Memorias de investigación
Artículos en revistas:
Detection of EEG-resting state independent networks by eLORETA-ICA method.
Año:2015
Áreas de investigación
  • Ciencias naturales y ciencias de la salud
Datos
Descripción
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.
Internacional
Si
JCR del ISI
Si
Título de la revista
Frontiers in Human Neuroscience
ISSN
1662-5161
Factor de impacto JCR
2,895
Información de impacto
Volumen
9
DOI
dx.doi.org/10.3389/fnhum.2015.00031
Número de revista
Desde la página
31
Hasta la página
43
Mes
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Participantes
  • Autor: Leonides Canuet Delis UPM
  • Autor: Yasunori Aoki
  • Autor: Ryouhei Ishii
  • Autor: Roberto D Pascual-Marqui
  • Autor: Shunichiro Ikeda
  • Autor: Masahiro Hata
  • Autor: Kaoru Imajo
  • Autor: Haruyasu Matsuzaki
  • Autor: Toshimitsu Musha
  • Autor: Takashi Asada
  • Autor: Masao Iwase
  • Autor: Masatoshi Takeda
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Tecnologías para Ciencias de la Salud
  • Centro o Instituto I+D+i: Centro de tecnología Biomédica CTB
  • Departamento: Tecnología Fotónica y Bioingeniería