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Attention Score in Context
| Title |
An Investigation of RSN Frequency Spectra Using Ultra-Fast Generalized Inverse Imaging
|
|---|---|
| Published in |
Frontiers in Human Neuroscience, January 2013
|
| DOI | 10.3389/fnhum.2013.00156 |
| Pubmed ID | |
| Authors |
Rasim Boyacioglu, Christian F. Beckmann, Markus Barth |
| Abstract |
With the advancements in MRI hardware, pulse sequences and reconstruction techniques, many low TR sequences are becoming more and more popular within the functional MRI (fMRI) community. In this study, we have investigated the spectral characteristics of resting state networks (RSNs) with a newly introduced ultra fast fMRI technique, called generalized inverse imaging (GIN). The high temporal resolution of GIN (TR = 50 ms) enables to sample cardiac signals without aliasing into a separate frequency band from the BOLD fluctuations. Respiration related signal changes are, on the other hand, removed from the data without the need for external physiological recordings. We have observed that the variance over the subjects is higher than the variance over RSNs. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Cuba | 1 | 2% |
| Sweden | 1 | 2% |
| Germany | 1 | 2% |
| Unknown | 47 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 14 | 28% |
| Student > Ph. D. Student | 11 | 22% |
| Student > Master | 6 | 12% |
| Professor | 5 | 10% |
| Student > Bachelor | 3 | 6% |
| Other | 8 | 16% |
| Unknown | 3 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 16 | 32% |
| Medicine and Dentistry | 7 | 14% |
| Engineering | 6 | 12% |
| Psychology | 6 | 12% |
| Agricultural and Biological Sciences | 4 | 8% |
| Other | 2 | 4% |
| Unknown | 9 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 23 April 2013.
All research outputs
#20,191,579
of 22,708,120 outputs
Outputs from Frontiers in Human Neuroscience
#6,523
of 7,125 outputs
Outputs of similar age
#248,737
of 280,717 outputs
Outputs of similar age from Frontiers in Human Neuroscience
#817
of 862 outputs
Altmetric has tracked 22,708,120 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,125 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.5. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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 280,717 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 862 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.