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Summary cerebrovascular reactivity (CVR) was assessed in 29 first ever lacunar stroke patients. After ACZ infusion, median phase angle decreased significantly (p<0.005 Wilcoxon) to 0.77 rad compared to a pre-test baseline value of 1.05 rad, indicating less efficient dCA due to ACZ. However, post-test phase values are still mostly within the normal range. It can be concluded that CVR testing with body weight adjusted infusion of ACZ lowers dCA performance but by no means exhausts dCA, suggesting that in this way maximal CVR is not determined. Cerebrovascular dysfunction plays a role not only in vascular causes of cognitive impairment but also in Alzheimer’s disease (AD). In chapter 4 we hypothesized that dynamic cerebral autoregulation is impaired in patients with AD compared to subjects with mild cognitive impairment (MCI) and controls (C). Apart from measures of dCA also cerebrovascular resistance index CVRi was 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. Results were also evaluated using a 3-parameter windkessel model (WKM). CVRi was significantly higher in AD compared to both MCI and C. Five MCI patients who converted to AD during the course of the study also had higher CVRi compared to non-converters. No significant differences in dCA gain and phase were found. In terms of the WKM approach, in the order !MCI!AD groups showed about equal arterial resistance and peripheral compliance, but increased peripheral vasculature resistance. In AD patients compared to MCI patients and controls we found increased CVRi, whereas dCA parameters do not seem to differentiate AD patients. For MCI patients CVRi might have predictive value in developing AD. Chapter 5 describes how parameter estimation of visually evoked flow responses (VEFR) may be improved when the effect of blood variations through dCA are incorporated in the model. VEFR are often quantified using a dynamic model of neurovascular coupling (NVC). The VEFR 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 VEFR is evaluated by separately modeling the dCA induced effects of beat-to-beat measured blood pressure related CBFV changes. VEFR NVC parameters of 71 subjects are estimated using two models: the RG model is a well-known second order dynamic NVC model. The second RGCA 139


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