Functional connectivity analysis in EEG source space: The choice of method

Abstract

Functional connectivity (FC) is among the most informative features derived from EEG. However, the most straightforward sensor-space analysis of FC is unreliable owing to volume conductance effects. An alternative—source-space analysis of FC—is optimal for high- and mid-density EEG (hdEEG, mdEEG); however, it is questionable for widely used low-density EEG (ldEEG) because of inadequate surface sampling. Here, using simulations, we investigate the performance of the two source FC methods, the inverse-based source FC (ISFC) and the cortical partial coherence (CPC). To examine the effects of localization errors of the inverse method on the FC estimation, we simulated an oscillatory source with varying locations and SNRs. To compare the FC estimations by the two methods, we simulated two synchronized sources with varying between-source distance and SNR. The simulations were implemented for hdEEG, mdEEG, and ldEEG. We showed that the performance of both methods deteriorates for deep sources owing to their inaccurate localization and smoothing. The accuracy of both methods improves with the increasing between-source distance. The best ISFC performance was achieved using hd/mdEEG, while the best CPC performance was observed with ldEEG. In conclusion, with hdEEG, ISFC outperforms CPC and therefore should be the preferred method. In the studies based on ldEEG, the CPC is a method of choice.

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

“…These include algorithms which directly estimate resting-state source space functional networks from the sensor space data without first inverting the data, e.g. partial canonical coherence (Schoffelen and Gross, 2009;Popov et al, 2018), cortical partial coherence (Barzegaran and Knyazeva, 2017), and estimation of MVAR coefficients (Gómez-Herrero et al, 2008), as well as linear algorithms estimated in the frequency domain, e.g. dynamic imaging of coherent sources (Gross et al, 2001) and frequency domain minimum norm estimates (Yuan et al, 2008).…”

“…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.…”

“…Care must be taken when interpreting the LE in the resting-state paradigm. We are not attempting to quantify the difference in peak activation of the estimate given a single 'true' active dipole as in simulation or theoretical studies (Pascual-Marqui, 2007;Barzegaran and Knyazeva, 2017;Pascual-Marqui et al, 2018;Halder et al, 2019), because of the expected cross talk from distributed sources in resting-state data. As a result, 'true' activity at a certain location does not necessarily imply a peak of estimated activity at that location -for example, localization statistics based on peak activation for sLORETA are accurate for single dipole localization, but are imperfect for localizing multiple dipoles (Bradley et al, 2016).…”

“…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).…”

Functional Brain Connectivity of Language Functions in Children Revealed by EEG and MEG: A Systematic Review

“…Source localization of cerebral activity, captured on the surface of the scalp, represents a particular challenge for sensor-space analysis. This is known as the inverse problem, which may lead to inaccurate identification of cerebral networks (e.g., Nunez et al, 1997;Sakkalis, 2011;Barzegaran and Knyazeva, 2017;Abreu et al, 2018Abreu et al, , 2019. Also, the effect of volume conduction, which is a mix of several signals within one sensor, and which originate from identical cerebral regions, makes critical a direct derivative from sensors to cerebral representation.…”