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A practical guideline for intracranial volume estimation in patients with Alzheimer's disease

Overview of attention for article published in BMC Bioinformatics, April 2015
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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 (84th percentile)
  • High Attention Score compared to outputs of the same age and source (86th percentile)

Mentioned by

news
1 news outlet
twitter
1 tweeter

Citations

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31 Dimensions

Readers on

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91 Mendeley
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Title
A practical guideline for intracranial volume estimation in patients with Alzheimer's disease
Published in
BMC Bioinformatics, April 2015
DOI 10.1186/1471-2105-16-s7-s8
Pubmed ID
Authors

Saman Sargolzaei, Arman Sargolzaei, Mercedes Cabrerizo, Gang Chen, Mohammed Goryawala, Shirin Noei, Qi Zhou, Ranjan Duara, Warren Barker, Malek Adjouadi

Abstract

Intracranial volume (ICV) is an important normalization measure used in morphometric analyses to correct for head size in studies of Alzheimer Disease (AD). Inaccurate ICV estimation could introduce bias in the outcome. The current study provides a decision aid in defining protocols for ICV estimation in patients with Alzheimer disease in terms of sampling frequencies that can be optimally used on the volumetric MRI data, and the type of software most suitable for use in estimating the ICV measure. Two groups of 22 subjects are considered, including adult controls (AC) and patients with Alzheimer Disease (AD). Reference measurements were calculated for each subject by manually tracing intracranial cavity by the means of visual inspection. The reliability of reference measurements were assured through intra- and inter- variation analyses. Three publicly well-known software packages (Freesurfer, FSL, and SPM) were examined in their ability to automatically estimate ICV across the groups. Analysis of the results supported the significant effect of estimation method, gender, cognitive condition of the subject and the interaction among method and cognitive condition factors in the measured ICV. Results on sub-sampling studies with a 95% confidence showed that in order to keep the accuracy of the interleaved slice sampling protocol above 99%, the sampling period cannot exceed 20 millimeters for AC and 15 millimeters for AD. Freesurfer showed promising estimates for both adult groups. However SPM showed more consistency in its ICV estimation over the different phases of the study. This study emphasized the importance in selecting the appropriate protocol, the choice of the sampling period in the manual estimation of ICV and selection of suitable software for the automated estimation of ICV. The current study serves as an initial framework for establishing an appropriate protocol in both manual and automatic ICV estimations with different subject populations.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 91 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Austria 1 1%
Unknown 90 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 20%
Student > Ph. D. Student 17 19%
Student > Master 12 13%
Student > Bachelor 7 8%
Other 5 5%
Other 13 14%
Unknown 19 21%
Readers by discipline Count As %
Medicine and Dentistry 15 16%
Neuroscience 14 15%
Psychology 13 14%
Engineering 6 7%
Computer Science 5 5%
Other 14 15%
Unknown 24 26%

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 13 December 2015.
All research outputs
#2,939,412
of 22,805,349 outputs
Outputs from BMC Bioinformatics
#1,040
of 7,281 outputs
Outputs of similar age
#39,917
of 265,337 outputs
Outputs of similar age from BMC Bioinformatics
#19
of 139 outputs
Altmetric has tracked 22,805,349 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 7,281 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 85% 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 265,337 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 84% of its contemporaries.
We're also able to compare this research output to 139 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.