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Attention Score in Context
| Title |
Spatiotemporal imaging of complexity
|
|---|---|
| Published in |
Frontiers in Computational Neuroscience, January 2013
|
| DOI | 10.3389/fncom.2012.00101 |
| Pubmed ID | |
| Authors |
Stephen E. Robinson, Arnold J. Mandell, Richard Coppola |
| Abstract |
What are the functional neuroimaging measurements required for more fully characterizing the events and locations of neocortical activity? A prime assumption has been that modulation of cortical activity will inevitably be reflected in changes in energy utilization (for the most part) changes of glucose and oxygen consumption. Are such a measures complete and sufficient? More direct measures of cortical electrophysiological activity show event or task-related modulation of amplitude or band-limited oscillatory power. Using magnetoencephalography (MEG), these measures have been shown to correlate well with energy utilization sensitive BOLD fMRI. In this paper, we explore the existence of state changes in electrophysiological cortical activity that can occur independently of changes in averaged amplitude, source power or indices of metabolic rates. In addition, we demonstrate that such state changes can be described by applying a new measure of complexity, rank vector entropy (RVE), to source waveform estimates from beamformer-processed MEG. RVE is a non-parametric symbolic dynamic informational entropy measure that accommodates the wide dynamic range of measured brain signals while resolving its temporal variations. By representing the measurements by their rank values, RVE overcomes the problem of defining embedding space partitions without resorting to signal compression. This renders RVE-independent of absolute signal amplitude. In addition, this approach is robust, being relatively free of tunable parameters. We present examples of task-free and task-dependent MEG demonstrating that RVE provides new information by uncovering hidden dynamical structure in the apparent turbulent (or chaotic) dynamics of spontaneous cortical activity. |
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 % |
|---|---|---|
| United Kingdom | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 7% |
| France | 1 | 2% |
| Germany | 1 | 2% |
| Spain | 1 | 2% |
| United Kingdom | 1 | 2% |
| Unknown | 49 | 86% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 16 | 28% |
| Researcher | 10 | 18% |
| Student > Bachelor | 5 | 9% |
| Student > Master | 4 | 7% |
| Student > Postgraduate | 3 | 5% |
| Other | 8 | 14% |
| Unknown | 11 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 11 | 19% |
| Psychology | 10 | 18% |
| Agricultural and Biological Sciences | 9 | 16% |
| Engineering | 5 | 9% |
| Computer Science | 3 | 5% |
| Other | 6 | 11% |
| Unknown | 13 | 23% |
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 22 December 2013.
All research outputs
#14,742,867
of 22,693,205 outputs
Outputs from Frontiers in Computational Neuroscience
#766
of 1,336 outputs
Outputs of similar age
#175,232
of 280,672 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#63
of 131 outputs
Altmetric has tracked 22,693,205 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,336 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. 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 280,672 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 131 others from the same source and published within six weeks on either side of this one. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.