Multiple sparse priors for the M/EEG inverse problem

Friston, Karl J.; Harrison, L.; Daunizeau, Jean; Kiebel, Stefan J.; Phillips, Christophe; Trujillo-Barreto, Nelson J.; Henson, Richard; Flandin, Guillaume; Mattout, Jérémie

doi:10.1016/j.neuroimage.2007.09.048

Cited by 555 publications

(644 citation statements)

References 30 publications

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“…Even larger mislocation errors would be foreseeably obtained for maps reconstructed from electroencephalographic (EEG) signals since they spread more than MEG signals and their propagation is harder to model (H€ am€ al€ ainen et al, 1993). One way to avoid the extreme smoothness of MEG maps is to use spatially sparse imaging methods such as, e.g., minimum current estimation (Uutela et al, 1999), minimum mixed-norm estimation (Gramfort et al, 2013(Gramfort et al, , 2012, or the multiple sparse priors approach (Friston et al, 2008). In this case the MEG maps are very focal, but this opposite extreme poses other problems to contrast two different conditions at the group level (unless maps are smoothed afterwards).…”

Section: Tablementioning

confidence: 99%

Contrasting functional imaging parametric maps: The mislocation problem and alternative solutions

2018

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A B S T R A C TIn the field of neuroimaging, researchers often resort to contrasting parametric maps to identify differences between conditions or populations. Unfortunately, contrast patterns mix effects related to amplitude and location differences and tend to peak away from sources of genuine brain activity to an extent that scales with the smoothness of the maps. Here, we illustrate this mislocation problem on source maps reconstructed from magnetoencephalographic recordings and propose a novel, dedicated location-comparison method. In realistic simulations, contrast mislocation was on average~10 mm when genuine sources were placed at the same location, and was still above 5 mm when sources were 20 mm apart. The dedicated location-comparison method achieved a sensitivity of~90% when inter-source distance was 12 mm. Its benefit is also illustrated on real brain-speech entrainment data. In conclusion, contrasts of parametric maps provide precarious information for source location. To specifically address the question of location difference, one should turn to dedicated methods as the one proposed here.

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Section: Tablementioning

confidence: 99%

Contrasting functional imaging parametric maps: The mislocation problem and alternative solutions

2018

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“…EEG activity arises when regional active neurons are active in synchrony (Baillet et al, 2001) and therefore many EEG inverse solvers incorporate an assumption of spatial smoothness (Phillips et al, 2002;Pascual-Marqui et al, 2002;Friston et al, 2008;Haufe et al, 2008). To obtain a spatially smooth source distribution we introduce spatial basis 130 functions following the implementation described in the multiple sparse priors model (MSP) (Friston et al, 2008).…”

Section: Spatial Coherencementioning

confidence: 99%

“…Although no gold standard EEG inverse solver has been established, the field is con-20 verging on methods that employ spatial sparsity (Gorodnitsky and Rao, 1997;Wipf and Rao, 2007;Vega-Hernández et al, 2008;Friston et al, 2008;Zhang and Rao, 2011;Stahlhut et al, 2011;Montoya-Martinez et al, 2012;Gramfort et al, 2013;Hansen et al, 2013c;Hansen and Hansen, 2014;Andersen et al, 2014). Evidence was presented, in recent work (Delorme et al, 2012) that the instantaneous independent components of EEG 25 signals are dipolar and localized.…”

Section: Introductionmentioning

confidence: 99%

“…Meaningful solutions thus rely on a qualified guess at the number of active dipoles. In contrast, distributed imaging approaches 40 estimate the source strength in a large number of source locations (Gorodnitsky et al, 1995;Friston et al, 2008). These methods thus avoid making subjective assumptions, but do render the EEG inverse problem underdetermined.…”

Section: Introductionmentioning

confidence: 99%

“…To stabilize the solution it is useful to impose some level of temporal smoothness. A basic scheme is to enforce that the locations of activity are 50 fixed throughout an analysis window (Wipf and Rao, 2007;Friston et al, 2008;Ou et al, 2009;Zhang and Rao, 2011;Hansen et al, 2013c). While useful for short time windows, this may be less appropriate for more extended and non-stationary settings.…”

Section: Introductionmentioning

confidence: 99%

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Spatio-temporal reconstruction of brain dynamics from EEG with a Markov prior

Hansen

2017

NeuroImage

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Electroencephalography (EEG) can capture brain dynamics in high temporal resolution.By projecting the scalp EEG signal back to its origin in the brain also high spatial resolution can be achieved. Source localized EEG therefore has potential to be a very powerful tool for understanding the functional dynamics of the brain. Solving the inverse problem of EEG is however highly ill-posed as there are many more potential locations of the EEG generators than EEG measurement points. Several well-known properties of brain dynamics can be exploited to alleviate this problem. More short ranging connections exist in the brain than long ranging, arguing for spatially focal sources. Additionally, recent work (Delorme et al., 2012) argues that EEG can be decomposed into components having sparse source distributions. On the temporal side both short and long term stationarity of brain activation are seen. We summarize these insights in an inverse solver, the so-called "Variational Garrote" (Kappen and Gómez, 2013). Using a Markov prior we can incorporate flexible degrees of temporal stationarity. Through spatial basis functions spatially smooth distributions are obtained. Sparsity of these are inherent to the Variational Garrote solver. We name our method the MarkoVG and demonstrate its ability to adapt to the temporal smoothness and spatial sparsity in simulated EEG data.Finally a benchmark EEG dataset is used to demonstrate MarkoVG's ability to recover non-stationary brain dynamics.

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A Potts‐mixture spatiotemporal joint model for combined magnetoencephalography and electroencephalography data

2019

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We develop a new methodology for determining the location and dynamics of brain activity from combined magnetoencephalography (MEG) and electroencephalography (EEG) data. The resulting inverse problem is ill-posed and is one of the most difficult problems in neuroimaging data analysis.In our development we propose a solution that combines the data from three different modalities, MRI, MEG, and EEG, together. We propose a new Bayesian spatial finite mixture model that builds on the mesostate-space model developed by Daunizeau and Friston (2007). Our new model incorporates two major extensions: (i) We combine EEG and MEG data together and formulate a joint model for dealing with the two modalities simultaneously; (ii) we incorporate the Potts model to represent the spatial dependence in an allocation process that partitions the cortical surface into a small number of latent states termed mesostates. The cortical surface is obtained from MRI. We formulate the new spatiotemporal model and derive an efficient procedure for simultaneous point estimation and model selection based on the iterated conditional modes algorithm combined with * local polynomial smoothing. The proposed method results in a novel estimator for the number of mixture components and is able to select active brain regions which correspond to active variables in a high-dimensional dynamic linear model. The methodology is investigated using synthetic data and simulation studies and then demonstrated on an application examining the neural response to the perception of scrambled faces. R software implementing the methodology along with several sample datasets are available at the following GitHub repository https://github.com/v2south/PottsMix.

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Multiple sparse priors for the M/EEG inverse problem

Cited by 555 publications

References 30 publications

Contrasting functional imaging parametric maps: The mislocation problem and alternative solutions

Contrasting functional imaging parametric maps: The mislocation problem and alternative solutions

Spatio-temporal reconstruction of brain dynamics from EEG with a Markov prior

A Potts‐mixture spatiotemporal joint model for combined magnetoencephalography and electroencephalography data

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