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Mendeley readers
Attention Score in Context
| Title |
Boosting brain connectome classification accuracy in Alzheimer's disease using higher-order singular value decomposition
|
|---|---|
| Published in |
Frontiers in Neuroscience, July 2015
|
| DOI | 10.3389/fnins.2015.00257 |
| Pubmed ID | |
| Authors |
Liang Zhan, Yashu Liu, Yalin Wang, Jiayu Zhou, Neda Jahanshad, Jieping Ye, Paul M. Thompson, Alzheimer's Disease Neuroimaging Initiative |
| Abstract |
Alzheimer's disease (AD) is a progressive brain disease. Accurate detection of AD and its prodromal stage, mild cognitive impairment (MCI), are crucial. There is also a growing interest in identifying brain imaging biomarkers that help to automatically differentiate stages of Alzheimer's disease. Here, we focused on brain structural networks computed from diffusion MRI and proposed a new feature extraction and classification framework based on higher order singular value decomposition and sparse logistic regression. In tests on publicly available data from the Alzheimer's Disease Neuroimaging Initiative, our proposed framework showed promise in detecting brain network differences that help in classifying different stages of Alzheimer's disease. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 50% |
| Switzerland | 1 | 25% |
| Unknown | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 2% |
| Unknown | 43 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 18% |
| Researcher | 7 | 16% |
| Student > Master | 4 | 9% |
| Student > Bachelor | 4 | 9% |
| Professor | 3 | 7% |
| Other | 9 | 20% |
| Unknown | 9 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 7 | 16% |
| Computer Science | 6 | 14% |
| Medicine and Dentistry | 4 | 9% |
| Neuroscience | 3 | 7% |
| Agricultural and Biological Sciences | 2 | 5% |
| Other | 8 | 18% |
| Unknown | 14 | 32% |
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 14 August 2015.
All research outputs
#3,415,054
of 25,373,627 outputs
Outputs from Frontiers in Neuroscience
#2,701
of 11,538 outputs
Outputs of similar age
#41,898
of 274,966 outputs
Outputs of similar age from Frontiers in Neuroscience
#19
of 103 outputs
Altmetric has tracked 25,373,627 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 274,966 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 84% of its contemporaries.
We're also able to compare this research output to 103 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.