Phase shift invariant imaging of coherent sources (PSIICOS) from MEG data

On the two-step estimation of the cross-power spectrum for dynamical linear inverse problems

“…Finally, as we point out in the last section, our results suggest that the two-step approach to estimation of the cross-power spectrum, and more in general of brain functional connectivity, might be sub-optimal. This idea is in line with recent literature on the topic [24,8,14,32,39,40]. Indeed, by looking at the filter factors obtained by the two-step approach, and comparing them to the filter factors one would get with a one-step approach to estimation of the power spectrum, we expect a better behaviour for this second option.…”

Modified covariance beamformer for solving MEG inverse problem in the environment with correlated sources

“…As our simulations show, our beamformer is more resistant to forward modelling errors than the classical LCMV beamformer. The key steps of the proposed algorithm are the PSIICOS projection procedure (Ossadtchi et al (2018)) and its variations.…”

“…Consequently, the projection operation will inevitably suppress power in both subspaces. However, as it has been demonstrated earlier in Ossadtchi et al (2018), the use of spectral value decomposition procedure (SVD) allows identifying a low dimensional subspace of S pwr capturing most of the power of the auto-product terms vec g i g T i . Based on this estimate of the principal power subspace, we build a projector that we then apply to the vectorized form of the observed data covariance matrix.…”

“…This projection does not remove the activity of the correlated sources from the sensor-space covariance matrix but rather selectively handles their contributions to the covariance matrix and creates a sufficiently accurate approximation of an ideal data covariance that could hypothetically be observed should these sources be uncorrelated. Since the projection operation is reciprocal to the PSIICOS method developed by us earlier (Ossadtchi et al (2018)) we refer to the family of algorithms presented here as ReciPSIICOS.We asses the performance of the novel approach using realistically simulated MEG data and show its superior performance in comparison to the well established MNE and classical BF approaches. We have also applied our approach to the MEG datasets from the two experiments involving two different auditory tasks.The analysis of experimental MEG datasets showed that beamformers from ReciPSIICOS family, but not MNE and the classical BF, discovered the expected bilateral focal sources in the primary auditory cortex and detected motor cortex activity associated with the audio-motor task.…”

“…This projection does not remove the activity of the correlated sources from the sensor-space covariance matrix but rather selectively handles their contributions to the covariance matrix and creates a sufficiently accurate approximation of an ideal data covariance that could hypothetically be observed should these sources be uncorrelated. Since the projection operation is reciprocal to the PSIICOS method developed by us earlier (Ossadtchi et al (2018)) we refer to the family of algorithms presented here as ReciPSIICOS.…”