EEG and MEG: forward solutions for inverse methods

Mosher, John C.; Leahy, Richard M.; Lewis, Paul

doi:10.1109/10.748978

Cited by 744 publications

(560 citation statements)

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“…Since each dipole has 6 degrees of freedom, an increase in the dipole number would substantially raise the computation time and error. This single-dipole approach has successfully been applied by former studies to approximate the accumulative activities of a large number of neurons (Mosher et al, 1999). Furthermore, since this is the first study on the issue of fatigue-induced cortical source adaptation, this single-dipole model, despite of its simplicity, serves the purpose of the study sufficiently.…”

Section: Discussionmentioning

confidence: 99%

“…The electrical conductivities of the brain and the scalp were 0.33 Ω -1 m -1 , and that of the skull was 0.0042 Ω -1 m -1 (Mosher et al, 1999). A single moving current dipole model was used to best represent the overall brain activation center (Mosher et al, 1999;Yao and Dewald, 2005). The difference between the actual and modeled EEG signals were minimized to obtain the dipole parameters (location, strength, and orientation) using the Nelder-Mead downhill simplex minimization algorithm (Nelder and Mead, 1965).…”

Section: Eeg Source Reconstructionmentioning

confidence: 97%

“…The radii of the brain, skull, and scalp spheres were 76.2, 83.4, and 89.7 mm, respectively. The electrical conductivities of the brain and the scalp were 0.33 Ω -1 m -1 , and that of the skull was 0.0042 Ω -1 m -1 (Mosher et al, 1999). A single moving current dipole model was used to best represent the overall brain activation center (Mosher et al, 1999;Yao and Dewald, 2005).…”

Section: Eeg Source Reconstructionmentioning

confidence: 99%

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Shifting of activation center in the brain during muscle fatigue: An explanation of minimal central fatigue?

et al. 2007

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Accumulating evidence suggests that the overall level of cortical activation controlling a voluntary motor task that leads to significant muscle fatigue does not decrease as much as the activation level of the motoneuron pool projecting to the muscle. One possible explanation for this "muscle fatigue>cortical fatigue" phenomenon is that the brain is an organ with built-in redundancies: it has multiple motor centers and parallel pathways, and the center of activation may shift from one location to another when neurons in the previous location become fatigued. This hypothesis was tested by estimating the changes of source locations of high-density (64 channels) scalp electroencephalographic (EEG) signals collected during both fatigue and non-fatigue motor tasks. A current dipole model was used to estimate the EEG sources. The fatigue motor task induced significant muscle fatigue, and the non-fatigue task did not. The EEG signal source that indicated the center of brain activation showed substantial location shifts during the fatigue motor task. The shifts could not be explained by variations of source locations caused by error estimated from the non-fatigue task EEG and simulated data. Compared to the non-fatigue condition, the weightedcenter of the source locations for all the participants shifted toward the right hemisphere (ipsilateral to the muscle activation), anterior, and inferior cortical regions under the fatigue condition. Fatigue did not alter dipole (source-signal) strength or the overall level of brain activation. The brain may avoid fatigue by shifting neuron populations that participate in a fatiguing motor task.

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Section: Discussionmentioning

confidence: 99%

Section: Eeg Source Reconstructionmentioning

confidence: 97%

Section: Eeg Source Reconstructionmentioning

confidence: 99%

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Shifting of activation center in the brain during muscle fatigue: An explanation of minimal central fatigue?

et al. 2007

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“…The (3N s × 1) vector location s forms the input argument for the nonlinear leadfield function, G(s), whose output is multiplied by the (3N s × 1) moment vector w to form the observed data. 1 The lead-field accounts for passive propagation of the electromagnetic field from the sources to the sensors (Mosher et al, 1999). Note that although the relationship between the data and primary current density is linear, it is non-linear in the dipole locations.…”

mentioning

confidence: 99%

“…For MEG, one can use a single sphere as a good approximation. The potential or magnetic field at the sensors requires an evaluation of an infinite series, which can be approximated using fast algorithms (Mosher et al, 1999;Zhang, 1995). For the ECD forward model, we used a Matlab (MathWorks) routine that is freely available as part of the FieldTrip package (http://www2.ru.nl/fcdonders/fieldtrip/, see also Oostenveld, 2003) under the GNU general public license.…”

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confidence: 99%

Variational Bayesian inversion of the equivalent current dipole model in EEG/MEG

et al. 2008

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In magneto-and electroencephalography (M/EEG), spatial modelling of sensor data is necessary to make inferences about underlying brain activity. Most source reconstruction techniques belong to one of two approaches: point source models, which explain the data with a small number of equivalent current dipoles and distributed source or imaging models, which use thousands of dipoles. Much methodological research has been devoted to developing sophisticated Bayesian source imaging inversion schemes, while dipoles have received less such attention. Dipole models have their advantages; they are often appropriate summaries of evoked responses or helpful first approximations. Here, we propose a variational Bayesian algorithm that enables the fast Bayesian inversion of dipole models. The approach allows for specification of priors on all the model parameters. The posterior distributions can be used to form Bayesian confidence intervals for interesting parameters, like dipole locations. Furthermore, competing models (e.g., models with different numbers of dipoles) can be compared using their evidence or marginal likelihood. Using synthetic data, we found the scheme provides accurate dipole localizations. We illustrate the advantage of our Bayesian scheme, using a multi-subject EEG auditory study, where we compare competing models for the generation of the N100 component. © 2007 Elsevier Inc. All rights reserved. Keywords: EEG; MEG; Equivalent current dipole; Variational Bayes IntroductionThe analysis of evoked responses using magneto-and electroencephalography (M/EEG) can proceed in several ways. If one is interested in inferring the locations of M/EEG generators within brain space, one has to solve the inverse spatial problem (Baillet et al., 2001). There are two main approaches to estimating sources from observed sensor data. The first assumes that sensor data can be explained by a small set of equivalent current dipoles. The inversion of this model amounts to a nonlinear optimization problem, because the forward model is nonlinear in dipole location (Mosher et al., 1992). Recently, the source reconstruction problem has been addressed by placing many dipoles in brain space, and using constraints on the solution to make it unique; for example (Baillet and Garnero, 1997;Mattout et al., 2006;Phillips et al., 2005). This approach is attractive, because it produces images of brain activity comparable to other imaging modalities and it eschews subjective constraints on the inversion. For imaging solutions, most constraints can be motivated by anatomical and physiological arguments, e.g., smoothness constraints and approximate location priors, based on regional activity in functional magnetic resonance imaging (fMRI). Traditional few-dipole solutions, however, are usually regarded as depending too much on user-specified modelling decisions; like the number of dipoles and their initial locations. Mathematically, it can be argued that the inversion of dipole models is a harder problem than inversion of distributed models, becaus...

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Biomagnetism

Cheyne¹,

Verba²

2006

Encyclopedia of Medical Devices and Instrumentation

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The science of biomagnetism refers to the measurement of magnetic fields produced by living organisms. These tiny magnetic fields are produced by naturally occurring electric currents resulting from muscle contraction, or signal transmission in the nervous system, or by the magnetization of biological tissue. This article discusses biomagnetic instruments and applications of these instruments as well as future direction of the field.

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EEG and MEG: forward solutions for inverse methods

Cited by 744 publications

References 52 publications

Shifting of activation center in the brain during muscle fatigue: An explanation of minimal central fatigue?

Shifting of activation center in the brain during muscle fatigue: An explanation of minimal central fatigue?

Variational Bayesian inversion of the equivalent current dipole model in EEG/MEG

Biomagnetism

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