IEEE Trans Biomed Eng. 2021 Nov;68(11):3347-3355. doi: 10.1109/TBME.2021.3070900. Epub 2021 Oct 21.
To extend closed-loop modeling of the heart-rate reflex (HRR) by including the dynamic effects of concurrent changes in blood CO2 tension. This extended dynamic model can be used to generate physio-markers of "baroreflex gain" (BRG) and "chemoreflex gain" (CRG) that allow quantitative assessment of the possible impact of pathologies upon HRR. Mild Cognitive Impairment (MCI) is used as an example.
The proposed data-based closed-loop modeling methodology estimates the forward and reverse dynamic components of the model via Laguerre kernel expansions of two open-loop models using spontaneous time-series data collected in 45 MCI patients and 15 controls. The BRG and CRG physio-markers are subsequently computed for each subject via simulation of the obtained closed-loop model for unit-step change of arterial pressure or blood CO2 tension, respectively.
Both open-loop and closed-loop HRR modeling revealed that MCI patients exhibit significantly smaller CRG relative to controls (p<0.001), but not significantly different BRG. Furthermore, the closed-loop model captured the dynamic effect of sympathetic activity as resonant peak around 0.1 Hz (Mayer wave) in the chemoreflex and baroreflex transfer functions (not captured via open-loop modeling). This may prove valuable in advancing our understanding of how sympathetic activity impacts HRR in various pathologies.
The extended HRR model, incorporating the dynamic effects of concurrent changes of blood CO2 tension, revealed significantly reduced chemoreflex gain (but not baroreflex gain) in MCI patients. Furthermore, the closed-loop model captured the sympathetic influence around 0.1 Hz.
Multivariate closed-loop dynamic modeling is valuable for understanding physiological autoregulation.
通过纳入血液 CO2 张力同时变化的动态效应,扩展心率反射(HRR)的闭环建模。这个扩展的动态模型可以用来生成“压力反射增益”(BRG)和“化学反射增益”(CRG)的生理标志物,从而对 HRR 可能受到病理影响的程度进行定量评估。轻度认知障碍(MCI)就是一个例子。
所提出的数据驱动闭环建模方法通过使用自发时间序列数据从两个开环模型中使用拉盖尔核扩展来估计模型的前向和反向动态分量,这些数据是在 45 名 MCI 患者和 15 名对照中收集的。随后,通过对获得的闭环模型进行单位阶跃动脉压或血液 CO2 张力变化的模拟,为每个受试者计算 BRG 和 CRG 生理标志物。
开环和闭环 HRR 建模都表明,MCI 患者的 CRG 明显小于对照组(p<0.001),但 BRG 没有明显差异。此外,闭环模型捕捉到了化学反射和压力反射传递函数中交感神经活动的动态效应,即 0.1 Hz 左右的共振峰(梅耶波)(通过开环建模无法捕捉到)。这可能有助于我们深入了解交感神经活动如何在各种病理情况下影响 HRR。
纳入血液 CO2 张力同时变化的动态效应的扩展 HRR 模型显示,MCI 患者的化学反射增益明显降低(但压力反射增益没有降低)。此外,闭环模型捕捉到了 0.1 Hz 左右的交感神经影响。
多变量闭环动态建模对于理解生理自动调节非常有价值。