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A Model of Brain Circulation and Metabolism: NIRS Signal Changes during Physiological Challenges

Overview of attention for article published in PLoS Computational Biology, November 2008
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Title
A Model of Brain Circulation and Metabolism: NIRS Signal Changes during Physiological Challenges
Published in
PLoS Computational Biology, November 2008
DOI 10.1371/journal.pcbi.1000212
Pubmed ID
Authors

Murad Banaji, Alfred Mallet, Clare E. Elwell, Peter Nicholls, Chris E. Cooper

Abstract

We construct a model of brain circulation and energy metabolism. The model is designed to explain experimental data and predict the response of the circulation and metabolism to a variety of stimuli, in particular, changes in arterial blood pressure, CO(2) levels, O(2) levels, and functional activation. Significant model outputs are predictions about blood flow, metabolic rate, and quantities measurable noninvasively using near-infrared spectroscopy (NIRS), including cerebral blood volume and oxygenation and the redox state of the Cu(A) centre in cytochrome c oxidase. These quantities are now frequently measured in clinical settings; however the relationship between the measurements and the underlying physiological events is in general complex. We anticipate that the model will play an important role in helping to understand the NIRS signals, in particular, the cytochrome signal, which has been hard to interpret. A range of model simulations are presented, and model outputs are compared to published data obtained from both in vivo and in vitro settings. The comparisons are encouraging, showing that the model is able to reproduce observed behaviour in response to various stimuli.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 153 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 4%
United Kingdom 5 3%
Japan 3 2%
Netherlands 1 <1%
Brazil 1 <1%
Ireland 1 <1%
Germany 1 <1%
Italy 1 <1%
Unknown 134 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 34 22%
Researcher 25 16%
Student > Master 18 12%
Professor > Associate Professor 15 10%
Professor 11 7%
Other 31 20%
Unknown 19 12%
Readers by discipline Count As %
Engineering 31 20%
Medicine and Dentistry 22 14%
Agricultural and Biological Sciences 17 11%
Physics and Astronomy 14 9%
Neuroscience 9 6%
Other 36 24%
Unknown 24 16%