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MRI predictors of amyloid pathology: results from the EMIF-AD Multimodal Biomarker Discovery study

Overview of attention for article published in Alzheimer's Research & Therapy, September 2018
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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 (89th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

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MRI predictors of amyloid pathology: results from the EMIF-AD Multimodal Biomarker Discovery study
Published in
Alzheimer's Research & Therapy, September 2018
DOI 10.1186/s13195-018-0428-1
Pubmed ID

Mara ten Kate, Alberto Redolfi, Enrico Peira, Isabelle Bos, Stephanie J. Vos, Rik Vandenberghe, Silvy Gabel, Jolien Schaeverbeke, Philip Scheltens, Olivier Blin, Jill C. Richardson, Regis Bordet, Anders Wallin, Carl Eckerstrom, José Luis Molinuevo, Sebastiaan Engelborghs, Christine Van Broeckhoven, Pablo Martinez-Lage, Julius Popp, Magdalini Tsolaki, Frans R. J. Verhey, Alison L. Baird, Cristina Legido-Quigley, Lars Bertram, Valerija Dobricic, Henrik Zetterberg, Simon Lovestone, Johannes Streffer, Silvia Bianchetti, Gerald P. Novak, Jerome Revillard, Mark F. Gordon, Zhiyong Xie, Viktor Wottschel, Giovanni Frisoni, Pieter Jelle Visser, Frederik Barkhof


With the shift of research focus towards the pre-dementia stage of Alzheimer's disease (AD), there is an urgent need for reliable, non-invasive biomarkers to predict amyloid pathology. The aim of this study was to assess whether easily obtainable measures from structural MRI, combined with demographic data, cognitive data and apolipoprotein E (APOE) ε4 genotype, can be used to predict amyloid pathology using machine-learning classification. We examined 810 subjects with structural MRI data and amyloid markers from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery study, including subjects with normal cognition (CN, n = 337, age 66.5 ± 7.2, 50% female, 27% amyloid positive), mild cognitive impairment (MCI, n = 375, age 69.1 ± 7.5, 53% female, 63% amyloid positive) and AD dementia (n = 98, age 67.0 ± 7.7, 48% female, 97% amyloid positive). Structural MRI scans were visually assessed and Freesurfer was used to obtain subcortical volumes, cortical thickness and surface area measures. We first assessed univariate associations between MRI measures and amyloid pathology using mixed models. Next, we developed and tested an automated classifier using demographic, cognitive, MRI and APOE ε4 information to predict amyloid pathology. A support vector machine (SVM) with nested 10-fold cross-validation was applied to identify a set of markers best discriminating between amyloid positive and amyloid negative subjects. In univariate associations, amyloid pathology was associated with lower subcortical volumes and thinner cortex in AD-signature regions in CN and MCI. The multi-variable SVM classifier provided an area under the curve (AUC) of 0.81 ± 0.07 in MCI and an AUC of 0.74 ± 0.08 in CN. In CN, selected features for the classifier included APOE ε4, age, memory scores and several MRI measures such as hippocampus, amygdala and accumbens volumes and cortical thickness in temporal and parahippocampal regions. In MCI, the classifier including demographic and APOE ε4 information did not improve after additionally adding imaging measures. Amyloid pathology is associated with changes in structural MRI measures in CN and MCI. An automated classifier based on clinical, imaging and APOE ε4 data can identify the presence of amyloid pathology with a moderate level of accuracy. These results could be used in clinical trials to pre-screen subjects for anti-amyloid therapies.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 128 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 128 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 26 20%
Student > Ph. D. Student 16 13%
Student > Master 12 9%
Student > Bachelor 11 9%
Student > Doctoral Student 8 6%
Other 23 18%
Unknown 32 25%
Readers by discipline Count As %
Medicine and Dentistry 24 19%
Neuroscience 13 10%
Biochemistry, Genetics and Molecular Biology 11 9%
Psychology 11 9%
Computer Science 8 6%
Other 22 17%
Unknown 39 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 20. 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 10 September 2019.
All research outputs
of 23,105,443 outputs
Outputs from Alzheimer's Research & Therapy
of 1,252 outputs
Outputs of similar age
of 341,808 outputs
Outputs of similar age from Alzheimer's Research & Therapy
of 35 outputs
Altmetric has tracked 23,105,443 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,252 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 25.8. This one has done well, scoring higher than 77% 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 341,808 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 89% of its contemporaries.
We're also able to compare this research output to 35 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 60% of its contemporaries.