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Journal of Integrative Neuroscience  2018, Vol. 17 Issue (4): 393-396    DOI: 10.31083/j.jin.2018.04.0415
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Comparison of functional connectivity metrics using an unsupervised approach: a source resting-state EEG study
Matteo Fraschini1, *(), Margherita Lai1, Luca Didaci1
1 Department of Electrical and Electronic Engineering, University of Cagliari, via Marengo 2, 09123 Cagliari, Italy
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Abstract  

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.

Key words:  EEG      connectivity      resting-state      clustering      source      classification     
Submitted:  03 November 2017      Accepted:  03 November 2017      Published:  15 November 2018     
*Corresponding Author(s):  Matteo Fraschini     E-mail:  fraschin@unica.it

Cite this article: 

Matteo Fraschini, Margherita Lai, Luca Didaci. Comparison of functional connectivity metrics using an unsupervised approach: a source resting-state EEG study. Journal of Integrative Neuroscience, 2018, 17(4): 393-396.

URL: 

https://jin.imrpress.com/EN/10.31083/j.jin.2018.04.0415     OR     https://jin.imrpress.com/EN/Y2018/V17/I4/393

Table 1  Properties of FC metrics
FC metric Influenced by Signal leakage correction
AEC Amplitude and phase YES
PLI phase YES
iCOH amplitude and phase YES
PLV phase NO
COH amplitude and phase NO
Fig. 1.  The mean global patterns of connectivity for each of the five FC metrics for source-reconstructed analysis. Brain regions are organized as front (left) to back (right) for the left and the right hemisphere, respectively. Connectivity values are intentionally not given at the same scale.

Figure.2.  Mean silhouette values for K varying from 2 to 6 for the source-reconstructed analysis (left panel) and scalp analysis (right panel).

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