Independent complexity patterns in single neuron activity induced by static magnetic field

Authors:
S. Spasić;Lj. Nikolić;D. Mutavdić;J. Šaponjić
Affiliations:
University of Belgrade, Institute for Multidisciplinary Research, Department for Life Sciences, Kneza Višeslava 1, 11000 Belgrade, Serbia;University of Belgrade, Institute for Biological Research - Siniša Stanković, Department of Neurophysiology, Belgrade, Serbia;University of Belgrade, Institute for Multidisciplinary Research, Department for Life Sciences, Kneza Višeslava 1, 11000 Belgrade, Serbia;University of Belgrade, Institute for Biological Research - Siniša Stanković, Department of Neurobiology, Belgrade, Serbia
Venue:
Computer Methods and Programs in Biomedicine
Year:
2011

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Abstract

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 2.7mT. 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.