Cortical current source estimation from electroencephalography in combination with near-infrared spectroscopy as a hierarchical prior

Takatsugu Aihara, Yusuke Takeda, Kotaro Takeda, Wataru Yasuda, Takanori Sato, Yohei Otaka, Takashi Hanakawa, Manabu Honda, Meigen Liu, Mitsuo Kawato, Masa aki Sato, Rieko Osu

Research output: Contribution to journalArticle

26 Citations (Scopus)

Abstract

Previous simulation and experimental studies have demonstrated that the application of Variational Bayesian Multimodal EncephaloGraphy (VBMEG) to magnetoencephalography (MEG) data can be used to estimate cortical currents with high spatio-temporal resolution, by incorporating functional magnetic resonance imaging (fMRI) activity as a hierarchical prior. However, the use of combined MEG and fMRI is restricted by the high costs involved, a lack of portability and high sensitivity to body-motion artifacts. One possible solution for overcoming these limitations is to use a combination of electroencephalography (EEG) and near-infrared spectroscopy (NIRS). This study therefore aimed to extend the possible applications of VBMEG to include EEG data with NIRS activity as a hierarchical prior. Using computer simulations and real experimental data, we evaluated the performance of VBMEG applied to EEG data under different conditions, including different numbers of EEG sensors and different prior information. The results suggest that VBMEG with NIRS prior performs well, even with as few as 19 EEG sensors. These findings indicate the potential value of clinically applying VBMEG using a combination of EEG and NIRS.

Original languageEnglish
Pages (from-to)4006-4021
Number of pages16
JournalNeuroImage
Volume59
Issue number4
DOIs
Publication statusPublished - 2012 Feb 15
Externally publishedYes

Fingerprint

Near-Infrared Spectroscopy
Electroencephalography
Magnetoencephalography
Magnetic Resonance Imaging
Computer Simulation
Artifacts
Costs and Cost Analysis

Keywords

  • Electroencephalography (EEG)
  • Near-infrared spectroscopy (NIRS)
  • Variational Bayesian Multimodal EncephaloGraphy (VBMEG)

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

Cite this

Cortical current source estimation from electroencephalography in combination with near-infrared spectroscopy as a hierarchical prior. / Aihara, Takatsugu; Takeda, Yusuke; Takeda, Kotaro; Yasuda, Wataru; Sato, Takanori; Otaka, Yohei; Hanakawa, Takashi; Honda, Manabu; Liu, Meigen; Kawato, Mitsuo; Sato, Masa aki; Osu, Rieko.

In: NeuroImage, Vol. 59, No. 4, 15.02.2012, p. 4006-4021.

Research output: Contribution to journalArticle

Aihara, T, Takeda, Y, Takeda, K, Yasuda, W, Sato, T, Otaka, Y, Hanakawa, T, Honda, M, Liu, M, Kawato, M, Sato, MA & Osu, R 2012, 'Cortical current source estimation from electroencephalography in combination with near-infrared spectroscopy as a hierarchical prior', NeuroImage, vol. 59, no. 4, pp. 4006-4021. https://doi.org/10.1016/j.neuroimage.2011.09.087
Aihara, Takatsugu ; Takeda, Yusuke ; Takeda, Kotaro ; Yasuda, Wataru ; Sato, Takanori ; Otaka, Yohei ; Hanakawa, Takashi ; Honda, Manabu ; Liu, Meigen ; Kawato, Mitsuo ; Sato, Masa aki ; Osu, Rieko. / Cortical current source estimation from electroencephalography in combination with near-infrared spectroscopy as a hierarchical prior. In: NeuroImage. 2012 ; Vol. 59, No. 4. pp. 4006-4021.
@article{d13168a99eba474f99eb7022c12eca7f,
title = "Cortical current source estimation from electroencephalography in combination with near-infrared spectroscopy as a hierarchical prior",
abstract = "Previous simulation and experimental studies have demonstrated that the application of Variational Bayesian Multimodal EncephaloGraphy (VBMEG) to magnetoencephalography (MEG) data can be used to estimate cortical currents with high spatio-temporal resolution, by incorporating functional magnetic resonance imaging (fMRI) activity as a hierarchical prior. However, the use of combined MEG and fMRI is restricted by the high costs involved, a lack of portability and high sensitivity to body-motion artifacts. One possible solution for overcoming these limitations is to use a combination of electroencephalography (EEG) and near-infrared spectroscopy (NIRS). This study therefore aimed to extend the possible applications of VBMEG to include EEG data with NIRS activity as a hierarchical prior. Using computer simulations and real experimental data, we evaluated the performance of VBMEG applied to EEG data under different conditions, including different numbers of EEG sensors and different prior information. The results suggest that VBMEG with NIRS prior performs well, even with as few as 19 EEG sensors. These findings indicate the potential value of clinically applying VBMEG using a combination of EEG and NIRS.",
keywords = "Electroencephalography (EEG), Near-infrared spectroscopy (NIRS), Variational Bayesian Multimodal EncephaloGraphy (VBMEG)",
author = "Takatsugu Aihara and Yusuke Takeda and Kotaro Takeda and Wataru Yasuda and Takanori Sato and Yohei Otaka and Takashi Hanakawa and Manabu Honda and Meigen Liu and Mitsuo Kawato and Sato, {Masa aki} and Rieko Osu",
year = "2012",
month = "2",
day = "15",
doi = "10.1016/j.neuroimage.2011.09.087",
language = "English",
volume = "59",
pages = "4006--4021",
journal = "NeuroImage",
issn = "1053-8119",
publisher = "Academic Press Inc.",
number = "4",

