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X Demographics
Mendeley readers
Attention Score in Context
| Title |
ICA-based artifact removal diminishes scan site differences in multi-center resting-state fMRI
|
|---|---|
| Published in |
Frontiers in Neuroscience, October 2015
|
| DOI | 10.3389/fnins.2015.00395 |
| Pubmed ID | |
| Authors |
Rogier A. Feis, Stephen M. Smith, Nicola Filippini, Gwenaëlle Douaud, Elise G. P. Dopper, Verena Heise, Aaron J. Trachtenberg, John C. van Swieten, Mark A. van Buchem, Serge A. R. B. Rombouts, Clare E. Mackay |
| Abstract |
Resting-state fMRI (R-fMRI) has shown considerable promise in providing potential biomarkers for diagnosis, prognosis and drug response across a range of diseases. Incorporating R-fMRI into multi-center studies is becoming increasingly popular, imposing technical challenges on data acquisition and analysis, as fMRI data is particularly sensitive to structured noise resulting from hardware, software, and environmental differences. Here, we investigated whether a novel clean up tool for structured noise was capable of reducing center-related R-fMRI differences between healthy subjects. We analyzed three Tesla R-fMRI data from 72 subjects, half of whom were scanned with eyes closed in a Philips Achieva system in The Netherlands, and half of whom were scanned with eyes open in a Siemens Trio system in the UK. After pre-statistical processing and individual Independent Component Analysis (ICA), FMRIB's ICA-based X-noiseifier (FIX) was used to remove noise components from the data. GICA and dual regression were run and non-parametric statistics were used to compare spatial maps between groups before and after applying FIX. Large significant differences were found in all resting-state networks between study sites before using FIX, most of which were reduced to non-significant after applying FIX. The between-center difference in the medial/primary visual network, presumably reflecting a between-center difference in protocol, remained statistically significant. FIX helps facilitate multi-center R-fMRI research by diminishing structured noise from R-fMRI data. In doing so, it improves combination of existing data from different centers in new settings and comparison of rare diseases and risk genes for which adequate sample size remains a challenge. |
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 % |
|---|---|---|
| United States | 2 | 67% |
| Switzerland | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 94 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 1% |
| Unknown | 93 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 28 | 30% |
| Student > Master | 13 | 14% |
| Researcher | 11 | 12% |
| Student > Doctoral Student | 6 | 6% |
| Student > Bachelor | 4 | 4% |
| Other | 16 | 17% |
| Unknown | 16 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 32 | 34% |
| Psychology | 11 | 12% |
| Engineering | 9 | 10% |
| Computer Science | 5 | 5% |
| Medicine and Dentistry | 5 | 5% |
| Other | 9 | 10% |
| Unknown | 23 | 24% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 19 November 2015.
All research outputs
#15,739,010
of 25,371,288 outputs
Outputs from Frontiers in Neuroscience
#6,686
of 11,538 outputs
Outputs of similar age
#152,252
of 295,215 outputs
Outputs of similar age from Frontiers in Neuroscience
#79
of 142 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
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 is in the 39th percentile – i.e., 39% 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 295,215 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 142 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.