Akihisa Kenmochi

Multiple Equivalent Current Dipole Source Localization of Visual Event-Related Potentials During Oddball Paradigm With Motor Response

Event-related potentials (ERPs) during a visual oddball paradigm with button-pressing responses were recorded in 12 right-handed subjects from 32 scalp electrodes. The single equivalent current dipole (ECD) of the target C1 (weak occipito-parietal negativity from 30-80ms) was consistently located at the primary visual cortex. From the 4-ECD localization of the target P1/N1 (temporally coincident frontal positivity and occipito-temporal negativity), it was suggested that this complex reflected activities from distributed sources along both dorsal occipito-parietal and ventral occipito-temporal areas. The stable multiple ECD solutions for the target P3b were chosen as those including the left primary motor and/or sensorimotor dipole and satisfying goodness-of-fit (GOF) of more than 98% and confidence limit (CL) of less than 1mm. The obtained frontal dipoles were discussed in terms of visual working memory and sustained attention in reference to the previous PET, fMRI and MEG studies. The distributed multiple ECDs may suggest that P3 should be interpreted as being the embodiment of the cortico-limbic-thalamic network which involves Halgren and Marinkovics emotional and behavioral model and Mesulams attentional circuit.

Integrated approach of an artificial neural network and numerical analysis to multiple equivalent current dipole source localization.

The authors have developed a PC-based multichannel electroencephalogram (EEG) measurement and analysis system. This system enables us (1) to simultaneously record a maximum of 64 channels of EEG data, (2) to measure three-dimensional positions of the recording electrodes, (3) to rapidly and precisely localize equivalent current dipoles (ECDs) responsible for the EEG data, and (4) to superimpose the localization results on magnetic resonance images. A new neural network and numerical analysis (NNN) approach to ECD localization is described which integrates a feedforward artificial neural network (ANN) and a numerical optimization (Powells hybrid) method. It was shown that the ANN method has the advantages of high-speed localization and noise robustness, because in this approach: (1) ECD parameters are immediately initialized from the recorded EEG data by the ANN and (2) ECD parameters are accurately refined by the hybrid method. Our multiple ECD localization method was applied to sensory evoked potentials and event-related potentials using the present system.