Chapter 5 blood pressure responses. Furthermore, spontaneous blood pressure changes that evoke a CA response can result in cerebral blood flow variations that may not be negligible after averaging multiple responses for an averaged NVC response. Therefore, the effect of beat-to-beat blood pressure related variation of CBFV in the averaged NVC response needs to be investigated in more detail. In this study two approaches of quantifying the visually evoked flow response will be compared: first evaluating the flow response as if exclusively evoked by the stimulus and second supposing the flow response to be the addition of on the one hand the stimulus evoked NVC response and on the other the flow resulting from the autoregulatory response due to blood pressure changes. To quantify the stimulus evoked NVC shape a second order linear model (RG) was used intro- duced by Rosengartenet al 20, 21. This model was adapted from a model for cerebral autoregulation developed by Tieckset al 29. The RG output signal is fitted to the measured NVC response while the input is a zero-to-one step func- tion (zero : visual stimulus off, 1: visual stimulus on). The RG model quantifies the dynamics of the visual evoked response with four model parameters namely gain K, attenuation , natural frequency! and rate time Tv. For cerebral autoregula- tion we use the original Tiecks model with system’s input cerebral perfusion pressure and cerebral blood flow velocity as output. The aim of this study is to investigate the effect of blood pressure changes via the action of cerebral autoregulation on visual evoked flow responses and eventually to correct the responses for these effects. The recorded CBFV response is as- sumed to be the linear sum of the blood flow velocity response evoked by the visual stimulus and flow velocity changes due to blood pressure variations under dCA control. Consequently, two different models will be used to fit the recorded CBFV re- sponse. The first only consists of the original RG stimulus response model quan- tifying the NVC response. The second RGCA model, shown in figure 1, adds to the output of the RG model a CBFV contribution from the CA model based on a beat-to-beat ABP input. Fitting the RGCA model to the visual evoked flow re- sponse, may result in different RG part parameters compared to the single RG model parameters, since part of the output variance will be explained from blood pressure related CBFV changes. We hypothesize total fit error is less for the RGCA model compared to the RG model and blood pressure corrected NVC responses result in increased parameter precision. 82
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