Comparative study of extracellular recording methods for analysis of afferent sensory information: Empirical modeling, data analysis and interpretation.

BACKGROUND: Physiological studies of sensorial systems often require the acquisition and processing of data extracted from their multiple components to evaluate how the neural information changes in relation to the environment changes. In this work, a comparative study about methodological aspects of two electrophysiological approaches is described. NEW METHOD: Extracellular recordings from deep vibrissal nerves were obtained by using a customized microelectrode Utah array during passive mechanical stimulation of rat´s whiskers. These recordings were compared with those obtained with bipolar electrodes. We also propose here a simplified empirical model of the electrophysiological activity obtained from a bundle of myelinated nerve fibers. RESULTS: The peripheral activity of the vibrissal system was characterized through the temporal and spectral features obtained with both recording methods. The empirical model not only allows the correlation between anatomical structures and functional features, but also allows to predict changes in the CAPs morphology when the arrangement and the geometry of the electrodes changes. COMPARISON WITH EXISTING METHOD(S): This study compares two extracellular recording methods based on analysis techniques, empirical modeling and data processing of vibrissal sensory information. CONCLUSIONS: This comparative study reveals a close relationship between the electrophysiological techniques and the processing methods necessary to extract sensory information. This relationship is the result of maximizing the extraction of information from recordings of sensory activity.

Time-frequency analysis of neuronal populations with instantaneous resolution based on noise-assisted multivariate empirical mode decomposition.

BACKGROUND: Linear analysis has classically provided powerful tools for understanding the behavior of neural populations, but the neuron responses to real-world stimulation are nonlinear under some conditions, and many neuronal components demonstrate strong nonlinear behavior. In spite of this, temporal and frequency dynamics of neural populations to sensory stimulation have been usually analyzed with linear approaches. NEW METHOD: In this paper, we propose the use of Noise-Assisted Multivariate Empirical Mode Decomposition (NA-MEMD), a data-driven template-free algorithm, plus the Hilbert transform as a suitable tool for analyzing population oscillatory dynamics in a multi-dimensional space with instantaneous frequency (IF) resolution. RESULTS: The proposed approach was able to extract oscillatory information of neurophysiological data of deep vibrissal nerve and visual cortex multiunit recordings that were not evidenced using linear approaches with fixed bases such as the Fourier analysis. COMPARISON WITH EXISTING METHODS: Texture discrimination analysis performance was increased when Noise-Assisted Multivariate Empirical Mode plus Hilbert transform was implemented, compared to linear techniques. Cortical oscillatory population activity was analyzed with precise time-frequency resolution. Similarly, NA-MEMD provided increased time-frequency resolution of cortical oscillatory population activity. CONCLUSIONS: Noise-Assisted Multivariate Empirical Mode Decomposition plus Hilbert transform is an improved method to analyze neuronal population oscillatory dynamics overcoming linear and stationary assumptions of classical methods.