Literature DB >> 20640882

Sensitivity of MEG and EEG to source orientation.

Seppo P Ahlfors1, Jooman Han, John W Belliveau, Matti S Hämäläinen.   

Abstract

An important difference between magnetoencephalography (MEG) and electroencephalography (EEG) is that MEG is insensitive to radially oriented sources. We quantified computationally the dependency of MEG and EEG on the source orientation using a forward model with realistic tissue boundaries. Similar to the simpler case of a spherical head model, in which MEG cannot see radial sources at all, for most cortical locations there was a source orientation to which MEG was insensitive. The median value for the ratio of the signal magnitude for the source orientation of the lowest and the highest sensitivity was 0.06 for MEG and 0.63 for EEG. The difference in the sensitivity to the source orientation is expected to contribute to systematic differences in the signal-to-noise ratio between MEG and EEG.

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Year:  2010        PMID: 20640882      PMCID: PMC2914866          DOI: 10.1007/s10548-010-0154-x

Source DB:  PubMed          Journal:  Brain Topogr        ISSN: 0896-0267            Impact factor:   3.020


  33 in total

1.  A sensor-weighted overlapping-sphere head model and exhaustive head model comparison for MEG.

Authors:  M X Huang; J C Mosher; R M Leahy
Journal:  Phys Med Biol       Date:  1999-02       Impact factor: 3.609

2.  EEG and MEG: forward solutions for inverse methods.

Authors:  J C Mosher; R M Leahy; P S Lewis
Journal:  IEEE Trans Biomed Eng       Date:  1999-03       Impact factor: 4.538

3.  Monte Carlo simulation studies of EEG and MEG localization accuracy.

Authors:  Arthur K Liu; Anders M Dale; John W Belliveau
Journal:  Hum Brain Mapp       Date:  2002-05       Impact factor: 5.038

4.  Role of soft bone, CSF and gray matter in EEG simulations.

Authors:  Ceon Ramon; P Schimpf; J Haueisen; M Holmes; A Ishimaru
Journal:  Brain Topogr       Date:  2004       Impact factor: 3.020

5.  Sources of auditory brainstem responses revisited: contribution by magnetoencephalography.

Authors:  Lauri Parkkonen; Nobuya Fujiki; Jyrki P Mäkelä
Journal:  Hum Brain Mapp       Date:  2009-06       Impact factor: 5.038

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Authors:  C Eulitz; H Eulitz; T Elbert
Journal:  Neurosci Lett       Date:  1997-05-23       Impact factor: 3.046

7.  Model studies of the magnetocardiogram.

Authors:  F Grynszpan; D B Geselowitz
Journal:  Biophys J       Date:  1973-09       Impact factor: 4.033

8.  Differences in MEG/EEG epileptic spike yields explained by regional differences in signal-to-noise ratios.

Authors:  Arent de Jongh; Jan Casper de Munck; Sónia I Gonçalves; Pauly Ossenblok
Journal:  J Clin Neurophysiol       Date:  2005-04       Impact factor: 2.177

9.  Regional differences in the sensitivity of MEG for interictal spikes in epilepsy.

Authors:  Geertjan Huiskamp; Zaloa Agirre-Arrizubieta; Frans Leijten
Journal:  Brain Topogr       Date:  2010-02-12       Impact factor: 3.020

10.  Magnetoencephalographic yield of interictal spikes in temporal lobe epilepsy. Comparison with scalp EEG recordings.

Authors:  Y Y Lin; Y H Shih; J C Hsieh; H Y Yu; C H Yiu; T T Wong; T C Yeh; S Y Kwan; L T Ho; D J Yen; Z A Wu; M S Chang
Journal:  Neuroimage       Date:  2003-07       Impact factor: 6.556

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  58 in total

1.  Source cancellation profiles of electroencephalography and magnetoencephalography.

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Journal:  Neuroimage       Date:  2011-09-18       Impact factor: 6.556

2.  Oscillatory dynamics of cortical functional connections in semantic prediction.

Authors:  Fahimeh Mamashli; Sheraz Khan; Jonas Obleser; Angela D Friederici; Burkhard Maess
Journal:  Hum Brain Mapp       Date:  2018-12-07       Impact factor: 5.038

3.  The temporal dynamics of structure and content in sentence comprehension: Evidence from fMRI-constrained MEG.

Authors:  William Matchin; Christian Brodbeck; Christopher Hammerly; Ellen Lau
Journal:  Hum Brain Mapp       Date:  2018-09-26       Impact factor: 5.038

4.  Spatio-temporal processing of words and nonwords: hemispheric laterality and acute alcohol intoxication.

Authors:  Ksenija Marinkovic; Burke Q Rosen; Brendan Cox; Donald J Hagler
Journal:  Brain Res       Date:  2014-02-22       Impact factor: 3.252

5.  Tones and numbers: a combined EEG-MEG study on the effects of musical expertise in magnitude comparisons of audiovisual stimuli.

Authors:  Evangelos Paraskevopoulos; Anja Kuchenbuch; Sibylle C Herholz; Nikolaos Foroglou; Panagiotis Bamidis; Christo Pantev
Journal:  Hum Brain Mapp       Date:  2014-06-11       Impact factor: 5.038

6.  Combining magnetoencephalography with magnetic resonance imaging enhances learning of surrogate-biomarkers.

Authors:  Denis A Engemann; Oleh Kozynets; David Sabbagh; Guillaume Lemaître; Gael Varoquaux; Franziskus Liem; Alexandre Gramfort
Journal:  Elife       Date:  2020-05-19       Impact factor: 8.140

7.  Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach.

Authors:  Abbas Sohrabpour; Shuai Ye; Gregory A Worrell; Wenbo Zhang; Bin He
Journal:  IEEE Trans Biomed Eng       Date:  2016-10-11       Impact factor: 4.538

8.  Sparse current source estimation for MEG using loose orientation constraints.

Authors:  Wei-Tang Chang; Seppo P Ahlfors; Fa-Hsuan Lin
Journal:  Hum Brain Mapp       Date:  2012-03-22       Impact factor: 5.038

9.  The phase of thalamic alpha activity modulates cortical gamma-band activity: evidence from resting-state MEG recordings.

Authors:  Frédéric Roux; Michael Wibral; Wolf Singer; Jaan Aru; Peter J Uhlhaas
Journal:  J Neurosci       Date:  2013-11-06       Impact factor: 6.167

10.  Reproducibility of EEG-MEG fusion source analysis of interictal spikes: Relevance in presurgical evaluation of epilepsy.

Authors:  Rasheda Arman Chowdhury; Giovanni Pellegrino; Ümit Aydin; Jean-Marc Lina; François Dubeau; Eliane Kobayashi; Christophe Grova
Journal:  Hum Brain Mapp       Date:  2017-11-21       Impact factor: 5.038

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