Detecting Functional Connectivity During Audiovisual Integration with MEG: A Comparison of Connectivity Metrics

Abstract

In typical magnetoencephalography and/or electroencephalography functional connectivity analysis, researchers select one of several methods that measure a relationship between regions to determine connectivity, such as coherence, power correlations, and others. However, it is largely unknown if some are more suited than others for various types of investigations. In this study, the authors investigate seven connectivity metrics to evaluate which, if any, are sensitive to audiovisual integration by contrasting connectivity when tracking an audiovisual object versus connectivity when tracking a visual object uncorrelated with the auditory stimulus. The authors are able to assess the metrics' performances at detecting audiovisual integration by investigating connectivity between auditory and visual areas. Critically, the authors perform their investigation on a whole-cortex all-to-all mapping, avoiding confounds introduced in seed selection. The authors find that amplitude-based connectivity measures in the beta band detect strong connections between visual and auditory areas during audiovisual integration, specifically between V4/V5 and auditory cortices in the right hemisphere. Conversely, phase-based connectivity measures in the beta band as well as phase and power measures in alpha, gamma, and theta do not show connectivity between audiovisual areas. The authors postulate that while beta power correlations detect audiovisual integration in the current experimental context, it may not always be the best measure to detect connectivity. Instead, it is likely that the brain utilizes a variety of mechanisms in neuronal communication that may produce differential types of temporal relationships.

Comparison of Phase Synchronization Measures for Identifying Stimulus-Induced Functional Connectivity in Human Magnetoencephalographic and Simulated Data

“…Other studies compared FC measures from the viewpoint of reproducibility (Colclough et al, 2016;Garcés et al, 2016), but more reproducible FC measures do not necessarily exhibit better performance for detecting FCs. In addition, in many of these studies (Dauwels et al, 2010;Mezeiová and Paluš, 2012;Ard et al, 2015;Colclough et al, 2016;Garcés et al, 2016;Lowet et al, 2016;Bakhshayesh et al, 2019), comparisons were made between different types of FC measures (e.g., amplitude envelope correlation and phase synchronization measures) or between FC measures and effective connectivity measures. However, because different types of FC measures have different functional roles (Mehrkanoon et al, 2014;Guggisberg et al, 2015), they should be regarded as complementary approaches.…”