Chapter 5 Abstract Visually evoked flow responses (VEFR) recorded using transcranial Doppler ultrasonography 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 auto- regulation (dCA) compensating cerebral blood flow for blood pressure fluctua- tions 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 proposed by Rosen- gartenet al 21. The second RGCA model extends the RG model with a CBFV contributing component being the output of a dCA model driven by blood pres- sure as input. Both models were evaluated for mean and systolic CBFV VEFR responses. The model-to-data fit errors from the RGCA mean and systolic blood pressure cor- rected model were significantly lower compared to the RG 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 confi- dence bounds of all RGCA estimated NVC model parameters were significantly (p<0.005) narrowed. In conclusion, blood pressure correction of NVC responses by including cerebral autoregulation in model fitting of averaged VEFR responses results in signifi- cantly 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 NVC should include beat-to-beat blood pressure measurement. 80
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