Visually evoked blood flow responses and interaction with dynamic cerebral autoregulation: correction for blood pressure variation.

Visually evoked flow responses recorded using transcranial Doppler ultrasonography are often quantified using a dynamic model of neurovascular coupling. The evoked flow response is seen as the model's response to a visual step input stimulus. However, the continuously active process of dynamic cerebral autoregulation (dCA) compensating cerebral blood flow for blood pressure fluctuations may induce changes of cerebral blood flow velocity (CBFV) as well. The effect of blood pressure variability on the flow response is evaluated by separately modeling the dCA-induced effects of beat-to-beat measured blood pressure related CBFV changes. Parameters of 71 subjects are estimated using an existing, well-known second order dynamic neurovascular coupling model proposed by Rosengarten et al., and a new model extending the existing model with a CBFV contributing component as the output of a dCA model driven by blood pressure as input. Both models were evaluated for mean and systolic CBFV responses. The model-to-data fit errors of mean and systolic blood pressure for the new model were significantly lower compared to the existing model: mean: 0.8%±0.6 vs. 2.4%±2.8, p<0.001; systolic: 1.5%±1.2 vs. 2.2%±2.6, p<0.001. The confidence bounds of all estimated neurovascular coupling model parameters were significantly (p<0.005) narrowed for the new model. In conclusion, blood pressure correction of visual evoked flow responses by including cerebral autoregulation in model fitting of averaged responses results in significantly lower fit errors and by that in more reliable model parameter estimation. Blood pressure correction is more effective when mean instead of systolic CBFV responses are used. Measurement and quantification of neurovascular coupling should include beat-to-beat blood pressure measurement.

A lumped parameter model of cerebral blood flow control combining cerebral autoregulation and neurovascular coupling.

Cerebral blood flow regulation is based on a variety of different mechanisms, of which the relative regulatory role remains largely unknown. The cerebral regulatory system expresses two regulatory properties: cerebral autoregulation and neurovascular coupling. Since partly the same mechanisms play a role in cerebral autoregulation and neurovascular coupling, this study aimed to develop a physiologically based mathematical model of cerebral blood flow regulation combining these properties. A lumped parameter model of the P2 segment of the posterior cerebral artery and its distal vessels was constructed. Blood flow regulation is exerted at the arteriolar level by vascular smooth muscle and implements myogenic, shear stress based, neurogenic, and metabolic mechanisms. In eight healthy subjects, cerebral autoregulation and neurovascular coupling were challenged by squat-stand maneuvers and visual stimulation using a checkerboard pattern, respectively. Cerebral blood flow velocity was measured using transcranial Doppler, whereas blood pressure was measured by finger volume clamping. In seven subjects, the model proposed fits autoregulation and neurovascular coupling measurement data well. Myogenic regulation is found to dominate the autoregulatory response. Neurogenic regulation, although only implemented as a first-order mechanism, describes neurovascular coupling responses to a great extent. It is concluded that our single, integrated model of cerebral blood flow control may be used to identify the main mechanisms affecting cerebral blood flow regulation in individual subjects.

Dynamic cerebral autoregulation in subjects with Alzheimer's disease, mild cognitive impairment, and controls: evidence for increased peripheral vascular resistance with possible predictive value.

Cerebrovascular dysfunction plays a role not only in vascular causes of cognitive impairment but also in Alzheimer's disease (AD). We hypothesized that cerebral autoregulation is impaired in patients with AD compared to subjects with mild cognitive impairment (MCI) and controls. Dynamic cerebral autoregulation (dCA) was investigated in 17 AD patients, 19 MCI subjects, and 20 controls (C). Groups were matched for age, gender, and level of education. Electrocardiogram and non-invasive finger arterial blood pressure were measured and transcranial doppler ultrasonography was used to measure cerebral blood flow velocity in right and left middle cerebral artery (MCA). Cerebrovascular resistance index (CVRi) was also computed. dCA in supine position was quantified based on spontaneous blood pressure variations by computation of the linear transfer function between arterial blood pressure and MCA cerebral blood flow velocity. dCA gain and phase were evaluated for different frequency bands. Results were also evaluated using a 3-parameter windkessel model (WKM). CVRi was significantly higher in AD (2.9 ± 0.2) compared to both MCI (2.3 ± 0.1, p = 0.02) and C (2.1 ± 0.1 mmHgs/cm, p = 0.002). Five MCI patients who converted to AD during the course of the study also had higher CVRi compared to non-converters (2.8 ± 0.6 versus 2.1 ± 0.5 mmHgs/cm, p < 0.05). No significant differences in dCA gain and phase were found. In terms of the WKM approach, in the order C→MCI→AD groups showed about equal arterial resistance and peripheral compliance, but increased peripheral vasculature resistance (26 ± 2 versus 36 ± 3 mmHgs/ml in C resp. AD, p = 0.004). In conclusion, AD patients compared to MCI patients and controls have increased CVRi, whereas dCA parameters do not seem to differentiate AD patients. For MCI patients, CVRi might have predictive value in developing AD.