discipline Count As %
Engineering 5 16%
Nursing and Health Professions 2 6%
Psychology 2 6%
Computer Science 2 6%
Neuroscience 2 6%
Other 2 6%
Unknown 16 52%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 06 August 2023.
All research outputs
#8,174,595
of 26,424,855 outputs
Outputs from Frontiers in Systems Neuroscience
#589
of 1,410 outputs
Outputs of similar age
#140,839
of 432,772 outputs
Outputs of similar age from Frontiers in Systems Neuroscience
#6
of 23 outputs
Altmetric has tracked 26,424,855 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 1,410 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.1. This one has gotten more attention than average, scoring higher than 57% 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 432,772 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.
We're also able to compare this research output to 23 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 73% of its contemporaries.