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Free keywords:
EEG; Connectivity metrics; Graph theory; Functional connectivity networks; Emotions
Abstract:
Brain functional connectivity has gained increasing interest over the last few years. The application of Graph Theory on functional connectivity networks (FCNs) has shed light into different topics related to physiology as well as pathology. To this end, different connectivity metrics may be used; however, some concerns are often raised related with inconsistency of results and their associated neurophysiological interpretations depending on the metric used. This paper examines how the use of different connectivity metrics affects the small-world-ness of the FCNs and eventually the neuroscientific evidences and their interpretation; to achieve this, electroencephalography (EEG) data recorded from healthy subjects during an emotional paradigm are utilized. Participants passively viewed emotional stimuli from the international affective picture system (IAPS), categorized in four groups ranging in pleasure (valence) and arousal. Four different pair-wise metrics were used to estimate the connectivity between each pair of EEG channels: the magnitude square coherence (MSC), cross-correlation (CCOR), normalized mutual information (NMI) and normalized joint entropy (NJE). The small-world-ness is found to be varying among the connectivity metrics, while it was also affected by the choice of the threshold level. The use of different connectivity metrics affected the significance of the neurophysiological results. However, the results from different metrics were to the same direction: pleasant images exhibited shorter characteristic path length than unpleasant ones, while high arousing images were related to lower local efficiency as compared to the low arousing ones. Our findings suggest that the choice of different metrics modulates the small-world-ness of the FCNs as well as the neurophysiological results and should be taken into account when studying brain functional connectivity using graph theory.
