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

Autores
Alegre Cortes, Javier; Soto Sanchez, Cristina; Pizá, Alvaro Gabriel; Albarracin, Ana Lia; Farfan, Fernando Daniel; Felice, Carmelo Jose; Fernandez, Eduardo
Año de publicación
2016
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
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.
Fil: Alegre Cortes, Javier. Universidad de Miguel Hernández; España
Fil: Soto Sanchez, Cristina. Universidad de Miguel Hernández; España
Fil: Pizá, Alvaro Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología. Departamento de Bioingeniería. Laboratorio de Medios e Interfases; Argentina
Fil: Albarracin, Ana Lia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología. Departamento de Bioingeniería. Laboratorio de Medios e Interfases; Argentina
Fil: Farfan, Fernando Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología. Departamento de Bioingeniería. Laboratorio de Medios e Interfases; Argentina
Fil: Felice, Carmelo Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología. Departamento de Bioingeniería. Laboratorio de Medios e Interfases; Argentina
Fil: Fernandez, Eduardo. Universidad de Miguel Hernández; España
Materia
NA-MEMD
NEURONAL POPULATION
NON-STATIONARY ANALYSIS
NONLINEAR ANALYSIS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/51657
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/51657
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Time-frequency analysis of neuronal populations with instantaneous resolution based on noise-assisted multivariate empirical mode decompositionAlegre Cortes, JavierSoto Sanchez, CristinaPizá, Alvaro GabrielAlbarracin, Ana LiaFarfan, Fernando DanielFelice, Carmelo JoseFernandez, EduardoNA-MEMDNEURONAL POPULATIONNON-STATIONARY ANALYSISNONLINEAR ANALYSIShttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1Background: 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.Fil: Alegre Cortes, Javier. Universidad de Miguel Hernández; EspañaFil: Soto Sanchez, Cristina. Universidad de Miguel Hernández; EspañaFil: Pizá, Alvaro Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología. Departamento de Bioingeniería. Laboratorio de Medios e Interfases; ArgentinaFil: Albarracin, Ana Lia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología. Departamento de Bioingeniería. Laboratorio de Medios e Interfases; ArgentinaFil: Farfan, Fernando Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología. Departamento de Bioingeniería. Laboratorio de Medios e Interfases; ArgentinaFil: Felice, Carmelo Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología. Departamento de Bioingeniería. Laboratorio de Medios e Interfases; ArgentinaFil: Fernandez, Eduardo. Universidad de Miguel Hernández; EspañaElsevier Science2016-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/51657Alegre Cortes, Javier; Soto Sanchez, Cristina; Pizá, Alvaro Gabriel; Albarracin, Ana Lia; Farfan, Fernando Daniel; et al.; Time-frequency analysis of neuronal populations with instantaneous resolution based on noise-assisted multivariate empirical mode decomposition; Elsevier Science; Journal of Neuroscience Methods; 267; 7-2016; 35-440165-0270CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.jneumeth.2016.03.018info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0165027016300243info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:10:45Zoai:ri.conicet.gov.ar:11336/51657instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 11:10:45.873CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Time-frequency analysis of neuronal populations with instantaneous resolution based on noise-assisted multivariate empirical mode decomposition
title Time-frequency analysis of neuronal populations with instantaneous resolution based on noise-assisted multivariate empirical mode decomposition
spellingShingle Time-frequency analysis of neuronal populations with instantaneous resolution based on noise-assisted multivariate empirical mode decomposition
Alegre Cortes, Javier
NA-MEMD
NEURONAL POPULATION
NON-STATIONARY ANALYSIS
NONLINEAR ANALYSIS
title_short Time-frequency analysis of neuronal populations with instantaneous resolution based on noise-assisted multivariate empirical mode decomposition
title_full Time-frequency analysis of neuronal populations with instantaneous resolution based on noise-assisted multivariate empirical mode decomposition
title_fullStr Time-frequency analysis of neuronal populations with instantaneous resolution based on noise-assisted multivariate empirical mode decomposition
title_full_unstemmed Time-frequency analysis of neuronal populations with instantaneous resolution based on noise-assisted multivariate empirical mode decomposition
title_sort Time-frequency analysis of neuronal populations with instantaneous resolution based on noise-assisted multivariate empirical mode decomposition
dc.creator.none.fl_str_mv Alegre Cortes, Javier
Soto Sanchez, Cristina
Pizá, Alvaro Gabriel
Albarracin, Ana Lia
Farfan, Fernando Daniel
Felice, Carmelo Jose
Fernandez, Eduardo
author Alegre Cortes, Javier
author_facet Alegre Cortes, Javier
Soto Sanchez, Cristina
Pizá, Alvaro Gabriel
Albarracin, Ana Lia
Farfan, Fernando Daniel
Felice, Carmelo Jose
Fernandez, Eduardo
author_role author
author2 Soto Sanchez, Cristina
Pizá, Alvaro Gabriel
Albarracin, Ana Lia
Farfan, Fernando Daniel
Felice, Carmelo Jose
Fernandez, Eduardo
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv NA-MEMD
NEURONAL POPULATION
NON-STATIONARY ANALYSIS
NONLINEAR ANALYSIS
topic NA-MEMD
NEURONAL POPULATION
NON-STATIONARY ANALYSIS
NONLINEAR ANALYSIS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv 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.
Fil: Alegre Cortes, Javier. Universidad de Miguel Hernández; España
Fil: Soto Sanchez, Cristina. Universidad de Miguel Hernández; España
Fil: Pizá, Alvaro Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología. Departamento de Bioingeniería. Laboratorio de Medios e Interfases; Argentina
Fil: Albarracin, Ana Lia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología. Departamento de Bioingeniería. Laboratorio de Medios e Interfases; Argentina
Fil: Farfan, Fernando Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología. Departamento de Bioingeniería. Laboratorio de Medios e Interfases; Argentina
Fil: Felice, Carmelo Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología. Departamento de Bioingeniería. Laboratorio de Medios e Interfases; Argentina
Fil: Fernandez, Eduardo. Universidad de Miguel Hernández; España
description 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.
publishDate 2016
dc.date.none.fl_str_mv 2016-07
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/51657
Alegre Cortes, Javier; Soto Sanchez, Cristina; Pizá, Alvaro Gabriel; Albarracin, Ana Lia; Farfan, Fernando Daniel; et al.; Time-frequency analysis of neuronal populations with instantaneous resolution based on noise-assisted multivariate empirical mode decomposition; Elsevier Science; Journal of Neuroscience Methods; 267; 7-2016; 35-44
0165-0270
CONICET Digital
CONICET
url http://hdl.handle.net/11336/51657
identifier_str_mv Alegre Cortes, Javier; Soto Sanchez, Cristina; Pizá, Alvaro Gabriel; Albarracin, Ana Lia; Farfan, Fernando Daniel; et al.; Time-frequency analysis of neuronal populations with instantaneous resolution based on noise-assisted multivariate empirical mode decomposition; Elsevier Science; Journal of Neuroscience Methods; 267; 7-2016; 35-44
0165-0270
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jneumeth.2016.03.018
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0165027016300243
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.982451

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