Abstract
Volume conduction (VC) and magnetic field spread (MFS) induce spurious correlations between EEG/MEG sensors, such that the estimation of functional networks from scalp recordings is inaccurate. Imaginary coherency [1] reduces VC/MFS artefacts between sensors by assuming that instantaneous interactions are caused predominantly by VC/MFS and do not contribute to the imaginary part of the cross-spectral densities (CSDs). We propose an adaptation of the dynamic imaging of coherent sources (DICS) [2] - a method for reconstructing the CSDs between sources, and subsequently inferring functional connectivity based on coherences between those sources. Firstly, we reformulate the principle of imaginary coherency by performing an eigenvector decomposition of the imaginary part of the CSD to estimate the power that only contributes to the non-zero phase-lagged (NZPL) interactions. Secondly, we construct an NZPL-optimised spatial filter with two a priori assumptions: (1) that only NZPL interactions exist at the source level and (2) the NZPL CSD at the sensor level is a good approximation of the projected source NZPL CSDs. We compare the performance of the NZPL method to the standard method by reconstructing a coherent network from simulated EEG/MEG recordings. We demonstrate that, as long as there are phase differences between the sources, the NZPL method reliably detects the underlying networks from EEG and MEG. We show that the method is also robust to very small phase lags, noise from phase jitter, and is less sensitive to regularisation parameters. The method is applied to a human dataset to infer parts of a coherent network underpinning face recognition.
Highlights
As cognitive function arises from the dynamic interaction between brain regions, there is an increasing interest in moving cognitive brain imaging beyond the identification of anatomical loci of functional processes, to the detection of the underlying functionally connected networks
Paired sample t-tests were performed to test for significant increases in area under the log ROC curve (AUC) for the reconstructions with the non-zero phase-lagged (NZPL) sensor level CSD (sCSD) compared to the reconstructions for the full sCSD
This study aimed to optimise the dynamic imaging of coherent sources [2] method to reconstruct only non-zero phase-lagged (NZPL) interactions using a variation of imaginary coherency [1]
Summary
As cognitive function arises from the dynamic interaction between brain regions, there is an increasing interest in moving cognitive brain imaging beyond the identification of anatomical loci of functional processes, to the detection of the underlying functionally connected networks. This results in a more sensitive measure of non-instantaneous interactions and is less susceptible to bias from phase lag This approach as the same advantage as the PLI method [46], by manipulating the CSD directly, the NZPL approach can be extended to source localisation directly, without the need to reconstruct virtual electrodes [47] (see below). Our assumption is that there is sufficient information about this crosstalk in the higher-order artefacts (see above) that remain in the imaginary part of the sCSD This estimate of the projected source NZPL CSD is used to compute a spatial filter optimised to recover NZPL interactions, using the same covariance minimisation constraint as per the existing DICS method. This approach significantly reduces the projection of artefactual VC/MFS interactions into the rCSD, improving the accuracy of the inferred connectivity
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