Neuromagnetic source imaging with FOCUSS: a recursive weighted minimum norm algorithm

Gorodnitsky, I.F.; George, John; Rao, Bhaskar D.

doi:10.1016/0013-4694(95)00107-a

Cited by 503 publications

(358 citation statements)

References 28 publications

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“…Over the last two decades there have been significant advances in this field of research, which have been key to providing insights into the spatial relationship between MEG and fMRI. There are many algorithms available for MEG source estimation, with examples including Minimum Norm (Hämäläinen and Ilmoniemi, 1994), LORETA (Pascual-Marqui et al, 1994), FOCUSS (Gorodnitsky et al, 1995), MUSIC (Mosher et al, 1992), beamforming Vrba, 1998, Sekihara et al, 2001), CHAMPAGNE (Wipf et al, 2010) and many others. The similarities and differences of these reconstruction techniques have been discussed elsewhere (e.g.…”

Section: Spatial Relationshipsmentioning

confidence: 99%

The relationship between MEG and fMRI

et al. 2014

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In recent years functional neuroimaging techniques such as fMRI, MEG, EEG and PET have provided researchers with a wealth of information on human brain function. However none of these modalities can measure directly either the neuro-electrical or neuro-chemical processes that mediate brain function. This means that metrics directly reflecting brain 'activity' must be inferred from other metrics (e.g. magnetic fields (MEG) or haemodynamics (fMRI)). To overcome this limitation, many studies seek to combine multiple complementary modalities and an excellent example of this is the combination of MEG (which has high temporal resolution) with fMRI (which has high spatial resolution). However, the full potential of multi-modal approaches can only be truly realised in cases where the relationship between metrics is known. In this paper, we explore the relationship between measurements made using fMRI and MEG. We describe the origins of the two signals as well as their relationship to electrophysiology. We review multiple studies that have attempted to characterise the spatial relationship between fMRI and MEG, and we also describe studies that exploit the rich information content of MEG to explore differing relationships between MEG and fMRI across neural oscillatory frequency bands. Monitoring the brain at "rest" has become of significant recent interest to the neuroimaging community and we review recent evidence comparing MEG and fMRI metrics of functional connectivity. A brief discussion of the use of magnetic resonance spectroscopy (MRS) to probe the relationship between MEG/fMRI and neurochemistry is also given. Finally, we highlight future areas of interest and offer some recommendations for the parallel use of fMRI and MEG.

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Section: Spatial Relationshipsmentioning

confidence: 99%

The relationship between MEG and fMRI

et al. 2014

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“…Twenty percent was estimated as the smallest regularization value still yielding stable solutions. Additionally, two further iteration steps were computed to get spatially more focal BSCD distributions (Gorodnitsky et al, 1995). During each iteration, the last solution is taken as a weighting matrix for the next, thereby sharpening the very distributed L2-Norm.…”

Section: Source Localizationmentioning

confidence: 99%

Localizing the distributed language network responsible for the N400 measured by MEG during auditory sentence processing

et al. 2006

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We studied auditory sentence comprehension using magnetoencephalography while subjects listened to sentences whose correctness they had to judge subsequently. The localization and the time course of brain electrical activity during processing of correct and semantically incorrect sentences were estimated by computing a brain surface current density within a cortical layer for both conditions. Finally, a region of interest (ROI) analysis was conducted to determine the time course of specific locations. A magnetic N400 was

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“…In the context of distributed approaches, priors based on mathematical, anatomical, physiological and functional heuristics have been considered (Hämäläinen and Llmoniemi, 1994;PascualMarqui et al, 1994;Gorodnitsky et al, 1995;Baillet and Garnero, 1997;Dale et al, 2000;Phillips et al, 2002;Mattout et al, 2003;Babiloni et al, 2004). Although these approaches involve different constraints and inverse criteria, they all obtain a unique solution by optimizing a goodness of fit term and a prior term in a carefully balanced way.…”

Section: Introductionmentioning

confidence: 99%

MEG Source Localization under Multiple Constraints: An Extended Bayesian Framework

Mattout¹,

Phillips²,

Henson³

et al.

Complex Medical Engineering

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To use Electroencephalography (EEG) and Magnetoencephalography (MEG) as functional brain 3D imaging techniques, identifiable distributed source models are required. The reconstruction of EEG/ MEG sources rests on inverting these models and is ill-posed because the solution does not depend continuously on the data and there is no unique solution in the absence of prior information or constraints. We have described a general framework that can account for several priors in a common inverse solution. An empirical Bayesian framework based on hierarchical linear models was proposed for the analysis of functional neuroimaging data [Friston, K., Penny, W., Phillips, C., Kiebel, S., Hinton, G., Ashburner, J., 2002. Classical and Bayesian inference in neuroimaging: theory. NeuroImage 16, 465 -483] and was evaluated recently in the context of EEG [Phillips, C., Mattout, J., Rugg, M.D., Maquet, P., Friston, K., 2005. An empirical Bayesian solution to the source reconstruction problem in EEG. NeuroImage 24, 997 -1011]. The approach consists of estimating the expected source distribution and its conditional variance that is constrained by an empirically determined mixture of prior variance components. Estimation uses Expectation-Maximization (EM) to give the Restricted Maximum Likelihood (ReML) estimate of the variance components (in terms of hyperparameters) and the Maximum A Posteriori (MAP) estimate of the source parameters. In this paper, we extend the framework to compare different combinations of priors, using a second level of inference based on Bayesian model selection. Using MonteCarlo simulations, ReML is first compared to a classic Weighted Minimum Norm (WMN) solution under a single constraint. Then, the ReML estimates are evaluated using various combinations of priors. Both standard criterion and ROC-based measures were used to assess localization and detection performance. The empirical Bayes approach proved useful as: (1) ReML was significantly better than WMN for single priors; (2) valid location priors improved ReML source localization; (3) invalid location priors did not significantly impair performance. Finally, we show how model selection, using the logevidence, can be used to select the best combination of priors. This enables a global strategy for multiple prior-based regularization of the MEG/EEG source reconstruction. D

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Neuromagnetic source imaging with FOCUSS: a recursive weighted minimum norm algorithm

Cited by 503 publications

References 28 publications

The relationship between MEG and fMRI

The relationship between MEG and fMRI

Localizing the distributed language network responsible for the N400 measured by MEG during auditory sentence processing

MEG Source Localization under Multiple Constraints: An Extended Bayesian Framework

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