Temporal fluctuations in regional red blood cell flux in the rat brain cortex is a fractal process.
Temporal fluctuations in regional red blood cell flux in the rat brain cortex is a fractal process.
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大鼠大脑皮层区域红细胞通量的时间波动是一个分形过程。
DOI：
10.1007/978-1-4615-5399-1_98
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发表时间：
1997
影响因子：
--
通讯作者：
Ikrényi,C
中科院分区：
文献类型：
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作者：
Eke,A;Hermán,P;Bassingthwaighte,JB;Raymond,GM;Balla,I;Ikrényi,C
自从 1929 年 Krogh 观察并认识到时间波动是微循环中血流的基本特征以来（Krogh，1929），时间波动就成为“血管运动”和“血流运动”概念的本质。这种流量波动在包括大脑在内的各种微循环床中很常见，并且可以通过激光多普勒流量计连续监测（Stern，1975；Nilsson 等，1980；Rosenblum 等，1987；Fasano 等，1988；Hudetz 等，1992；Morita-Tsuzuki 等，1992）。当受到挑战时，它们通常会表现出缓慢（6-12 个周期/分钟）的振荡模式，如果从信号中消除较高频率，则可以进行相对简单的表征（Hudetz 等人，1992 年；Morita-Tsuzuki 等人，1992 年）。然而，微循环血流的时间变化是多因素的，这些因素之间的相互作用可以体现在高分辨率激光多普勒血流计（LDF）记录的时间序列的复杂结构中。由于不存在明显的主导频率（图 3），它们过于复杂，无法通过传统的描述性统计、幅度和频率测量进行具体分析。因此，我们使用本质上真正整体的分形方法来获得对此类随机信号的统计洞察，并确定在不受挑战的控制条件下它们是否代表无组织行为或显示长期相关性（West和Goldberger，1987；Bassingthwaighte，1988；West和Shlesinger，1990；Weibel，1991；Bassingthwaighte等人，1994）。
Temporal fluctuations ever since it was observed and recognized as an elementary feature of blood flow in the microcirculation by Krogh in 1929 (Krogh, 1929) became the essence of concepts termed “vasomotion” and “flow motion”. Such fluctuations in flow are common in various microcirculatory beds including that of the brain and can be continuously monitored by laser-Doppler flowmetry (Stern, 1975; Nilsson et al., 1980; Rosenblum et al., 1987; Fasano et al., 1988; Hudetz et al., 1992; Morita-Tsuzuki et al., 1992). When challenged, they often show slow (6–12 cycles/minute) oscillatory pattern, which allow for a relatively simple characterization if the higher frequencies are eliminated from the signal (Hudetz et al., 1992; Morita-Tsuzuki et al., 1992). Temporal variation in microcirculatory flow is however multifactorial and the interactions among these factors can manifest in a complex structuring of the time series recorded by high-resolution laserDoppler flowmetry (LDF). With no apparent dominating frequency present (Fig. 3), they are much too complex to be analyzed in specific terms by conventional descriptive statistics, amplitude and frequency measures. Thus we have used fractal methods genuinely holistic in nature to gain statistical insight into random signals of this kind and to determine if under unchallenged control conditions they represent disorganized behavior or show long-range correlations (West and Goldberger, 1987; Bassingthwaighte, 1988; West and Shlesinger, 1990; Weibel, 1991; Bassingthwaighte et al., 1994).