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Neurovascular coupling: a parallel implementation

Overview of attention for article published in Frontiers in Computational Neuroscience, September 2015
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Title
Neurovascular coupling: a parallel implementation
Published in
Frontiers in Computational Neuroscience, September 2015
DOI 10.3389/fncom.2015.00109
Pubmed ID
Authors

Katharina Dormanns, Richard G. Brown, Tim David

Abstract

A numerical model of neurovascular coupling (NVC) is presented based on neuronal activity coupled to vasodilation/contraction models via the astrocytic mediated perivascular K(+) and the smooth muscle cell (SMC) Ca(2+) pathway termed a neurovascular unit (NVU). Luminal agonists acting on P2Y receptors on the endothelial cell (EC) surface provide a flux of inositol trisphosphate (IP3) into the endothelial cytosol. This concentration of IP3 is transported via gap junctions between EC and SMC providing a source of sarcoplasmic derived Ca(2+) in the SMC. The model is able to relate a neuronal input signal to the corresponding vessel reaction (contraction or dilation). A tissue slice consisting of blocks, each of which contain an NVU is connected to a space filling H-tree, simulating a perfusing arterial tree (vasculature) The model couples the NVUs to the vascular tree via a stretch mediated Ca(2+) channel on both the EC and SMC. The SMC is induced to oscillate by increasing an agonist flux in the EC and hence increased IP3 induced Ca(2+) from the SMC stores with the resulting calcium-induced calcium release (CICR) oscillation inhibiting NVC thereby relating blood flow to vessel contraction and dilation following neuronal activation. The coupling between the vasculature and the set of NVUs is relatively weak for the case with agonist induced where only the Ca(2+) in cells inside the activated area becomes oscillatory however, the radii of vessels both inside and outside the activated area oscillate (albeit small for those outside). In addition the oscillation profile differs between coupled and decoupled states with the time required to refill the cytosol with decreasing Ca(2+) and increasing frequency with coupling. The solution algorithm is shown to have excellent weak and strong scaling. Results have been generated for tissue slices containing up to 4096 blocks.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
New Zealand 1 4%
Unknown 23 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 29%
Researcher 5 21%
Student > Bachelor 2 8%
Student > Postgraduate 2 8%
Professor 1 4%
Other 3 13%
Unknown 4 17%
Readers by discipline Count As %
Neuroscience 5 21%
Mathematics 4 17%
Agricultural and Biological Sciences 3 13%
Computer Science 2 8%
Engineering 2 8%
Other 3 13%
Unknown 5 21%
Attention Score in Context

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 15 September 2015.
All research outputs
#17,773,420
of 22,828,180 outputs
Outputs from Frontiers in Computational Neuroscience
#960
of 1,343 outputs
Outputs of similar age
#181,250
of 268,887 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#27
of 34 outputs
Altmetric has tracked 22,828,180 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,343 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 21st percentile – i.e., 21% 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 268,887 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% 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 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.