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Automated Spatial Brain Normalization and Hindbrain White Matter Reference Tissue Give Improved [18F]-Florbetaben PET Quantitation in Alzheimer's Model Mice

Overview of attention for article published in Frontiers in Neuroscience, February 2016
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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 (82nd percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

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Title
Automated Spatial Brain Normalization and Hindbrain White Matter Reference Tissue Give Improved [18F]-Florbetaben PET Quantitation in Alzheimer's Model Mice
Published in
Frontiers in Neuroscience, February 2016
DOI 10.3389/fnins.2016.00045
Pubmed ID
Authors

Felix Overhoff, Matthias Brendel, Anna Jaworska, Viktoria Korzhova, Andreas Delker, Federico Probst, Carola Focke, Franz-Josef Gildehaus, Janette Carlsen, Karlheinz Baumann, Christian Haass, Peter Bartenstein, Jochen Herms, Axel Rominger

Abstract

Preclinical PET studies of β-amyloid (Aβ) accumulation are of growing importance, but comparisons between research sites require standardized and optimized methods for quantitation. Therefore, we aimed to evaluate systematically the (1) impact of an automated algorithm for spatial brain normalization, and (2) intensity scaling methods of different reference regions for Aβ-PET in a large dataset of transgenic mice. PS2APP mice in a 6 week longitudinal setting (N = 37) and another set of PS2APP mice at a histologically assessed narrow range of Aβ burden (N = 40) were investigated by [(18)F]-florbetaben PET. Manual spatial normalization by three readers at different training levels was performed prior to application of an automated brain spatial normalization and inter-reader agreement was assessed by Fleiss Kappa (κ). For this method the impact of templates at different pathology stages was investigated. Four different reference regions on brain uptake normalization were used to calculate frontal cortical standardized uptake value ratios (SUVRCTX∕REF), relative to raw SUVCTX. Results were compared on the basis of longitudinal stability (Cohen's d), and in reference to gold standard histopathological quantitation (Pearson's R). Application of an automated brain spatial normalization resulted in nearly perfect agreement (all κ≥0.99) between different readers, with constant or improved correlation with histology. Templates based on inappropriate pathology stage resulted in up to 2.9% systematic bias for SUVRCTX∕REF. All SUVRCTX∕REF methods performed better than SUVCTX both with regard to longitudinal stability (d≥1.21 vs. d = 0.23) and histological gold standard agreement (R≥0.66 vs. R≥0.31). Voxel-wise analysis suggested a physiologically implausible longitudinal decrease by global mean scaling. The hindbrain white matter reference (R mean = 0.75) was slightly superior to the brainstem (R mean = 0.74) and the cerebellum (R mean = 0.73). Automated brain normalization with reference region templates presents an excellent method to avoid the inter-reader variability in preclinical Aβ-PET scans. Intracerebral reference regions lacking Aβ pathology serve for precise longitudinal in vivo quantification of [(18)F]-florbetaben PET. Hindbrain white matter reference performed best when considering the composite of quality criteria.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 4%
Unknown 22 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 39%
Other 2 9%
Student > Doctoral Student 2 9%
Student > Master 2 9%
Student > Bachelor 1 4%
Other 2 9%
Unknown 5 22%
Readers by discipline Count As %
Neuroscience 5 22%
Psychology 4 17%
Medicine and Dentistry 2 9%
Business, Management and Accounting 1 4%
Biochemistry, Genetics and Molecular Biology 1 4%
Other 3 13%
Unknown 7 30%
Attention Score in Context

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 18 March 2016.
All research outputs
#3,415,054
of 25,371,288 outputs
Outputs from Frontiers in Neuroscience
#2,701
of 11,538 outputs
Outputs of similar age
#51,634
of 312,035 outputs
Outputs of similar age from Frontiers in Neuroscience
#33
of 169 outputs
Altmetric has tracked 25,371,288 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 11,538 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.9. This one has done well, scoring higher than 75% 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 312,035 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 82% of its contemporaries.
We're also able to compare this research output to 169 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.