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Modeling Cerebral Autoregulation During Orthostatic Stress in the Presence of Aging and Hypertension.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Modeling Cerebral Autoregulation During Orthostatic Stress in the Presence of Aging and Hypertension./
Author:	Mader, Gregory Charles.
Description:	1 online resource (158 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.
Contained By:	Dissertation Abstracts International78-08B(E).
Subject:	Mathematics. -
Online resource:	click for full text (PQDT)
ISBN:	9781369621532

Modeling Cerebral Autoregulation During Orthostatic Stress in the Presence of Aging and Hypertension.
Mader, Gregory Charles.

Modeling Cerebral Autoregulation During Orthostatic Stress in the Presence of Aging and Hypertension. - 1 online resource (158 pages)

Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

Cerebral autoregulation refers to the brain's control mechanisms responsible for maintaining cerebral blood flowat an appropriate, approximately constant, level despite changes in arterial blood pressure.Metabolic, myogenic, shear-dependent, and neurogenic influences work collectively to ensure adequate flow and necessary distribution of nutrients to cerebral tissue. Cerebral autoregulation is typically studied from two perspectives. First, static autoregulation defines the steady-state relationship between blood pressure and blood flow, typically illustrated by the autoregulatory plateau curve. The second perspective is dynamic cerebral autoregulation, which describes the transient response of blood flowvelocity to changes in arterial pressure. This study combines the two modeling methodologies deriving a simple pulsatile nonlinear model that uses measured pressure values as an input to quantitatively predict cerebral blood flow dynamics during postural change. The model is motivated by the analysis of time-varying dynamics observed in the filtered and pulsatile measurements of flow and pressure, indicating a nonlinear response. The present study addresses data analysis, model development, and shows how structural and practical parameter identifiability methods can be used to demonstrate that the model displays correct qualitative and quantitative behavior. Finally parameter estimation is used to show that the model can accurately predict middle cerebral blood flow velocity measurements recorded during postural change. Current methods do not detect a difference in the performance of cerebral autoregulation due to aging or hypertension, despite the many cerebrovascular changes that occur in each of those states. In this study, we show that by accounting for pulsatility and nonlinearity, it is possible to devise a measure that can distinguish between three patient groups: healthy young, healthy elderly, and hypertensive elderly. Results are obtained by analyzing model dynamics and estimating patient specific model parameters for each subject. In addition, nonlinear mixed effects analysis was used to test if all subjects belong to the same population with equal population parameter values, or if the population parameters vary among the three subgroups. Future work could entail using a detailed theoretical autoregulation model as a tool for generating various static curves within the model framework, eventually arriving at a clinically useful physiologically-based index for cerebral autoregulation.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369621532Subjects--Topical Terms:

527692
Mathematics.
Index Terms--Genre/Form:

554714
Electronic books.

Modeling Cerebral Autoregulation During Orthostatic Stress in the Presence of Aging and Hypertension.
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520 $a Cerebral autoregulation refers to the brain's control mechanisms responsible for maintaining cerebral blood flowat an appropriate, approximately constant, level despite changes in arterial blood pressure.Metabolic, myogenic, shear-dependent, and neurogenic influences work collectively to ensure adequate flow and necessary distribution of nutrients to cerebral tissue. Cerebral autoregulation is typically studied from two perspectives. First, static autoregulation defines the steady-state relationship between blood pressure and blood flow, typically illustrated by the autoregulatory plateau curve. The second perspective is dynamic cerebral autoregulation, which describes the transient response of blood flowvelocity to changes in arterial pressure. This study combines the two modeling methodologies deriving a simple pulsatile nonlinear model that uses measured pressure values as an input to quantitatively predict cerebral blood flow dynamics during postural change. The model is motivated by the analysis of time-varying dynamics observed in the filtered and pulsatile measurements of flow and pressure, indicating a nonlinear response. The present study addresses data analysis, model development, and shows how structural and practical parameter identifiability methods can be used to demonstrate that the model displays correct qualitative and quantitative behavior. Finally parameter estimation is used to show that the model can accurately predict middle cerebral blood flow velocity measurements recorded during postural change. Current methods do not detect a difference in the performance of cerebral autoregulation due to aging or hypertension, despite the many cerebrovascular changes that occur in each of those states. In this study, we show that by accounting for pulsatility and nonlinearity, it is possible to devise a measure that can distinguish between three patient groups: healthy young, healthy elderly, and hypertensive elderly. Results are obtained by analyzing model dynamics and estimating patient specific model parameters for each subject. In addition, nonlinear mixed effects analysis was used to test if all subjects belong to the same population with equal population parameter values, or if the population parameters vary among the three subgroups. Future work could entail using a detailed theoretical autoregulation model as a tool for generating various static curves within the model framework, eventually arriving at a clinically useful physiologically-based index for cerebral autoregulation.
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