Real-Time MEG Source Localization Using Regional Clustering

With its millisecond temporal resolution, Magnetoencephalography (MEG) is well suited for real-time monitoring of brain activity. Real-time feedback allows the adaption of the experiment to the subject’s reaction and increases time efficiency by shortening acquisition and off-line analysis. Two formidable challenges exist in real-time analysis: the low signal-to-noise ratio (SNR) and the limited time available for computations. Since the low SNR reduces the number of distinguishable sources, we propose an approach which downsizes the source space based on a cortical atlas and allows to discern the sources in the presence of noise. Each cortical region is represented by a small set of dipoles, which is obtained by a clustering algorithm. Using this approach, we adapted dynamic statistical parametric mapping for real-time source localization. In terms of point spread and crosstalk between regions the proposed clustering technique performs better than selecting spatially evenly distributed dipoles. We conducted real-time source localization on MEG data from an auditory experiment. The results demonstrate that the proposed real-time method localizes sources reliably in the superior temporal gyrus. We conclude that real-time source estimation based on MEG is a feasible, useful addition to the standard on-line processing methods, and enables feedback based on neural activity during the measurements.Deutsche Forschungsgemeinschaft (grant Ba 4858/1-1)National Institutes of Health (U.S.) (grants 5R01EB009048 and 2P41EB015896)Universitätsschule Jena (J21)German Academic Exchange Servic

Time-Frequency Mixed-Norm Estimates: Sparse M/EEG imaging with non-stationary source activations

International audienceMagnetoencephalography (MEG) and electroencephalography (EEG) allow functional brain imaging with high temporal resolution. While solving the inverse problem independently at every time point can give an image of the active brain at every millisecond, such a procedure does not capitalize on the temporal dynamics of the signal. Linear inverse methods (Minimum-norm, dSPM, sLORETA, beamformers) typically assume that the signal is stationary: regularization parameter and data covariance are independent of time and the time varying signal-to-noise ratio (SNR). Other recently proposed non-linear inverse solvers promoting focal activations estimate the sources in both space and time while also assuming stationary sources during a time interval. However such an hypothesis only holds for short time intervals. To overcome this limitation, we propose time-frequency mixed-norm estimates (TF-MxNE), which use time-frequency analysis to regularize the ill-posed inverse problem. This method makes use of structured sparse priors defined in the time-frequency domain, offering more accurate estimates by capturing the non-stationary and transient nature of brain signals. State-of-the-art convex optimization procedures based on proximal operators are employed, allowing the derivation of a fast estimation algorithm. The accuracy of the TF-MxNE is compared to recently proposed inverse solvers with help of simulations and by analyzing publicly available MEG datasets

M/EEG source reconstruction based on Gabor thresholding in the source space

International audienceThanks to their high temporal resolution, source reconstruction based on Magnetoencephalography (MEG) and/or Electroencephalography (EEG) is an important tool for noninvasive functional brain imaging. Since the MEG/EEG inverse problem is ill-posed, inverse solvers employ priors on the sources. While priors are generally applied in the time domain, the time-frequency (TF) characteristics of brain signals are rarely employed as a spatio-temporal prior. In this work, we present an inverse solver which employs a structured sparse prior formed by the sum of $\ell_{21}$ and $\ell_{1}$ norms on the coefficients of the Gabor TF decomposition of the source activations. The resulting convex optimization problem is solved using a first-order scheme based on proximal operators. We provide empirical evidence based on EEG simulations that the proposed method is able to recover neural activations that are spatially sparse, temporally smooth and non-stationary. We compare our approach to alternative solvers based also on convex sparse priors, and demonstrate the benefit of promoting sparse Gabor decompositions via a mathematically principled iterative thresholding procedure

Apport des dictionnaires temps-fréquence, de la parcimonie et des structures pour l'imagerie cérébrale fonctionnelle M/EEG

National audienceOn s'intéresse au problème inverse mal posé rencontré dans la localisation de sources M/EEG (Magneto/Electro Encéphalo Graphique). Bien qu'on ait à disposition un modèle physique réaliste de la diffusion (ou du "mélange") des sources, le caractère très sous-déterminé le rend très difficile à inverser. La nécessité de trouver des a priori forts et pertinent physiquement sur les sources est une des parties difficiles de ce problème. Bien que les ondelettes et les gaborettes soient largement utilisées en traitement du signal pour l'analyse temps-fréquence et le débruitage, elles n'ont été que relativement peu employées afin d'améliorer le problèmes inverse mal-posé de localisation de sources en M/EEG. On présente comment les décompositions temps-fréquence et les a priori de parcimonie structurée peuvent être utilisés afin d'obtenir un a priori convexe et physiologiquement motivé. L'a priori introduit ici favorise des estimations avec peu de sources neuronales activées, tout en ayant un décour temporel lisse. La méthode présentée est alors capable de reconstruire des signaux corticaux non-stationaires. Les résultats obtenus sont comparés avec ceux obtenus par l'etat de l'art sur des signaux MEG simulés, mais aussi sur des données réelles

Source localization algorithm based on topographic matching pursuit

Spatio-temporal decomposition methods in combination with source localization algorithms can contribute to an improved description and allocation of neural activity from electrical and magnetic multichannel measurements. We introduce a new algorithm, which combines Topographic Matching Pursuit as spatio-temporal decomposition method with a dipole-source localization. The new algorithm is applied to EEG-data obtained from a photic driving experiment with eleven volunteers. In comparison to a hitherto published Multichannel Matching Pursuit (MMP) source localization the new algorithm shows, for a Mirrored-Dipole configuration, higher Goodness-of-Fit values, if temporal asynchrony exists in the EEG-channels. We conclude that the suggested algorithm is more appropriate for source reconstruction in case of temporal asynchrony than MMP-based procedures used so far