Berger C S, Malpas S C
Department of Electrical Engineering and Computer Systems, Monash University, Clayton, Baker Medical Research Institute, Prahran, Victoria, Australia.
J Exp Biol. 1998 Dec;201(Pt 24):3425-30. doi: 10.1242/jeb.201.24.3425.
A linear autoregressive/moving-average model was developed to describe the dynamic relationship between mean arterial pressure (MAP), renal sympathetic nerve activity (SNA) and renal blood flow (RBF) in conscious rabbits. The RBF and SNA to the same kidney were measured under resting conditions in a group of eight rabbits. Spectral analysis of the data sampled at 0.4 Hz showed that the low-pass bandwidth of the signal power for RBF was approximately 0. 05 Hz. An autoregressive/moving-average model with an exogenous input (ARMAX) was then derived (using the iterative Gauss-Newton algorithm provided by the MATLAB identification Toolbox), with MAP and SNA as inputs and RBF as output, to model the low-frequency fluctuations. The model step responses of RBF to changes in SNA and arterial pressure indicated an overdamped response with a settling time that was usually less than 2 s. Calculated residuals from the model indicated that 79 5 % (mean s.d., averaged over eight independent experiments) of the variation in RBF could be accounted for by the variations in arterial pressure and SNA. Two additional single-input models for each of the inputs were similarly obtained and showed conclusively that changes in RBF, in the conscious resting rabbit, are a function of both SNA and MAP and that the SNA signal has the predominant effect. These results indicate a strong reliance on SNA for the dynamic regulation of RBF. Such information is likely to be important in understanding the diminished renal function that occurs in a variety of disease conditions in which overactivity of the sympathetic nervous system occurs.
建立了一个线性自回归/移动平均模型，以描述清醒兔平均动脉压（MAP）、肾交感神经活动（SNA）和肾血流量（RBF）之间的动态关系。在一组8只兔的静息条件下，测量了同一肾脏的RBF和SNA。对以0.4Hz采样的数据进行频谱分析表明，RBF信号功率的低通带宽约为0.05Hz。然后推导了一个具有外生输入的自回归/移动平均模型（ARMAX）（使用MATLAB识别工具箱提供的迭代高斯-牛顿算法），以MAP和SNA作为输入，RBF作为输出，对低频波动进行建模。RBF对SNA和动脉压变化的模型阶跃响应表明是过阻尼响应，建立时间通常小于2秒。模型计算的残差表明，RBF变化的79.5%（平均值±标准差，8次独立实验的平均值）可由动脉压和SNA的变化来解释。同样获得了每个输入的另外两个单输入模型，结果确凿地表明，清醒静息兔的RBF变化是SNA和MAP两者的函数，且SNA信号具有主要作用。这些结果表明，RBF的动态调节强烈依赖于SNA。此类信息对于理解在交感神经系统活动过度的各种疾病状态下发生的肾功能减退可能很重要。
