# Using Coherent Hemodynamic Spectroscopy Model to Investigate Cardiac Arrest
## 3 Results  
The normal baseline ranges for the model parameters were taken from the literature [1]; values used for simulating changes in Section 3.1 and values obtained by applying the inverse algorithm to the animal data in Section 3.2 are summarized in Table 2.  
Numerical Simulation of Effects of Baseline Fluctuations, Blood Flow Modulation, and Cardiac Arrest on Brain  
Figure 2 shows changes in the simulated and computed (using the forward CHS model) variables during various epochs shown by the vertical lines. Figure 2a shows the computed responses in the tissue microvascular concentrations of oxy- and deoxy-hemoglobin ([_HHb_] and [_HbO_2]) to the simulated changes in the tissue total hemoglobin concentration ([_HHb_] = [_HHb_] + [_HbO_2]) and _CBF_ shown in Figure 2b, which are the inputvariables of the CHS model. The simulated \(CBF\) changes represent periodic modulation (which can be caused by various experimental paradigms such as periodic changes in the amount of the inhaled oxygen or occlusion of carotid arteries), \(CBF\) cessation due to cardiac arrest, and \(CBF\) restoration due to CPR and ROSC. The simulated \([tHb]\) changes shown in Figure 2b represent typical baseline variations in the microvascular \(CBV\) due to the vasodilation and vasoconstriction. In Figure 2a, one can clearly see the \([HHb]\) and \([HbO_{2}]\) responses to all simulated changes in the model input variables (\([tHb]\) and \(CBF\)) as they could be measured by NIRS. In particular, any increases in \(CBF\) cause increases in \([HbO_{2}]\) and decreases in \([HHb]\). Conversely, any decreases in \(CBF\) cause decreases in \([HbO_{2}]\) and increases in \([HHb]\). Increases and decreases in \([tHb]\) mainly correspond to the increases and decreases in \([HbO_{2}]\) as the vasodilation magnitude in arterioles (carrying highly oxygenated blood) greatly exceeds vasodilation in venules and capillaries. Other model variables shown in Figure 2 also show moderate responses to the periodic \(CBF\) modulation and most significant changes during the cardiac arrest, CPR, and ROSC. In particular, during CA, the tissue oxygen saturation \(S_{t}O_{2}=[HbO_{2}]/([HbO_{2}]+[HHb])\) drops by about 20% (see Figure 2c). Although this drop is very significant, it is notable that during CA the capillary oxygen saturation \(\langle S^{(c)}\rangle(t)\) (blue curve in Figure 2d) drops to 0%. Unlike \(\langle S^{(c)}\rangle(t)\), the venule oxygen saturation \(\langle S^{(v)}\rangle(t)\) computed using the CHS model (red curve in Figure 2d) drops only slightly during CA and more significantly during CPR, when the deoxygenated blood arrives to the venules from the capillaries due to the restoration of \(CBF\). Since \(CMRO_{2}\) is proportional to \(CBF\) according to [PERSON]'s principle (Equation (9)), in Figure 2e, \(CMRO_{2}\) also drops to zero during CA. From Figure 2d, one can also see that Equation (10) gives higher values of the venule saturation \(\langle S^{(v)}\rangle(t)\) (green-dotted curve in Figure 2d) compared with the values resulting from the CHS model (red curve). This overestimation of the venule saturation results in the underestimation of \(CMRO_{2}\) (green-dotted curve in Figure 2e) when using [PERSON]'s principle, as in Equation (9).