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Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression
Published on: December 10, 2014
Quantification of dynamic cerebral autoregulation and CO2 dynamic vasomotor reactivity impairment in essential
Vasilis Z Marmarelis1, Dae C Shin1, Mareike Oesterreich2
1Biomedical Simulations Resource Center, University of Southern California, Los Angeles, California.
Insights
Essential hypertension impairs dynamic cerebral autoregulation (DCA) and dynamic vasomotor reactivity (DVR). Novel principal dynamic modes (PDMs) methodology reveals significant differences in these functions between hypertensive patients and controls.
Area of Science:
- Neuroscience
- Cardiovascular Physiology
- Biomedical Engineering
Background:
- Dynamic cerebral autoregulation (DCA) and dynamic vasomotor reactivity (DVR) are crucial for maintaining stable cerebral blood flow.
- Essential hypertension is known to affect cerebrovascular function, but precise mechanisms require further elucidation.
- Previous analyses of DCA and DVR have limitations, particularly with short or noisy hemodynamic data.
Purpose of the Study:
- To introduce a novel methodology using principal dynamic modes (PDMs) for improved estimation of dynamic cerebral autoregulation (DCA) and dynamic vasomotor reactivity (DVR).
- To quantify and compare DCA and DVR indexes in patients with essential hypertension and normotensive controls.
- To explore the potential of PDMs in identifying specific physiological mechanisms affected by essential hypertension.
Main Methods:
- Extraction of input-output predictive models from spontaneous time series hemodynamic data using principal dynamic modes (PDMs).
- Analysis included 24 patients with essential hypertension and 20 normotensive control subjects under resting conditions.
- Development of model-based indexes to quantify DCA and dynamic vasomotor reactivity (DVR).
Main Results:
- Model-based DCA and DVR indexes were significantly different (P < 0.05) in hypertensive patients compared to control subjects.
- Significant differences were observed in the relative contribution of three PDMs to model output prediction between groups.
- The novel PDM methodology demonstrated improved estimation accuracy for relatively short and noisy data.
Conclusions:
- The novel PDM-based methodology provides accurate diagnostic indexes for DCA and DVR in hypertension.
- Essential hypertension significantly alters dynamic cerebral autoregulation and dynamic vasomotor reactivity.
- PDM analysis offers a promising approach to unraveling the specific physiological mechanisms impacted by essential hypertension.
Abstract:
The study of dynamic cerebral autoregulation (DCA) in essential hypertension has received considerable attention because of its clinical importance. Several studies have examined the dynamic relationship between spontaneous beat-to-beat arterial blood pressure data and contemporaneous cerebral blood flow velocity measurements (obtained via transcranial Doppler at the middle cerebral arteries) in the form of a linear input-output model using transfer function analysis. This analysis is more reliable when the contemporaneous effects of changes in blood CO2 tension are also taken into account, because of the significant effects of CO2 dynamic vasomotor reactivity (DVR) upon cerebral flow. In this article, we extract such input-output predictive models from spontaneous time series hemodynamic data of 24 patients with essential hypertension and 20 normotensive control subjects under resting conditions, using the novel methodology of principal dynamic modes (PDMs) that achieves improved estimation accuracy over previous methods for relatively short and noisy data. The obtained data-based models are subsequently used to compute indexes and markers that quantify DCA and DVR in each subject or patient and therefore can be used to assess the effects of essential hypertension. These model-based DCA and DVR indexes were properly defined to capture the observed effects of DCA and VR and found to be significantly different (P < 0.05) in the hypertensive patients. We also found significant differences between patients and control subjects in the relative contribution of three PDMs to the model output prediction, a finding that offers the prospect of identifying the physiological mechanisms affected by essential hypertension when the PDMs are interpreted in terms of specific physiological mechanisms.NEW & NOTEWORTHY This article presents novel model-based methodology for obtaining diagnostic indexes of dynamic cerebral autoregulation and dynamic vasomotor reactivity in hypertension.
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