Comparison of undirected frequency-domain connectivity measures for cerebro-peripheral analysis

Gross, J. , Kluger, D. S., Abbasi, O., Chalas, N., Steingräber, N., Daube, C. and Schoffelen, J.-M. (2021) Comparison of undirected frequency-domain connectivity measures for cerebro-peripheral analysis. NeuroImage, 245, 118660. (doi: 10.1016/j.neuroimage.2021.118660)

Abstract

Analyses of cerebro-peripheral connectivity aim to quantify ongoing coupling between brain activity (measured by MEG/EEG) and peripheral signals such as muscle activity, continuous speech, or physiological rhythms (such as pupil dilation or respiration). Due to the distinct rhythmicity of these signals, undirected connectivity is typically assessed in the frequency domain. This leaves the investigator with two critical choices, namely a) the appropriate measure for spectral estimation (i.e., the transformation into the frequency domain) and b) the actual connectivity measure. As there is no consensus regarding best practice, a wide variety of methods has been applied. Here we systematically compare combinations of six standard spectral estimation methods (comprising fast Fourier and continuous wavelet transformation, bandpass filtering, and short-time Fourier transformation) and six connectivity measures (phase-locking value, Gaussian-Copula mutual information, Rayleigh test, weighted pairwise phase consistency, magnitude squared coherence, and entropy). We provide performance measures of each combination for simulated data (with precise control over true connectivity), a single-subject set of real MEG data, and a full group analysis of real MEG data. Our results show that, overall, WPPC and GCMI tend to outperform other connectivity measures, while entropy was the only measure sensitive to bimodal deviations from a uniform phase distribution. For group analysis, choosing the appropriate spectral estimation method appears to be more critical than the connectivity measure. We discuss practical implications (sampling rate, SNR, computation time, and data length) and aim to provide recommendations tailored to particular research questions.

• Item Type: Articles
• Additional Information: This work was supported by the Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of Münster (Gro3/001/19) and the DFG (GR 2024/5-1). This work was further supported by The Netherlands Organisation for Scientific Research (NWO Vidi: 864.14.011) to JMS. The authors would like to thank Karin Wilken, Ute Trompeter, and Hildegard Deitermann for their invaluable assistance during data collection.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Gross, Professor Joachim
• Authors: Gross, J., Kluger, D. S., Abbasi, O., Chalas, N., Steingräber, N., Daube, C., and Schoffelen, J.-M.
• College/School: College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
• Journal Name: NeuroImage
• Publisher: Elsevier
• ISSN: 1053-8119
• ISSN (Online): 1095-9572
• Published Online: 29 October 2021
• Copyright Holders: Copyright © The Author(s) 2021
• First Published: First published in NeuroImage 245: 118660
• Publisher Policy: Reproduced under a Creative Commons licence