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Whole Brain Magnetic Resonance Image Atlases: A Systematic Review of Existing Atlases and Caveats for Use in Population Imaging

Overview of attention for article published in Frontiers in Neuroinformatics, January 2017
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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 (86th percentile)
  • Good Attention Score compared to outputs of the same age and source (77th percentile)

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21 X users
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2 Facebook pages

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191 Mendeley
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Title
Whole Brain Magnetic Resonance Image Atlases: A Systematic Review of Existing Atlases and Caveats for Use in Population Imaging
Published in
Frontiers in Neuroinformatics, January 2017
DOI 10.3389/fninf.2017.00001
Pubmed ID
Authors

David Alexander Dickie, Susan D. Shenkin, Devasuda Anblagan, Juyoung Lee, Manuel Blesa Cabez, David Rodriguez, James P. Boardman, Adam Waldman, Dominic E. Job, Joanna M. Wardlaw

Abstract

Brain MRI atlases may be used to characterize brain structural changes across the life course. Atlases have important applications in research, e.g., as registration and segmentation targets to underpin image analysis in population imaging studies, and potentially in future in clinical practice, e.g., as templates for identifying brain structural changes out with normal limits, and increasingly for use in surgical planning. However, there are several caveats and limitations which must be considered before successfully applying brain MRI atlases to research and clinical problems. For example, the influential Talairach and Tournoux atlas was derived from a single fixed cadaveric brain from an elderly female with limited clinical information, yet is the basis of many modern atlases and is often used to report locations of functional activation. We systematically review currently available whole brain structural MRI atlases with particular reference to the implications for population imaging through to emerging clinical practice. We found 66 whole brain structural MRI atlases world-wide. The vast majority were based on T1, T2, and/or proton density (PD) structural sequences, had been derived using parametric statistics (inappropriate for brain volume distributions), had limited supporting clinical or cognitive data, and included few younger (>5 and <18 years) or older (>60 years) subjects. To successfully characterize brain structural features and their changes across different stages of life, we conclude that whole brain structural MRI atlases should include: more subjects at the upper and lower extremes of age; additional structural sequences, including fluid attenuation inversion recovery (FLAIR) and T2(*) sequences; a range of appropriate statistics, e.g., rank-based or non-parametric; and detailed cognitive and clinical profiles of the included subjects in order to increase the relevance and utility of these atlases.

X Demographics

X Demographics

The data shown below were collected from the profiles of 21 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 191 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
China 1 <1%
Germany 1 <1%
Canada 1 <1%
Unknown 187 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 39 20%
Student > Ph. D. Student 37 19%
Student > Master 20 10%
Student > Bachelor 12 6%
Student > Doctoral Student 8 4%
Other 27 14%
Unknown 48 25%
Readers by discipline Count As %
Neuroscience 39 20%
Engineering 18 9%
Psychology 16 8%
Medicine and Dentistry 12 6%
Computer Science 12 6%
Other 28 15%
Unknown 66 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 February 2017.
All research outputs
#2,980,859
of 25,547,904 outputs
Outputs from Frontiers in Neuroinformatics
#111
of 842 outputs
Outputs of similar age
#56,743
of 421,500 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#5
of 18 outputs
Altmetric has tracked 25,547,904 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 842 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one has done well, scoring higher than 86% 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 421,500 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 86% of its contemporaries.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.