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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Improving the Nulling Beamformer Using Subspace Suppression
|
|---|---|
| Published in |
Frontiers in Computational Neuroscience, June 2018
|
| DOI | 10.3389/fncom.2018.00035 |
| Pubmed ID | |
| Authors |
Kunjan D. Rana, Matti S. Hämäläinen, Lucia M. Vaina |
| Abstract |
Magnetoencephalography (MEG) captures the magnetic fields generated by neuronal current sources with sensors outside the head. In MEG analysis these current sources are estimated from the measured data to identify the locations and time courses of neural activity. Since there is no unique solution to this so-called inverse problem, multiple source estimation techniques have been developed. The nulling beamformer (NB), a modified form of the linearly constrained minimum variance (LCMV) beamformer, is specifically used in the process of inferring interregional interactions and is designed to eliminate shared signal contributions, or cross-talk, between regions of interest (ROIs) that would otherwise interfere with the connectivity analyses. The nulling beamformer applies the truncated singular value decomposition (TSVD) to remove small signal contributions from a ROI to the sensor signals. However, ROIs with strong crosstalk will have high separating power in the weaker components, which may be removed by the TSVD operation. To address this issue we propose a new method, the nulling beamformer with subspace suppression (NBSS). This method, controlled by a tuning parameter, reweights the singular values of the gain matrix mapping from source to sensor space such that components with high overlap are reduced. By doing so, we are able to measure signals between nearby source locations with limited cross-talk interference, allowing for reliable cortical connectivity analysis between them. In two simulations, we demonstrated that NBSS reduces cross-talk while retaining ROIs' signal power, and has higher separating power than both the minimum norm estimate (MNE) and the nulling beamformer without subspace suppression. We also showed that NBSS successfully localized the auditory M100 event-related field in primary auditory cortex, measured from a subject undergoing an auditory localizer task, and suppressed cross-talk in a nearby region in the superior temporal sulcus. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 50% |
| United States | 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 % |
|---|---|---|
| Student > Ph. D. Student | 6 | 33% |
| Researcher | 2 | 11% |
| Student > Master | 2 | 11% |
| Professor | 1 | 6% |
| Other | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 5 | 28% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 6 | 33% |
| Neuroscience | 3 | 17% |
| Psychology | 2 | 11% |
| Agricultural and Biological Sciences | 1 | 6% |
| Physics and Astronomy | 1 | 6% |
| Other | 0 | 0% |
| Unknown | 5 | 28% |
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 20 June 2018.
All research outputs
#18,639,173
of 23,090,520 outputs
Outputs from Frontiers in Computational Neuroscience
#1,059
of 1,358 outputs
Outputs of similar age
#253,548
of 328,349 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#31
of 31 outputs
Altmetric has tracked 23,090,520 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 1,358 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.1. This one is in the 13th percentile – i.e., 13% 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 328,349 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 31 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.