Robust Bayesian estimation of the location, orientation, and time course of multiple correlated neural sources using MEG

Abstract

The synchronous brain activity measured via MEG (or EEG) can be interpreted as arising from a collection (possibly large) of current dipoles or sources located throughout the cortex. Estimating the number, location, and time course of these sources remains a challenging task, one that is significantly compounded by the effects of source correlations and unknown orientations and by the presence of interference from spontaneous brain activity, sensor noise, and other artifacts. This paper derives an empirical Bayesian method for addressing each of these issues in a principled fashion. The resulting algorithm guarantees descent of a cost function uniquely designed to handle unknown orientations and arbitrary correlations. Robust interference suppression is also easily incorporated. In a restricted setting, the proposed method is shown to produce theoretically zero reconstruction error estimating multiple dipoles even in the presence of strong correlations and unknown orientations, unlike a variety of existing Bayesian localization methods or common signal processing techniques such as beamforming and sLORETA. Empirical results on both simulated and real data sets verify the efficacy of this approach.

The relationship between MEG and fMRI

“…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.…”

An evaluation of EEG scanner's dependence on the imaging technique, forward model computation method, and array dimensionality

“…For example l 2 -norm approaches, like the weighted minimum norm method [2] and low resolution electromagnetic tomography (LORETA) [3], assume sources to be diffuse and highly distributed. On the other hand models based on the l 1 -norm [4], l p -norms [5], minimum variance beamformer [6], Bayesian model averaging [7], multiple priors models [8], and automatic relevance determination methods [9], [10] While the recent EEG imaging literature mainly have focused on the source reconstruction performance using high density EEG equipment we here draw the attention to quantify the performance of EEG brain imaging using few electrodes as we are interested in mobile EEG equipment. We have previously, demonstrated the feasibility of performing online brain imaging on a smartphone device [11] allowing for experiments in more naturalistic settings.…”

Bayesian EEG source localization using a structured sparsity prior

“…The first data set corresponds to the auditory evoked responses to left ear pure tone stimulus while the second one consists of the evoked responses to facial stimulus. The results of the proposed method are compared with the weighted ℓ 21 mixed norm (Gramfort et al, 2012), the Champagne model (Wipf et al, 2010) and the method investigated in Friston et al (2008) based on multiple sparse priors.…”

“…Several Bayesian methods have also been used to solve the inverse problem (Friston et al, 2008;Stahlhut et al, 2013;Wipf et al, 2010;Lucka et al, 2012). Friston et al (2008) developed the multiple sparse priors (MSP) approach, in which they segment the brain into different pre-defined regions and promote all the dipoles in each region to be active or inactive jointly.…”

“…Friston et al (2008) developed the multiple sparse priors (MSP) approach, in which they segment the brain into different pre-defined regions and promote all the dipoles in each region to be active or inactive jointly. In contrast, Wipf et al developed the Champagne algorithm to promote activity to be concentrated on a sparse set of dipoles (Wipf et al, 2010). Lucka et al (2012) studied a hierarchical Bayesian model (HBM) offering significant improvements over established methods such as MNE and sLoreta.…”