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脑血管系统病理生理学的分形分析。

Fractal Analysis of the Cerebrovascular System Pathophysiology.

机构信息

Department of Anesthesiology and Intensive Care Medicine, University Hospital Bonn, Bonn, Germany.

出版信息

Adv Neurobiol. 2024;36:385-396. doi: 10.1007/978-3-031-47606-8_19.

Abstract

The cerebrovascular system is characterized by parameters such as arterial blood pressure (ABP), cerebral perfusion pressure (CPP), and cerebral blood flow velocity (CBFV). These are regulated by interconnected feedback loops resulting in a fluctuating and complex time course. They exhibit fractal characteristics such as (statistical) self-similarity and scale invariance which could be quantified by fractal measures. These include the coefficient of variation, the Hurst coefficient H, or the spectral exponent α in the time domain, as well as the spectral index ß in the frequency domain. Prior to quantification, the time series has to be classified as either stationary or nonstationary, which determines the appropriate fractal analysis and measure for a given signal class. CBFV was characterized as a nonstationary (fractal Brownian motion) signal with spectral index ß between 2.0 and 2.3. In the high-frequency range (>0.15 Hz), CBFV variability is mainly determined by the periodic ABP variability induced by heartbeat and respiration. However, most of the spectral power of CBFV is contained in the low-frequency range (<0.15 Hz), where cerebral autoregulation acts as a low-pass filter and where the fractal properties are found. Cerebral vasospasm, which is a complication of subarachnoid hemorrhage (SAH), is associated with an increase in ß denoting a less complex time course. A reduced fractal dimension of the retinal microvasculature has been observed in neurodegenerative disease and in stroke. According to the decomplexification theory of illness, such a diminished complexity could be explained by a restriction or even dropout of feedback loops caused by disease.

摘要

脑血管系统的特点是动脉血压(ABP)、脑灌注压(CPP)和脑血流速度(CBFV)等参数。这些参数通过相互关联的反馈环进行调节,导致其时间过程呈现波动和复杂的特征。它们具有分形特征,如(统计)自相似性和标度不变性,可以通过分形测量来量化。这些包括变异系数、Hurst 系数 H 或时间域中的谱指数 α,以及频域中的谱指数 β。在进行量化之前,必须将时间序列分类为平稳或非平稳,这决定了给定信号类的适当分形分析和测量。CBFV 被描述为非平稳(分形布朗运动)信号,其谱指数 β 介于 2.0 和 2.3 之间。在高频范围(>0.15 Hz)中,CBFV 的可变性主要由心跳和呼吸引起的周期性 ABP 可变性决定。然而,CBFV 的大部分谱功率都包含在低频范围(<0.15 Hz)中,在该范围内,脑自动调节充当低通滤波器,并且可以找到分形特性。蛛网膜下腔出血(SAH)的并发症脑血管痉挛与β值增加相关,这表示时间过程的复杂性降低。在神经退行性疾病和中风中,已经观察到视网膜微血管的分形维数降低。根据疾病的去复杂化理论,这种复杂性的降低可以通过疾病引起的反馈环的限制甚至中断来解释。

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