}

TY - JOUR

T1 - Cortical current source estimation from electroencephalography in combination with near-infrared spectroscopy as a hierarchical prior

AU - Aihara, Takatsugu

AU - Takeda, Yusuke

AU - Takeda, Kotaro

AU - Yasuda, Wataru

AU - Sato, Takanori

AU - Otaka, Yohei

AU - Hanakawa, Takashi

AU - Honda, Manabu

AU - Liu, Meigen

AU - Kawato, Mitsuo

AU - Sato, Masa aki

AU - Osu, Rieko

PY - 2012/2/15

Y1 - 2012/2/15

N2 - Previous simulation and experimental studies have demonstrated that the application of Variational Bayesian Multimodal EncephaloGraphy (VBMEG) to magnetoencephalography (MEG) data can be used to estimate cortical currents with high spatio-temporal resolution, by incorporating functional magnetic resonance imaging (fMRI) activity as a hierarchical prior. However, the use of combined MEG and fMRI is restricted by the high costs involved, a lack of portability and high sensitivity to body-motion artifacts. One possible solution for overcoming these limitations is to use a combination of electroencephalography (EEG) and near-infrared spectroscopy (NIRS). This study therefore aimed to extend the possible applications of VBMEG to include EEG data with NIRS activity as a hierarchical prior. Using computer simulations and real experimental data, we evaluated the performance of VBMEG applied to EEG data under different conditions, including different numbers of EEG sensors and different prior information. The results suggest that VBMEG with NIRS prior performs well, even with as few as 19 EEG sensors. These findings indicate the potential value of clinically applying VBMEG using a combination of EEG and NIRS.

AB - Previous simulation and experimental studies have demonstrated that the application of Variational Bayesian Multimodal EncephaloGraphy (VBMEG) to magnetoencephalography (MEG) data can be used to estimate cortical currents with high spatio-temporal resolution, by incorporating functional magnetic resonance imaging (fMRI) activity as a hierarchical prior. However, the use of combined MEG and fMRI is restricted by the high costs involved, a lack of portability and high sensitivity to body-motion artifacts. One possible solution for overcoming these limitations is to use a combination of electroencephalography (EEG) and near-infrared spectroscopy (NIRS). This study therefore aimed to extend the possible applications of VBMEG to include EEG data with NIRS activity as a hierarchical prior. Using computer simulations and real experimental data, we evaluated the performance of VBMEG applied to EEG data under different conditions, including different numbers of EEG sensors and different prior information. The results suggest that VBMEG with NIRS prior performs well, even with as few as 19 EEG sensors. These findings indicate the potential value of clinically applying VBMEG using a combination of EEG and NIRS.

KW - Electroencephalography (EEG)

KW - Near-infrared spectroscopy (NIRS)

KW - Variational Bayesian Multimodal EncephaloGraphy (VBMEG)

UR - http://www.scopus.com/inward/record.url?scp=84857030723&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84857030723&partnerID=8YFLogxK

U2 - 10.1016/j.neuroimage.2011.09.087

DO - 10.1016/j.neuroimage.2011.09.087

M3 - Article

C2 - 22036684

AN - SCOPUS:84857030723

VL - 59

SP - 4006

EP - 4021

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

IS - 4

ER -