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