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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Generative FDG-PET and MRI Model of Aging and Disease Progression in Alzheimer's Disease
|
|---|---|
| Published in |
PLoS Computational Biology, April 2013
|
| DOI | 10.1371/journal.pcbi.1002987 |
| Pubmed ID | |
| Authors |
Juergen Dukart, Ferath Kherif, Karsten Mueller, Stanislaw Adaszewski, Matthias L. Schroeter, Richard S. J. Frackowiak, Bogdan Draganski |
| Abstract |
The failure of current strategies to provide an explanation for controversial findings on the pattern of pathophysiological changes in Alzheimer's Disease (AD) motivates the necessity to develop new integrative approaches based on multi-modal neuroimaging data that captures various aspects of disease pathology. Previous studies using [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) and structural magnetic resonance imaging (sMRI) report controversial results about time-line, spatial extent and magnitude of glucose hypometabolism and atrophy in AD that depend on clinical and demographic characteristics of the studied populations. Here, we provide and validate at a group level a generative anatomical model of glucose hypo-metabolism and atrophy progression in AD based on FDG-PET and sMRI data of 80 patients and 79 healthy controls to describe expected age and symptom severity related changes in AD relative to a baseline provided by healthy aging. We demonstrate a high level of anatomical accuracy for both modalities yielding strongly age- and symptom-severity- dependant glucose hypometabolism in temporal, parietal and precuneal regions and a more extensive network of atrophy in hippocampal, temporal, parietal, occipital and posterior caudate regions. The model suggests greater and more consistent changes in FDG-PET compared to sMRI at earlier and the inversion of this pattern at more advanced AD stages. Our model describes, integrates and predicts characteristic patterns of AD related pathology, uncontaminated by normal age effects, derived from multi-modal data. It further provides an integrative explanation for findings suggesting a dissociation between early- and late-onset AD. The generative model offers a basis for further development of individualized biomarkers allowing accurate early diagnosis and treatment evaluation. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 33% |
| United States | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 67% |
| Science communicators (journalists, bloggers, editors) | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 136 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 4 | 3% |
| United States | 1 | <1% |
| Austria | 1 | <1% |
| Canada | 1 | <1% |
| Unknown | 129 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 30 | 22% |
| Student > Master | 20 | 15% |
| Student > Ph. D. Student | 19 | 14% |
| Student > Bachelor | 15 | 11% |
| Professor | 7 | 5% |
| Other | 22 | 16% |
| Unknown | 23 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 28 | 21% |
| Neuroscience | 26 | 19% |
| Agricultural and Biological Sciences | 14 | 10% |
| Computer Science | 7 | 5% |
| Psychology | 6 | 4% |
| Other | 26 | 19% |
| Unknown | 29 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 37. 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 April 2017.
All research outputs
#1,093,045
of 25,374,917 outputs
Outputs from PLoS Computational Biology
#882
of 8,960 outputs
Outputs of similar age
#8,037
of 212,592 outputs
Outputs of similar age from PLoS Computational Biology
#6
of 157 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has done particularly well, scoring higher than 90% 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 212,592 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 96% of its contemporaries.
We're also able to compare this research output to 157 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 96% of its contemporaries.