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

Date Published: July 20, 2017

Publisher: Public Library of Science

Author(s): Elham Barzegaran, Maria G. Knyazeva, Lawrence M. Ward.


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.

Partial Text

Cognitive functions are implemented via coordinated activity of the neural modules distributed in the brain [1, 2]. The coordination of modular activity is analyzed within the framework of a concept of the functional connectivity (FC) [3–5]. Among various methods for measuring the FC, electroencephalography- (EEG-) based techniques are unique in that they provide tools to evaluate the FC dynamics on a millisecond time scale inherent in cognitive processes. Some of these techniques estimate synchronization of distributed EEG signals recorded from the head surface [6, 7].

We obtained similar results for the WMN, LORETA, and LAURA methods in one- and two-source simulations. Here we present the WMN results, whereas the comparative analysis of the effects of these methods on the FC estimation can be found in the S1 File.

The studied factors that affect the accuracy of source FC estimation by means of the CPC and ISFC methods can be summarized as follows. The performance of the methods depends on the number of EEG sensors, on the source depth and between-source distances, and on the SNR level. For both methods, the increase of the source depth deteriorates the accuracy of FC estimations owing to the decreased accuracy of source localization and size. The FC can be more precisely estimated between distant sources than between close ones, independent of the method used. For the ISFC method, specifically, the FC accuracy increases with increasing sensor density, but not with SNR. In contrast, the performance of the CPC method improves with increasing SNR, but mildly declines with increasing the number of sensors.

The methods for source FC studies should be carefully chosen with regard to the most important factors that affect the FC measurements and are comprehensively analyzed and discussed here. In general, the ISFC method compared to the CPC one is a more accurate technique that is relatively immune to noise, given the high number of sensors used. Yet, for conventional ldEEG, the CPC method is an optimal choice, provided appropriate precautions are taken to ensure high SNR. In addition, independent of the method, the FC findings should not be over-interpreted considering the limitations inherent for deep and /or close sources.




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