Cortical Imaging on a Head Template: A Simulation Study Using a Resistor Mesh Model (RMM)

Abstract

The T1 head template model used in Statistical Parametric Mapping Version 2000 (SPM2), was segmented into five layers (scalp, skull, CSF, grey and white matter) and implemented in 2 mm voxels. We designed a resistor mesh model (RMM), based on the finite volume method (FVM) to simulate the electrical properties of this head model along the three axes for each voxel. Then, we introduced four dipoles of high eccentricity (about 0.8) in this RMM, separately and simultaneously, to compute the potentials for two sets of conductivities. We used the direct cortical imaging technique (CIT) to recover the simulated dipoles, using 60 or 107 electrodes and with or without addition of Gaussian white noise (GWN). The use of realistic conductivities gave better CIT results than standard conductivities, lowering the blurring effect on scalp potentials and displaying more accurate position areas when CIT was applied to single dipoles. Simultaneous dipoles were less accurately localized, but good qualitative and stable quantitative results were obtained up to 5% noise level for 107 electrodes and up to 10% noise level for 60 electrodes, showing that a compromise must be found to optimize both the number of electrodes and the noise level. With the RMM defined in 2 mm voxels, the standard 128-electrode cap and 5% noise appears to be the upper limit providing reliable source positions when direct CIT is used. The admittance matrix defining the RMM is easy to modify so as to adapt to different conductivities. The next step will be the adaptation of individual real head T2 images to the RMM template and the introduction of anisotropy using diffusion imaging (DI).