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Modeling Cerebral Blood Flow Velocity During Orthostatic Stress.

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Title: Modeling Cerebral Blood Flow Velocity During Orthostatic Stress.
Authors: Mader, Greg1, Olufsen, Mette1, Mahdi, Adam2 adam.mahdi@eng.ox.ac.uk
Source: Annals of Biomedical Engineering. Aug2015, Vol. 43 Issue 8, p1748-1758. 11p. 1 Diagram, 2 Charts, 4 Graphs.
Subjects: Cerebral circulation, Blood pressure, Viscoelasticity, Sensitivity analysis, Control groups, Qualitative research
Abstract: Cerebral autoregulation refers to the physiological process that maintains stable cerebral blood flow (CBF) during changes in arterial blood pressure (ABP). In this study, we propose a simple, nonlinear quantitative model with only four parameters that can predict CBF velocity as a function of ABP. The model was motivated by the viscoelastic-like behavior observed in the data collected during postural change from sitting to standing. Qualitative testing of the model involved analysis of dynamic responses to step-changes in pressure both within and outside the autoregulatory range, while quantitative testing was used to show that the model can fit dynamics observed in data measured from a healthy young and a healthy elderly subject. The latter involved analysis of structural and practical identifiability, sensitivity analysis, and parameter estimation. Results showed that the model is able to reproduce observed overshoot and adaptation and predict the different responses in the healthy young and the healthy elderly subject. For the healthy young subject, the overshoot was significantly more pronounced than for the elderly subject, but the recovery time was longer for the young subject. These differences resulted in different parameter values estimated using the two datasets. [ABSTRACT FROM AUTHOR]
Copyright of Annals of Biomedical Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="AR" term="%22Mader%2C+Greg%22">Mader, Greg</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Olufsen%2C+Mette%22">Olufsen, Mette</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Mahdi%2C+Adam%22">Mahdi, Adam</searchLink><relatesTo>2</relatesTo><i> adam.mahdi@eng.ox.ac.uk</i>
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  Data: <searchLink fieldCode="JN" term="%22Annals+of+Biomedical+Engineering%22">Annals of Biomedical Engineering</searchLink>. Aug2015, Vol. 43 Issue 8, p1748-1758. 11p. 1 Diagram, 2 Charts, 4 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Cerebral+circulation%22">Cerebral circulation</searchLink><br /><searchLink fieldCode="DE" term="%22Blood+pressure%22">Blood pressure</searchLink><br /><searchLink fieldCode="DE" term="%22Viscoelasticity%22">Viscoelasticity</searchLink><br /><searchLink fieldCode="DE" term="%22Sensitivity+analysis%22">Sensitivity analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Control+groups%22">Control groups</searchLink><br /><searchLink fieldCode="DE" term="%22Qualitative+research%22">Qualitative research</searchLink>
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  Data: Cerebral autoregulation refers to the physiological process that maintains stable cerebral blood flow (CBF) during changes in arterial blood pressure (ABP). In this study, we propose a simple, nonlinear quantitative model with only four parameters that can predict CBF velocity as a function of ABP. The model was motivated by the viscoelastic-like behavior observed in the data collected during postural change from sitting to standing. Qualitative testing of the model involved analysis of dynamic responses to step-changes in pressure both within and outside the autoregulatory range, while quantitative testing was used to show that the model can fit dynamics observed in data measured from a healthy young and a healthy elderly subject. The latter involved analysis of structural and practical identifiability, sensitivity analysis, and parameter estimation. Results showed that the model is able to reproduce observed overshoot and adaptation and predict the different responses in the healthy young and the healthy elderly subject. For the healthy young subject, the overshoot was significantly more pronounced than for the elderly subject, but the recovery time was longer for the young subject. These differences resulted in different parameter values estimated using the two datasets. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Annals of Biomedical Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Aug2015
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