Improving lesion conductivity estimate by means of EEG source localization sensitivity to model parameter.
EEG-based source localization techniques use scalp-potential data to estimate the location of underlying neural activity. EEG source location reconstruction requires the assumption of a source model and the assumption of a conductive head model. Brain lesions can present conductivity values that are dramatically different from those of surrounding normal tissues and have to be included in head models for accurate neural source reconstruction. It is therefore necessary to analyze subjects' anatomic images (using MRI or computed tomography) to identify lesion type and to assign the appropriate conductivity value. Source localization accuracy may be influenced by uncertainties in tissue conductivity assignment during head model construction. The authors present a sensitivity study quantifying the effect of uncertainty in brain lesion conductivity assignment on EEG dipole source localization. They adopted an eccentric-spheres head model in which an eccentric bubble approximated the effects of actual brain lesions. After simulating EEG signal measurement in 64 different pathologic situations, an inverse dipole fitting procedure was carried out, assuming an incorrect lesion conductivity assignment ranging from a half to twice the real value. Incorrect lesion conductivity assignment led to markedly wrong source reconstruction for highly conductive lesions like liquid-filled ones (localization errors as much as 1.7 cm). Conversely, low sensitivity to uncertainties in conductivity assignment was found for lesions with low conductivity like calcified tumors. The authors propose a method based on residual error analysis to improve the lesion conductivity estimate. This procedure can identify lesion tissue conductivity with only a few percent error and guarantees source localization errors less than 5 mm.
Vatta
Federica
F
Department of Electrotechnics, Electronics and Computer Science, University of Trieste, Via Valerio 10, 34127 Trieste, Italy.
Bruno
Paolo
P
Inchingolo
Paolo
P
D001921:Brain / Q000503:physiopathology; D001927:Brain Diseases / Q000175:diagnosis / Q000503:physiopathology*; D001932:Brain Neoplasms / Q000175:diagnosis / Q000503:physiopathology*; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008959:Models, Neurological; D008962:Models, Theoretical; D009474:Neurons / Q000502:physiology; D012107:Research Design; D009435:Synaptic Transmission / Q000502:physiology*; D014057:Tomography, X-Ray Computed