Individual FEM pipeline for EEG source analysis requiring minimal user intervention
Creators: name: Lanfer, Benjamin
name: Paul-Jordanov, Isabella
name: Wolters, Carsten H.
Publisher: name: F1000Research
Year: 2016

description: <b>Introduction</b><br>Individual, realistic volume conductor models improve EEG source analysis accuracy. Still, these models have not found widespread application in the EEG community. Reasons for this are the challenges of  correctly segmenting the head tissues, especially the skull, and the mesh generation required for the numerical solution of the forward problem.<br>Here, we are presenting a complete pipeline for creating and using individual, realistically shaped head models for EEG source analysis. Minimal user interaction is required, and computations are sufficiently fast.<br>The efficacy of our pipeline is demonstrated on two exemplary data sets showing that the more realistic head models improve the source reconstruction compared to a spherical model and a standardized, realistic model.<br><br><b>Methods</b><br>The pipeline consists of four major parts: MRI pre-processing, volume conductor segmentation, leadfield computation, and the EEG forward computation.<br>MRI pre-processing includes defining landmark points by the user, automatic correction of shading artifacts (Pham and Prince 1999), as well as automatic scalp (Chan and Vese 2001) and cortex surface reconstruction (Zhang, Brady, and Smith 2001).<br>Next, the four tissues most relevant for volume conduction – scalp, skull, CSF, and brain – are segmented (Fig. 1a). For this purpose a newly developed approach is employed, which is based on a Markov Random field representation of the arrangement of head tissues. Previous studies have shown that the approach is able to accurately reconstruct the skull geometry (Lanfer et al. 2013). Based on the labeled MR image, a 2mm hexahedral finite element mesh is constructed and refined by smoothing the tissue interfaces using the node-shift approach  (Wolters et al. 2007). The geometry-adapted hexahedral meshes created in this way (Fig. 1b) have been proven suitable for EEG source analysis (Rullmann et al. 2009).<br>Electrode positions are coregistered to the previously reconstructed scalp surface, and the leadfield is computed on a regular 2mm grid covering the whole brain using the finite element method (FEM). To speed up computation despite the large number of sources, the transfer matrix approach (Wolters, Grasedyck, and Hackbusch 2004) is employed.<br>The forward computation during source analysis uses interpolation based on the pre-computed leadfield table by means of an accurate second order Bezier spline interpolation (Yvert, Crouzeix‐Cheylus, and Pernier 2001).<br><br><b>Results</b><br>Our pipeline is able to generate accurate volume conductor segmentations. For the skull, an agreement of 85% with a CT-based reference segmentation was found (Lanfer et al. 2013). The pipeline was designed to require minimal user interaction. For each subject only around three minutes of interaction is necessary for set-up. The automatic segmentation procedures and the leadfield computation can then run unsupervised. Computation time on a fairly modern laptop is around 40 minutes for one dataset.<br>We applied the presented processing steps to two data sets: an auditory odd-ball experiment and a case of basal temporal lobe epilepsy. Using the individual FEM model, the generators of the auditory N100 component were reconstructed close to Heschl's gyrus as expected. Employing a four-shell ellipsoidal model resulted in too superior dipole locations. Similarly, for the epilepsy case only the individual FEM model was able to reconstruct the inter-ictal activity at the base of the temporal lobe with a plausible dipole orientation. The four-shell ellipsoidal and the standard FEM model again yielded too superior sources.<br><br><b>Conclusion</b><br>The presented pipeline allows effortless creation of realistically shaped, individual head models for EEG source analysis. Due to the minimal required effort, individual models hopefully will find a more wide-spread application facilitating improved source localization accuracy for EEG source analysis.<br>