Multiple dipole modeling and localization from spatio-temporal MEG data

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

doi:10.1109/10.141192

Cited by 942 publications

(553 citation statements)

References 29 publications

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“…In statistical terms, this distinction is one of 'fixed' versus 'random' effects. Hammalainen and Ilmoniemi, 1994), LORETA (Pascal Marqui et al, 1994), MUSIC (Mosher et al, 1992), DICS (Gross et al, 2001) and beamformers (Robinson and Vrba, 1999;Sekihara et al, 1999); each of which makes a different set of fundamental underlying assumptions. Thus, in making choices to solve the inverse problem, variability is introduced to the outcome of the analysis.…”

Section: Discussionmentioning

confidence: 99%

The mechanisms of tinnitus: Perspectives from human functional neuroimaging

2009

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In this review, we highlight the contribution of advances in human neuroimaging to the current understanding of central mechanisms underpinning tinnitus and explain how interpretations of neuroimaging data have been guided by animal models. The primary motivation for studying the neural subtrates of tinnitus in humans has been to demonstrate objectively its representation in the central auditory system and to develop a better understanding of its diverse pathophysiology and of the functional interplay between sensory, cognitive and affective systems. The ultimate goal of neuroimaging is to identify subtypes of tinnitus in order to better inform treatment strategies. The three neural mechanisms considered in this review may provide a basis for TI classification. While human neuroimaging evidence strongly implicates the central auditory system and emotional centres in TI, evidence for the precise contribution from the three mechanisms is unclear because the data are somewhat inconsistent. We consider a number of methodological issues limiting the field of human neuroimaging and recommend approaches to overcome potential inconsistency in results arising from poorly matched participants, lack of appropriate controls and low statistical power. AbstractIn this review, we highlight the contribution of advances in human neuroimaging to the current understanding of central mechanisms underpinning tinnitus and explain how interpretations of neuroimaging data have been guided by animal models. The primary motivation for studying the neural subtrates of tinnitus in humans has been to demonstrate objectively its representation in the central auditory system and to develop a better understanding of its diverse pathophysiology and of the functional interplay between sensory, cognitive and affective systems. The ultimate goal of neuroimaging is to identify subtypes of tinnitus in order to better inform treatment strategies. The three neural mechanisms considered in this review may provide a basis for TI classification. While human neuroimaging evidence strongly implicates the central auditory system and emotional centres in TI, evidence for the precise contribution from the three mechanisms is unclear because the data are somewhat inconsistent. We consider a number of methodological issues limiting the field of human neuroimaging and recommend approaches to overcome potential inconsistency in results arising from poorly matched participants, lack of appropriate controls and low statistical power.

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Section: Discussionmentioning

confidence: 99%

The mechanisms of tinnitus: Perspectives from human functional neuroimaging

2009

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“…Provided the activity is generated within small and well defined regions a multiple dipole model can provide valuable information. Some very promising algorithms for multiple dipole analysis have been proposed recently (Mosher et al 1990), which can deal with the problem of deciding how many such point sources need be considered.…”

Section: Discussionmentioning

confidence: 99%

Comparison of single current dipole and Magnetic Field Tomography analyses of the cortical response to auditory stimuli

et al. 1993

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Summary: Measurements of the magnetic field elicited by a 50 ms long auditory stimulus, from three normal subjects and one head injured subject, are used to estimate the three dimensional distribution of generators in the brain. The resulting images are compared with point source solutions obtained with the usual single current dipole fitting procedures, over a latency range which includes the extrema in the (average) measured signal. In all cases considered, 100 or so epochs time-locked to the stimulus were magnetically recorded. These were averaged, and then analyzed using two techniques; a new distributed current model known as Magnetic Field Tomography (MFT), and the standard single current dipole (SCD) model. Both methods provide estimates of the current generators in the brain. In two of the normal subjects, the MFT solutions are super-imposed onto Magnetic Resonance Images (MRI) of the relevant cortical area. The results show that when the SCD model provides a reasonable description of the data, the MFT estimate shows one dominant localized region in agreement with the current dipole position. In the MFT sequence of solutions the activity evolves smoothly; multiple areas of activity often arise as the focal activity in one region declines while focal activity in another region grows. In contrast the SCD solutions during these Intermediate periods fit the data poorly, and may move erratically from one locale to another. We conclude that MFT seems to provide a reasonable description of the activity through cortical and subcortical regions. The evolution of activity, as derived from the average signal, can be traced continuously from the onset of the stimulus, not just at the peaks. This could be particularly important for clinical applications where injury or other pathology produce a response with no clear dipolar pattern.

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“…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).…”

Section: Introductionmentioning

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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Multiple dipole modeling and localization from spatio-temporal MEG data

Cited by 942 publications

References 29 publications

The mechanisms of tinnitus: Perspectives from human functional neuroimaging

The mechanisms of tinnitus: Perspectives from human functional neuroimaging

Comparison of single current dipole and Magnetic Field Tomography analyses of the cortical response to auditory stimuli

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

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