Qualitative assessment.

<p>Visual analysis of source localization results together with Area Under the ROC curve (AUC) values for a simulated source of spatial extent <i>s_e</i> = 2 and eccentricity 79 mm. All source localization results are presented as the absolute value of the current density at the peak of the spike, normalized to its maximum activity and thresholded upon the level of background activity <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055969#pone.0055969-Otsu1" target="_blank">[51]</a>. (a) Theoretical simulated source: spatial extent of the cortical source and associated simulated MEG signal for all MEG sensors (data being localized within a window of 20 time samples around the first peak of the spike). (b) Source localization results obtained for MEM-s, CMEM-s and COH-s at <i>s</i> = 3. (c) Source localization results obtained for MEM-s, CMEM-s and COH-s at <i>s</i> = 5. (d) Source localization results obtained for IID and COH.</p

Qualitative assessment.

<p>Visual analysis of source localization results together with Area Under the ROC curve (AUC) values for a simulated source of spatial extent <i>s_e</i> = 5 and eccentricity 79 mm. Remaining information same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055969#pone-0055969-g002" target="_blank">Figure 2</a>.</p

Effect of spatial clustering scale <i>s</i>.

<p>Distribution of AUC results using boxplot representations over 100 simulations of randomly placed sources for all source localization methods (x-axis from left to right: MEM-s with <i>s</i> from 3 to 6 (purple), CMEM-s with <i>s</i> from 3 to 6 (red), COH-s with <i>s</i> from 3 to 6 (blue), (y-axis: AUC values); (Horizontal line, AUC = 0.8). (a) Evaluation using simulated sources of spatial extent <i>s_e</i> = 2 (∼3 cm²). (b) Evaluation using simulated sources of spatial extent <i>s_e</i> = 4 (∼11 cm²). (c) Evaluation using simulated sources of spatial extent <i>s_e</i> = 6 (∼24 cm²).</p

MEG Source Localization of Spatially Extended Generators of Epileptic Activity: Comparing Entropic and Hierarchical Bayesian Approaches

<div><p>Localizing the generators of epileptic activity in the brain using Electro-EncephaloGraphy (EEG) or Magneto-EncephaloGraphy (MEG) signals is of particular interest during the pre-surgical investigation of epilepsy. Epileptic discharges can be detectable from background brain activity, provided they are associated with spatially extended generators. Using realistic simulations of epileptic activity, this study evaluates the ability of distributed source localization methods to accurately estimate the location of the generators and their sensitivity to the spatial extent of such generators when using MEG data. Source localization methods based on two types of realistic models have been investigated: (i) brain activity may be modeled using cortical parcels and (ii) brain activity is assumed to be locally smooth within each parcel. A Data Driven Parcellization (DDP) method was used to segment the cortical surface into non-overlapping parcels and diffusion-based spatial priors were used to model local spatial smoothness within parcels. These models were implemented within the Maximum Entropy on the Mean (MEM) and the Hierarchical Bayesian (HB) source localization frameworks. We proposed new methods in this context and compared them with other standard ones using Monte Carlo simulations of realistic MEG data involving sources of several spatial extents and depths. Detection accuracy of each method was quantified using Receiver Operating Characteristic (ROC) analysis and localization error metrics. Our results showed that methods implemented within the MEM framework were sensitive to all spatial extents of the sources ranging from 3 cm² to 30 cm², whatever were the number and size of the parcels defining the model. To reach a similar level of accuracy within the HB framework, a model using parcels larger than the size of the sources should be considered.</p> </div

Qualitative assessment.

<p>Visual analysis of source localization results together with Area Under the ROC curve (AUC) values for a simulated source of spatial extent <i>s_e</i> = 3 and eccentricity 70 mm. Remaining information same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055969#pone-0055969-g002" target="_blank">Figure 2</a>, except that cluster scales <i>s</i> = 4 (b) and <i>s</i> = 6 (c) were considered for MEM-s, CMEM-s and COH-s.</p

Parcellization.

<p>Examples of clustering of the cortical surface at different spatial scales s obtained using the DDP technique (each color represents one parcel).</p

Median (Med) and L1 Dispersion (Disp) of MSE and Dmin over 100 source configurations for all five methods, all five spatial extents <i>s_e</i> = <i>2,3,4,5,6</i> and all four clustering scale <i>s = 3,4,5,6</i> in MEM-s, CMEM-s and COH-s.

<p>MSE = Mean Squared Error, Dmin = Minimum geodesic distance between the local extrema of the reconstructed source and the simulated source, L1 Dispersion = the average of the absolute deviations from the median.</p

Comparison of detection accuracy AUC.

<p>Plot showing the (a) effect of background activity versus data of interest for the parcellization: MEM-s (purple), CMEM-s (red) and COH-s (blue) (x-axis: AUC value for Baseline, y-axis: AUC value for data), and (b) effect of single spike localization versus average spike localization: IID (green), COH (black), MEM-s (purple), CMEM-s (red) and COH-s (blue) (x-axis: AUC value for single spike localization, y-axis: AUC value for averaged spike localization).</p