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

Autor: Alegre-Cortés J; Bioengineering Institute, Miguel Hernández University (UMH), Alicante, Spain., Soto-Sánchez C; Bioengineering Institute, Miguel Hernández University (UMH), Alicante, Spain; Biomedical Research Networking center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Zaragoza, Spain., Pizá ÁG; Laboratorio de Medios e Interfases (LAMEIN), Departamento de Bioingeniería, Facultad de Ciencias Exactas y Tecnología, Universidad Nacional de Tucumán, Tucumán, Argentina; Instituto Superior de Investigaciones Biológicas (INSIBIO), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Tucumán, Argentina., Albarracín AL; Laboratorio de Medios e Interfases (LAMEIN), Departamento de Bioingeniería, Facultad de Ciencias Exactas y Tecnología, Universidad Nacional de Tucumán, Tucumán, Argentina; Instituto Superior de Investigaciones Biológicas (INSIBIO), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Tucumán, Argentina., Farfán FD; Laboratorio de Medios e Interfases (LAMEIN), Departamento de Bioingeniería, Facultad de Ciencias Exactas y Tecnología, Universidad Nacional de Tucumán, Tucumán, Argentina; Instituto Superior de Investigaciones Biológicas (INSIBIO), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Tucumán, Argentina., Felice CJ; Laboratorio de Medios e Interfases (LAMEIN), Departamento de Bioingeniería, Facultad de Ciencias Exactas y Tecnología, Universidad Nacional de Tucumán, Tucumán, Argentina; Instituto Superior de Investigaciones Biológicas (INSIBIO), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Tucumán, Argentina., Fernández E; Bioengineering Institute, Miguel Hernández University (UMH), Alicante, Spain; Biomedical Research Networking center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Zaragoza, Spain. Electronic address: e.fernandez@umh.es.
Jazyk: angličtina
Zdroj: Journal of neuroscience methods [J Neurosci Methods] 2016 Jul 15; Vol. 267, pp. 35-44. Date of Electronic Publication: 2016 Apr 01.
DOI: 10.1016/j.jneumeth.2016.03.018
Abstrakt: 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.
(Copyright © 2016 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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