IMR Press / JIN / Volume 17 / Issue 4 / DOI: 10.31083/j.jin.2018.04.0415
Open Access Brief Report
Comparison of functional connectivity metrics using an unsupervised approach: a source resting-state EEG study
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1 Department of Electrical and Electronic Engineering, University of Cagliari, via Marengo 2, 09123 Cagliari, Italy
*Correspondence: (Matteo Fraschini)
J. Integr. Neurosci. 2018, 17(4), 393–396;
Submitted: 3 November 2017 | Accepted: 3 November 2017 | Published: 15 November 2018
Copyright: © 2018 The authors. Published by IMR press.
This is an open access article under the CC BY-NC 4.0 license.

The study of inter-regional synchronization between brain regions represents an important challenge in neuroimaging. Electroencephalography, given the high temporal resolution, allows the investigation of brain activity, connectivity, and network organization in time and frequency domains. Here, some of the most common metrics used to estimate the strength of functional interaction between pairs of brain regions are compared using source reconstructed time-series from resting-state high-density electroencephalography. Results show that the investigated metrics, on the basis of their connectivity profiles, may be naturally grouped into two main clusters. In particular, this finding shows that metrics which tend to limit the effects of volume conduction/signal leakage, although based on different properties of the original signals, may be partitioned into a specific homogeneous cluster, whilst the metrics which do not correct for these effects form a separate cluster. Moreover, this effect is even clearer when the analysis is replicated at scalp level. In conclusion, although within each cluster different metrics may still capture specific connectivity profiles, this study provides evidence that the result of an arbitrary choice of metric that either does or does not correct for volume conduction and signal leakage is more relevant.

Fig. 1.
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