Functional Imaging based on swLORETA and phase synchronization

In order to overcome some of the limitations of the distributed inverse solution algorithms, a new algorithm named Standardized Weighted Low Resolution Tomography (swLORETA) was developed. The swLORETA algorithm incorporates a singular value decomposition (SVD) based lead field weighting to compensate the tendency of the linear inverse procedures in general, and sLORETA in particular, to reconstruct the sources close to the location of the sensors. It also contributes to decrease the sensitivity of the solution to the presence of noise. An extension of the swLORETA to the time-frequency domain was also developed by applying the Hilbert transform to the time series obtained with the swLORETA. Finally, the coherence and phase synchronization imaging methods were introduced to assess functional connectivity within the brain. The tomographic properties of swLORETA and sLORETA were compared using both simulated and real data. In the simulation studies, the reconstruction of single and multiple current dipoles was simulated varying their position and orientation across the source space and taking into account the presence of noise. The real data was obtained from healthy subjects who performed a classical spatial attention experiment. The tests performed demonstrated that the resulting algorithm is not only efficient but also accurate as demonstrated by the analysis of a spatial attention experiment.