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Attention Score in Context
| Title |
Nonlinear Coupling between Cortical Oscillations and Muscle Activity during Isotonic Wrist Flexion
|
|---|---|
| Published in |
Frontiers in Computational Neuroscience, December 2016
|
| DOI | 10.3389/fncom.2016.00126 |
| Pubmed ID | |
| Authors |
Yuan Yang, Teodoro Solis-Escalante, Mark van de Ruit, Frans C. T. van der Helm, Alfred C. Schouten |
| Abstract |
Coupling between cortical oscillations and muscle activity facilitates neuronal communication during motor control. The linear part of this coupling, known as corticomuscular coherence, has received substantial attention, even though neuronal communication underlying motor control has been demonstrated to be highly nonlinear. A full assessment of corticomuscular coupling, including the nonlinear part, is essential to understand the neuronal communication within the sensorimotor system. In this study, we applied the recently developed n:m coherence method to assess nonlinear corticomuscular coupling during isotonic wrist flexion. The n:m coherence is a generalized metric for quantifying nonlinear cross-frequency coupling as well as linear iso-frequency coupling. By using independent component analysis (ICA) and equivalent current dipole source localization, we identify four sensorimotor related brain areas based on the locations of the dipoles, i.e., the contralateral primary sensorimotor areas, supplementary motor area (SMA), prefrontal area (PFA) and posterior parietal cortex (PPC). For all these areas, linear coupling between electroencephalogram (EEG) and electromyogram (EMG) is present with peaks in the beta band (15-35 Hz), while nonlinear coupling is detected with both integer (1:2, 1:3, 1:4) and non-integer (2:3) harmonics. Significant differences between brain areas is shown in linear coupling with stronger coherence for the primary sensorimotor areas and motor association cortices (SMA, PFA) compared to the sensory association area (PPC); but not for the nonlinear coupling. Moreover, the detected nonlinear coupling is similar to previously reported nonlinear coupling of cortical activity to somatosensory stimuli. We suggest that the descending motor pathways mainly contribute to linear corticomuscular coupling, while nonlinear coupling likely originates from sensory feedback. |
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 % |
|---|---|---|
| Switzerland | 1 | 50% |
| Netherlands | 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 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 2% |
| Unknown | 61 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 18 | 29% |
| Student > Master | 8 | 13% |
| Student > Bachelor | 6 | 10% |
| Student > Doctoral Student | 5 | 8% |
| Professor | 5 | 8% |
| Other | 9 | 15% |
| Unknown | 11 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 17 | 27% |
| Engineering | 14 | 23% |
| Nursing and Health Professions | 5 | 8% |
| Medicine and Dentistry | 4 | 6% |
| Agricultural and Biological Sciences | 2 | 3% |
| Other | 5 | 8% |
| Unknown | 15 | 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 16 December 2016.
All research outputs
#14,875,637
of 22,908,162 outputs
Outputs from Frontiers in Computational Neuroscience
#763
of 1,347 outputs
Outputs of similar age
#240,594
of 419,595 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#19
of 34 outputs
Altmetric has tracked 22,908,162 research outputs across all sources so far. This one is in the 33rd percentile – i.e., 33% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,347 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 36th percentile – i.e., 36% 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 419,595 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 34 others from the same source and published within six weeks on either side of this one. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.