Influence of the head model on EEG and MEG source connectivity analyses

Abstract

The results of brain connectivity analysis using reconstructed source time courses derived from EEG and MEG data depend on a number of algorithmic choices. While previous studies have investigated the influence of the choice of source estimation method or connectivity measure, the effects of the head modeling errors or simplifications have not been studied sufficiently. In the present simulation study, we investigated the influence of particular properties of the head model on the reconstructed source time courses as well as on source connectivity analysis in EEG and MEG. Therefore, we constructed a realistic head model and applied the finite element method to solve the EEG and MEG forward problems. We considered the distinction between white and gray matter, the distinction between compact and spongy bone, the inclusion of a cerebrospinal fluid (CSF) compartment, and the reduction to a simple 3-layer model comprising only the skin, skull, and brain. Source time courses were reconstructed using a beamforming approach and the source connectivity was estimated by the imaginary coherence (ICoh) and the generalized partial directed coherence (GPDC). Our results show that in both EEG and MEG, neglecting the white and gray matter distinction or the CSF causes considerable errors in reconstructed source time courses and connectivity analysis, while the distinction between spongy and compact bone is just of minor relevance, provided that an adequate skull conductivity value is used. Large inverse and connectivity errors are found in the same regions that show large topography errors in the forward solution. Moreover, we demonstrate that the very conservative ICoh is relatively safe from the crosstalk effects caused by imperfect head models, as opposed to the GPDC.

Musical expertise is related to altered functional connectivity during audiovisual integration

“…45. The use of realistic FEMs generated on the basis of the individual anatomy of each subject improves the source localization reliability of the MEG signals, especially when the compartment of CSF is included in the model (46). Current density reconstructions (CDRs) were calculated on the neural responses of each subject separately for each run's congruent and incongruent averages using the sLORETA method (12) as provided by BESA.…”

Combined EMD-sLORETA Analysis of EEG Data Collected during a Contour Integration Task

“…Unique solutions can, however, be achieved by imposing additional constraints to the resulting optimization problem. Such constraints are often of a purely mathematical nature, but biophysically realistic constraints have been formulated as well (see for example LAURA [19]), [20, 21]. Source localization methods use measured scalp potentials in the microvolt range, and apply signal processing techniques to estimate current sources inside the brain which best explain the observations.…”

Development of the error-monitoring system from ages 9–35: Unique insight provided by MRI-constrained source localization of EEG

“…It is possible that previous investigations missed such effects, given the use of a “dipole fitting” procedure, in which the number and possible location of sources are strongly influenced by the decisions of the experimenter (for details, see Michel et al, 2004). Additionally, none of the previous developmental investigations employed realistic modeling of the brain and skull, a factor that can substantially influence source localization accuracy (Vorwerk et al, 2014; Cho et al, 2015), especially within a developmental context (Reynolds and Richards, 2009; Richards and Xie, 2015). …”