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Attention Score in Context
| Title |
Approximate Subject Specific Pseudo MRI from an Available MRI Dataset for MEG Source Imaging
|
|---|---|
| Published in |
Frontiers in Neuroinformatics, August 2017
|
| DOI | 10.3389/fninf.2017.00050 |
| Pubmed ID | |
| Authors |
Bakul Gohel, Sanghyun Lim, Min-Young Kim, Hyukchan Kwon, Kiwoong Kim |
| Abstract |
Computation of headmodel and sourcemodel from the subject's MRI scan is an essential step for source localization of magnetoencephalography (MEG) (or EEG) sensor signals. In the absence of a real MRI scan, pseudo MRI (i.e., associated headmodel and sourcemodel) is often approximated from an available standard MRI template or pool of MRI scans considering the subject's digitized head surface. In the present study, we approximated two types of pseudo MRI (i.e., associated headmodel and sourcemodel) using an available pool of MRI scans with the focus on MEG source imaging. The first was the first rank pseudo MRI; that is, the MRI scan in the dataset having the lowest objective registration error (ORE) after being registered (rigid body transformation with isotropic scaling) to the subject's digitized head surface. The second was the averaged rank pseudo MRI that is generated by averaging of headmodels and sourcemodels from multiple MRI scans respectively, after being registered to the subject's digitized head surface. Subject level analysis showed that the mean upper bound of source location error for the approximated sourcemodel in reference to the real one was 10 ± 3 mm for the averaged rank pseudo MRI, which was significantly lower than the first rank pseudo MRI approach. Functional group source response in the brain to visual stimulation in the form of event-related power (ERP) at the time latency of peak amplitude showed noticeably identical source distribution for first rank pseudo MRI, averaged rank pseudo MRI, and real MRI. The source localization error for functional peak response was significantly lower for averaged rank pseudo MRI compared to first rank pseudo MRI. We conclude that it is feasible to use approximated pseudo MRI, particularly the averaged rank pseudo MRI, as a substitute for real MRI without losing the generality of the functional group source response. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 50% |
| Switzerland | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 18 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 4 | 22% |
| Student > Ph. D. Student | 4 | 22% |
| Student > Master | 3 | 17% |
| Student > Doctoral Student | 2 | 11% |
| Other | 1 | 6% |
| Other | 4 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 6 | 33% |
| Computer Science | 2 | 11% |
| Physics and Astronomy | 2 | 11% |
| Psychology | 2 | 11% |
| Medicine and Dentistry | 2 | 11% |
| Other | 1 | 6% |
| Unknown | 3 | 17% |
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 17 August 2017.
All research outputs
#18,567,744
of 22,997,544 outputs
Outputs from Frontiers in Neuroinformatics
#627
of 753 outputs
Outputs of similar age
#243,462
of 317,854 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#18
of 20 outputs
Altmetric has tracked 22,997,544 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 753 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.2. This one is in the 9th percentile – i.e., 9% of its peers scored the same or lower than it.
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