Simulation studies of multiple dipole neuromagnetic source localization: model order and limits of source resolution

Supek, Selma; Aine, Cheryl J.

doi:10.1109/10.237672

Cited by 139 publications

(78 citation statements)

References 18 publications

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“…With conventional approaches, the goodnessof-fit (GOF) falls into the trap of over-fitting, i.e., attaining a high GOF by selecting an overly complex model. There are other measures, in the literature, that either use classical model selection, e.g., an F-test (Supek and Aine, 1993), or use a simple approximation to the model evidence, e.g., the Bayesian or Akaike Information Criterion (Beal, 2003;Penny et al, 2004). Although these measures are in widespread use, they do not work well when different models can have the same number of parameters but different informative priors.…”

Section: Discussionmentioning

confidence: 99%

“…We summarise the results for each of the simulations by reporting the (i) negative free energy as an approximation to model evidence, (ii) the goodness-of-fit (Supek and Aine, 1993) and (iii) the Akaike Information Criterion (Penny et al, 2004), using their Eqs. (18) and (21).…”

Section: Two-and Three-dipole Models: Case Studiesmentioning

confidence: 99%

“…This model comparison is superior to classical goodness-of-fit measures, because it takes into account the complexity of the models (e.g., the number of dipoles) and, implicitly, uncertainty about the model parameters. For this reason, classical schemes have adopted Supek and Aine (1993) for an example using classical model comparison). For most models, the AIC and its cousin, the Bayesian Information Criterion (BIC) are a rough approximation to the model evidence (Beal, 2003;Penny et al, 2004), and are less accurate than the negative free energy.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Two-and Three-dipole Models: Case Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2008

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“…As pointed out by numerous authors [e.g., Fuchs et al, 1998;Hari et al, 1988;Liu et al, 1998;Murro et al, 1995;Supek and Aine, 1993], localization accuracy is highly dependent on the location of the source. Therefore, to better approximate realistic data which can occur anywhere in the brain, our simulations use a large random sampling of source locations to provide an average estimate of localization accuracy.…”

Section: Monte Carlo Simulationsmentioning

confidence: 99%

Monte Carlo simulation studies of EEG and MEG localization accuracy

Liu¹,

Dale²,

Belliveau³

2002

Hum. Brain Mapp.

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Both electroencephalography (EEG) and magnetoencephalography (MEG) are currently used to localize brain activity. The accuracy of source localization depends on numerous factors, including the specific inverse approach and source model, fundamental differences in EEG and MEG data, and the accuracy of the volume conductor model of the head (i.e., the forward model). Using Monte Carlo simulations, this study removes the effect of forward model errors and theoretically compares the use of EEG alone, MEG alone, and combined EEG/MEG data sets for source localization. Here, we use a linear estimation inverse approach with a distributed source model and a realistic forward head model. We evaluated its accuracy using the crosstalk and point spread metrics. The crosstalk metric for a specified location on the cortex describes the amount of activity incorrectly localized onto that location from other locations. The point spread metric provides the complementary measure: for that same location, the point spread describes the mis-localization of activity from that specified location to other locations in the brain. We also propose and examine the utility of a "noise sensitivity normalized" inverse operator. Given our particular forward and inverse models, our results show that 1) surprisingly, EEG localization is more accurate than MEG localization for the same number of sensors averaged over many source locations and orientations; 2) as expected, combining EEG with MEG produces the best accuracy for the same total number of sensors; 3) the noise sensitivity normalized inverse operator improves the spatial resolution relative to the standard linear estimation operator; and 4) use of an a priori fMRI constraint universally reduces both crosstalk and point spread. Hum. Brain Mapping 16:47-62, 2002.

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“…The cost function we adopted in the minimization procedure (i.e., minimizing the difference between the measured magnetic field distribution and the magnetic field calculated for an assumed model) is the 2 where the noise estimated for each sensor location is taken into consideration. Specifically, we used the reduced chi-square value which is 2 divided by the number of degrees of freedom (Bevington, 1969;Supek and Aine, 1993;Huang et al, 1998).…”

Section: Source Localization and Statistical Methodsmentioning

confidence: 99%

Aging: Compensation or maturation?

Aine

Woodruff²,

Knoefel

et al. 2006

NeuroImage

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Neuroimaging studies of healthy aging often reveal differences in neural activation patterns between young and elderly groups for episodic memory tasks, even though there are no differences in behavioral performance. One explanation typically offered is that the elderly compensate for their memory deficiencies through the recruitment of additional prefrontal regions. The present study of healthy aging compared magnetoencephalographic (MEG) time-courses localized to specific cortical regions in two groups of subjects (20-29 years and ≥65 years) during a visual delayed-match-to-sample (DMS) task. MR morphometrics and neuropsychological test results were also examined with the hope of providing insight into the nature of the age-related differences. The behavioral results indicated no differences in performance between young and elderly groups. Although there was a main effect of age on the latency of the initial peak in primary/secondary visual cortex, these longer latencies were not correlated with the performance of elderly on the DMS task. The lateral occipital gyrus (LOG) revealed qualitatively different patterns of activity for the two age groups corroborated by neuropsychological test results. Morphometric results for the young versus elderly groups revealed less white (WM) and gray matter (GM) volumes in the frontal lobes of the elderly. When a group of middle-aged subjects (33-43 years) was included in the morphometric analyses, the middle-aged subjects revealed statistically greater WM volumes in frontal and parietal cortex suggesting immature WM tracts in the young. Perhaps our elderly utilized a different strategy compared to the young due to the different brain maturation levels of these groups.

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Simulation studies of multiple dipole neuromagnetic source localization: model order and limits of source resolution

Cited by 139 publications

References 18 publications

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

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

Monte Carlo simulation studies of EEG and MEG localization accuracy

Aging: Compensation or maturation?

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