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Mendeley readers
Attention Score in Context
| Title |
Integrative EEG biomarkers predict progression to Alzheimer's disease at the MCI stage
|
|---|---|
| Published in |
Frontiers in Aging Neuroscience, January 2013
|
| DOI | 10.3389/fnagi.2013.00058 |
| Pubmed ID | |
| Authors |
Simon-Shlomo Poil, Willem de Haan, Wiesje M. van der Flier, Huibert D. Mansvelder, Philip Scheltens, Klaus Linkenkaer-Hansen |
| Abstract |
Alzheimer's disease (AD) is a devastating disorder of increasing prevalence in modern society. Mild cognitive impairment (MCI) is considered a transitional stage between normal aging and AD; however, not all subjects with MCI progress to AD. Prediction of conversion to AD at an early stage would enable an earlier, and potentially more effective, treatment of AD. Electroencephalography (EEG) biomarkers would provide a non-invasive and relatively cheap screening tool to predict conversion to AD; however, traditional EEG biomarkers have not been considered accurate enough to be useful in clinical practice. Here, we aim to combine the information from multiple EEG biomarkers into a diagnostic classification index in order to improve the accuracy of predicting conversion from MCI to AD within a 2-year period. We followed 86 patients initially diagnosed with MCI for 2 years during which 25 patients converted to AD. We show that multiple EEG biomarkers mainly related to activity in the beta-frequency range (13-30 Hz) can predict conversion from MCI to AD. Importantly, by integrating six EEG biomarkers into a diagnostic index using logistic regression the prediction improved compared with the classification using the individual biomarkers, with a sensitivity of 88% and specificity of 82%, compared with a sensitivity of 64% and specificity of 62% of the best individual biomarker in this index. In order to identify this diagnostic index we developed a data mining approach implemented in the Neurophysiological Biomarker Toolbox (http://www.nbtwiki.net/). We suggest that this approach can be used to identify optimal combinations of biomarkers (integrative biomarkers) also in other modalities. Potentially, these integrative biomarkers could be more sensitive to disease progression and response to therapeutic intervention. |
X Demographics
The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Comoros | 1 | 17% |
| Mexico | 1 | 17% |
| Switzerland | 1 | 17% |
| Unknown | 3 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 6 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 351 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 1% |
| Netherlands | 3 | <1% |
| Italy | 2 | <1% |
| United Kingdom | 2 | <1% |
| France | 1 | <1% |
| Portugal | 1 | <1% |
| Russia | 1 | <1% |
| Switzerland | 1 | <1% |
| Greece | 1 | <1% |
| Other | 1 | <1% |
| Unknown | 334 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 70 | 20% |
| Researcher | 69 | 20% |
| Student > Master | 35 | 10% |
| Student > Bachelor | 26 | 7% |
| Other | 23 | 7% |
| Other | 66 | 19% |
| Unknown | 62 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 68 | 19% |
| Engineering | 42 | 12% |
| Psychology | 37 | 11% |
| Medicine and Dentistry | 37 | 11% |
| Agricultural and Biological Sciences | 28 | 8% |
| Other | 49 | 14% |
| Unknown | 90 | 26% |
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 07 January 2021.
All research outputs
#2,705,275
of 24,811,707 outputs
Outputs from Frontiers in Aging Neuroscience
#945
of 5,338 outputs
Outputs of similar age
#26,950
of 292,060 outputs
Outputs of similar age from Frontiers in Aging Neuroscience
#13
of 77 outputs
Altmetric has tracked 24,811,707 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 5,338 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.4. This one has done well, scoring higher than 82% 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 292,060 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 90% of its contemporaries.
We're also able to compare this research output to 77 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.