Detection of EEG-resting state independent networks by eLORETA-ICA method

Aoki, Yasunori; Ishii, Ryouhei; Pascual Marqui, Roberto D.; Canuet Delis, Leonides; Ikeda, Shunichiro; Hata, Masahiro; Imajo, Kaoru; Matsuzaki, Haruyasu; Musha, Toshimitsu; Asada, Takashi; Iwase, Masao y Takeda, M. (2015). Detection of EEG-resting state independent networks by eLORETA-ICA method. "Frontiers in Human Neuroscience", v. 9 (n. 31); pp. 1-12. ISSN 1662-5161. https://doi.org/10.3389/fnhum.2015.00031.

Descripción

Título: Detection of EEG-resting state independent networks by eLORETA-ICA method
Autor/es:
  • Aoki, Yasunori
  • Ishii, Ryouhei
  • Pascual Marqui, Roberto D.
  • Canuet Delis, Leonides
  • Ikeda, Shunichiro
  • Hata, Masahiro
  • Imajo, Kaoru
  • Matsuzaki, Haruyasu
  • Musha, Toshimitsu
  • Asada, Takashi
  • Iwase, Masao
  • Takeda, M.
Tipo de Documento: Artículo
Título de Revista/Publicación: Frontiers in Human Neuroscience
Fecha: 10 Febrero 2015
Volumen: 9
Materias:
Palabras Clave Informales: eLORETA-ICA, LORETA, resting state network, independent component analysis, ICA, EEG
Escuela: Centro de Tecnología Biomédica (CTB) (UPM)
Departamento: Tecnología Fotónica y Bioingeniería
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Recent functional magnetic resonance imaging (fMRI) studies have shown that functional networks can be extracted even from resting state data, the so called ?Resting State independent Networks? (RS-independent-Ns) by applying independent component analysis (ICA). However, compared to fMRI, electroencephalography (EEG) and magnetoencephalography (MEG) have much higher temporal resolution and provide a direct estimation of cortical activity. To date, MEG studies have applied ICA for separate frequency bands only, disregarding cross-frequency couplings. In this study, we aimed to detect EEG-RS-independent-Ns and their interactions in all frequency bands. We applied exact low resolution brain electromagnetic tomography-ICA (eLORETA-ICA) to resting-state EEG data in 80 healthy subjects using five frequency bands (delta, theta, alpha, beta and gamma band) and found five RS-independent-Ns in alpha, beta and gamma frequency bands. Next, taking into account previous neuroimaging findings, five RS-independent-Ns were identified: (1) the visual network in alpha frequency band, (2) dual-process of visual perception network, characterized by a negative correlation between the right ventral visual pathway (VVP) in alpha and beta frequency bands and left posterior dorsal visual pathway (DVP) in alpha frequency band, (3) self-referential processing network, characterized by a negative correlation between the medial prefrontal cortex (mPFC) in beta frequency band and right temporoparietal junction (TPJ) in alpha frequency band, (4) dual-process of memory perception network, functionally related to a negative correlation between the left VVP and the precuneus in alpha frequency band; and (5) sensorimotor network in beta and gamma frequency bands. We selected eLORETA-ICA which has many advantages over the other network visualization methods and overall findings indicate that eLORETA-ICA with EEG data can identify five RS-independent-Ns in their intrinsic frequency bands, and correct correlations within RS-independent-Ns.

Más información

ID de Registro: 41072
Identificador DC: http://oa.upm.es/41072/
Identificador OAI: oai:oa.upm.es:41072
Identificador DOI: 10.3389/fnhum.2015.00031
URL Oficial: http://journal.frontiersin.org/article/10.3389/fnhum.2015.00031/full
Depositado por: Memoria Investigacion
Depositado el: 22 Abr 2017 09:42
Ultima Modificación: 22 Abr 2017 09:42
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