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In Vivo Imaging of Tau Pathology Using Magnetic Resonance Imaging Textural Analysis

Overview of attention for article published in Frontiers in Neuroscience, November 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 (85th percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

Mentioned by

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20 tweeters

Citations

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3 Dimensions

Readers on

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27 Mendeley
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Title
In Vivo Imaging of Tau Pathology Using Magnetic Resonance Imaging Textural Analysis
Published in
Frontiers in Neuroscience, November 2017
DOI 10.3389/fnins.2017.00599
Pubmed ID
Authors

Colgan, Niall, Ganeshan, Balaji, Harrison, Ian Francis, Holmes, Holly Elizabeth, Ismail, Ozama, Powell, Nick M, O'Callaghan, James, Murray, Tracey K, Ahmed, Zeshan, Johnson, Ross A, Collins, Emily Catherine, O’Neill, Michael J, Groves, Ashley, Lythgoe, Mark F, Harrison, Ian F., Holmes, Holly E., Wells, Jack A., Powell, Nick M., O'Callaghan, James M., O'Neill, Michael J., Murray, Tracey K., Collins, Emily C., Johnson, Ross A., Lythgoe, Mark F., Niall Colgan, Balaji Ganeshan, Ian F. Harrison, Ozama Ismail, Holly E. Holmes, Jack A. Wells, Nick M. Powell, James M. O'Callaghan, Michael J. O'Neill, Tracey K. Murray, Zeshan Ahmed, Emily C. Collins, Ross A. Johnson, Ashley Groves, Mark F. Lythgoe

Abstract

Background: Non-invasive characterization of the pathological features of Alzheimer's disease (AD) could enhance patient management and the development of therapeutic strategies. Magnetic resonance imaging texture analysis (MRTA) has been used previously to extract texture descriptors from structural clinical scans in AD to determine cerebral tissue heterogeneity. In this study, we examined the potential of MRTA to specifically identify tau pathology in an AD mouse model and compared the MRTA metrics to histological measures of tau burden. Methods: MRTA was applied to T2 weighted high-resolution MR images of nine 8.5-month-old rTg4510 tau pathology (TG) mice and 16 litter matched wild-type (WT) mice. MRTA comprised of the filtration-histogram technique, where the filtration step extracted and enhanced features of different sizes (fine, medium, and coarse texture scales), followed by quantification of texture using histogram analysis (mean gray level intensity, mean intensity, entropy, uniformity, skewness, standard-deviation, and kurtosis). MRTA was applied to manually segmented regions of interest (ROI) drawn within the cortex, hippocampus, and thalamus regions and the level of tau burden was assessed in equivalent regions using histology. Results: Texture parameters were markedly different between WT and TG in the cortex (E, p < 0.01, K, p < 0.01), the hippocampus (K, p < 0.05) and in the thalamus (K, p < 0.01). In addition, we observed significant correlations between histological measurements of tau burden and kurtosis in the cortex, hippocampus and thalamus. Conclusions: MRTA successfully differentiated WT and TG in brain regions with varying degrees of tau pathology (cortex, hippocampus, and thalamus) based on T2 weighted MR images. Furthermore, the kurtosis measurement correlated with histological measures of tau burden. This initial study indicates that MRTA may have a role in the early diagnosis of AD and the assessment of tau pathology using routinely acquired structural MR images.

Twitter Demographics

The data shown below were collected from the profiles of 20 tweeters 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 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 27 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 41%
Student > Ph. D. Student 4 15%
Student > Master 3 11%
Other 2 7%
Student > Doctoral Student 1 4%
Other 2 7%
Unknown 4 15%
Readers by discipline Count As %
Neuroscience 5 19%
Medicine and Dentistry 5 19%
Agricultural and Biological Sciences 2 7%
Psychology 2 7%
Immunology and Microbiology 1 4%
Other 4 15%
Unknown 8 30%

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 30 November 2017.
All research outputs
#1,623,027
of 16,255,167 outputs
Outputs from Frontiers in Neuroscience
#1,010
of 6,424 outputs
Outputs of similar age
#41,384
of 282,308 outputs
Outputs of similar age from Frontiers in Neuroscience
#5
of 46 outputs
Altmetric has tracked 16,255,167 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,424 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.5. This one has done well, scoring higher than 84% 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 282,308 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 85% of its contemporaries.
We're also able to compare this research output to 46 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.