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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >Independent complexity patterns in single neuron activity induced by static magnetic field.
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Independent complexity patterns in single neuron activity induced by static magnetic field.

机译：静态磁场在单个神经元活动中的独立复杂性模式。

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摘要

We applied a combination of fractal analysis and Independent Component Analysis (ICA) method to detect the sources of fractal complexity in snail Br neuron activity induced by static magnetic field of 2.7 mT. The fractal complexity of Br neuron activity was analyzed before (Control), during (MF), and after (AMF) exposure to the static magnetic field in six experimental animals. We estimated the fractal dimension (FD) of electrophysiological signals using Higuchi's algorithm, and empirical FD distributions. By using the Principal Component Analysis (PCA) and FastICA algorithm we determined the number of components, and defined the statistically independent components (ICs) in the fractal complexity of signal waveforms. We have isolated two independent components of the empirical FD distributions for each of three groups of data by using FastICA algorithm. ICs represent the sources of fractal waveforms complexity of Br neuron activity in particular experimental conditions. Our main results have shown that there could be two opposite intrinsic mechanisms in single snail Br neuron response to static magnetic field stimulation. We named identified ICs that correspond to those mechanisms - the component of plasticity and the component of elasticity. We have shown that combination of fractal analysis with ICA method could be very useful for the decomposition and identification of the sources of fractal complexity of bursting neuronal activity waveforms.
机译：我们应用分形分析和独立成分分析（ICA）方法的组合来检测2.7 mT静磁场引起的蜗牛Br神经元活动中分形复杂性的来源。在六只实验动物中，在暴露于静磁场之前（对照），期间（MF）和之后（AMF），分析了Br神经元活性的分形复杂性。我们使用Higuchi算法和经验FD分布估算了电生理信号的分形维数（FD）。通过使用主成分分析（PCA）和FastICA算法，我们确定了成分的数量，并定义了信号波形的分形复杂度中的统计独立成分（IC）。通过使用FastICA算法，我们为三组数据中的每组隔离了经验FD分布的两个独立成分。 IC代表特定实验条件下Br神经元活动的分形波形复杂性来源。我们的主要结果表明，单个蜗牛Br神经元对静态磁场刺激的响应可能有两个相反的内在机制。我们命名了与那些机制相对应的已识别IC，即可塑性的组成部分和弹性的组成部分。我们已经表明，分形分析与ICA方法的结合对于分解和识别爆发性神经元活动波形的分形复杂性来源可能非常有用。

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