Quantifying the Effect of Demixing Approaches on Directed Connectivity Estimated Between Reconstructed EEG Sources

Abstract

Electrical activity recorded on the scalp using electroencephalography (EEG) results from the mixing of signals originating from different regions of the brain as well as from artifactual sources. In order to investigate the role of distinct brain areas in a given experiment, the signal recorded on the sensors is typically projected back into the brain (source reconstruction) using algorithms that address the socalled EEG "inverse problem". Once the activity of sources located inside of the brain has been reconstructed, it is often desirable to study the statistical dependencies among them, in particular to quantify directional dynamical interactions between brain areas. Unfortunately, even when performing source reconstruction, the superposition of signals that is due to the propagation of activity from sources to sensors cannot be completely undone, resulting in potentially biased estimates of directional functional connectivity. Here we perform a set of simulations involving interacting sources to quantify source connectivity estimation performance as a function of the location of the sources, their distance to each other, the noise level, the source reconstruction algorithm, and the connectivity estimator. The generated source activity was projected onto the scalp and projected back to the cortical level using two source reconstruction algorithms, Linearly Constrained Minimum Variance (LCMV) beamforming and 'Exact' Low-resolution Tomography (eLORETA). In source space, directed connectivity was estimated using Multi-Variate Granger Causality (MVGC) and Time-Reversed Granger Causality (TRGC), and compared with the imposed ground truth. Our results demonstrate that all considered factors significantly affect the connectivity estimation performance.

Cortical source imaging of resting-state MEG with a high resolution atlas: An evaluation of methods

“…Another crucial methodological decision was choice of methods used to compare different algorithms. Previous studies have compared algorithms for source localization -identifying the origin of a small number of sources (Bai et al, 2007;Hassan et al, 2014;Bradley et al, 2016;Finger et al, 2016;Barzegaran and Knyazeva, 2017;Hassan et al, 2017;Hincapié et al, 2017;Bonaiuto et al, 2018;Pascual-Marqui et al, 2018;Seeland et al, 2018;Anzolin et al, 2019;Halder et al, 2019), such as known networks during task or simulated dipoles. These methods are not directly generalizable to resting-state data, where activity is not a point source but is distributed widely across the cortex.…”

“…Much effort has been made to assess the quality of source reconstruction algorithms in the literature, which mainly focuses on source localization, i.e. identifying the origin of a small number of sources, for example evoked potentials with ground truth based on known task-relevant activity (Bai et al, 2007;Hassan et al, 2014;Seeland et al, 2018;Halder et al, 2019), or simulated activity at a small number of dipoles (Bradley et al, 2016;Finger et al, 2016;Barzegaran and Knyazeva, 2017;Hassan et al, 2017;Hincapié et al, 2017;Bonaiuto et al, 2018;Pascual-Marqui et al, 2018;Anzolin et al, 2019;Halder et al, 2019).…”

Mean-field modeling of brain-scale dynamics for the evaluation of EEG source-space networks

“…To tackle those challenges, several comparative studies have been conducted with the aim of evaluating the performance of the adopted techniques and the influence of different parameters affecting the network estimation procedure (Anzolin et al, 2019;Colclough et al, 2016;Fornito et al, 2010;Halder et al, 2019;Lantz et al, 2003;Sohrabpour et al, 2015;Song et al, 2015;Wang et al, 2009;Zalesky et al, 2010). In the context of EEG, several studies investigated the effect of different electrode montages on the estimation of functional connectivity.…”

Controversies in EEG Source Imaging and Connectivity: Modeling, Validation, Benchmarking

“…The second reason was explicitly quantified in another study (Anzolin et al 2019), in which a set of simulations were performed involving interacting sources to quantify source connectivity estimation performance as a function of the location of the sources, their distance to each other, the noise level, the source reconstruction algorithm, and the connectivity estimator.…”