Quantitative estimation of the nonstationary behavior of neural spontaneous activity

  • Authors:

  • João-Batista Destro-Filho;Carlos-Alberto Estombelo-Montesco;Luiz-Otavio Murta-Junior;Sergio Martinoia;Michela Chiappalone;Suelen Moreira-Marques;Amanda F. Neves

  • Affiliations:

  • Biomedical Engineering Laboratory, School of Electronic Engineering, Federal University of Uberlândia, Uberlândia, MG, Brazil;Physics Department, FFCLH, São Paulo University, Ribeirão Preto, Brazil;Physics Department, FFCLH, São Paulo University, Ribeirão Preto, Brazil;Neuroengineering & Bio-Nano Technology Group, Department of Biophysical and Electronic Engineering, University of Genova, Genova, Italy;Neuroengineering & Bio-Nano Technology Group, Department of Biophysical and Electronic Engineering, University of Genova, Genova, Italy;Biomedical Engineering Laboratory, School of Electronic Engineering, Federal University of Uberlândia, Uberlândia, MG, Brazil;Biomedical Engineering Laboratory, School of Electronic Engineering, Federal University of Uberlândia, Uberlândia, MG, Brazil

  • Venue:
  • Computational Intelligence and Neuroscience - Special issue on signal processing for neural spike trains
  • Year:
  • 2010

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Abstract

The "stationarity time" (ST) of neuronal spontaneous activity signals of rat embryonic cortical cells, measured by means of a planar Multielectrode Array (MEA), was estimated based on the "Detrended Fluctuation Analysis" (DFA). The ST is defined as the mean time interval during which the signal under analysis keeps its statistical characteristics constant. An upgrade on the DFA method is proposed, leading to a more accurate procedure. Strong statistical correlation between the ST, estimated from the Absolute Amplitude of Neural Spontaneous Activity (AANSA) signals and the Mean Interburst Interval (MIB), calculated by classical spike sorting methods applied to the interspike interval time series, was obtained. In consequence, the MIB may be estimated by means of the ST, which further includes relevant biological information arising from basal activity. The results point out that the average ST of MEA signals lies between 2-3 seconds. Furthermore, it was shown that a neural culture presents signals that lead to different statistical behaviors, depending on the relative geometric position of each electrode and the cells. Such behaviors may disclose physiological phenomena, which are possibly associated with different adaptation/facilitation mechanisms.