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bioRxiv · 10.1101/2023.04.15.537017

Tuning Minimum-Norm regularization parameters for optimal MEG connectivity estimation

Vallarino, E.·Hincapie, A. S.·Jerbi, K.·Leahyf, R.·Pascarella, A.·Sorrentino, A.·Sommariva, S.
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Abstract

The accurate characterization of cortical functional connectivity from Magnetoencephalography (MEG) data remains a challenging problem due to the subjective nature of the analysis, which requires several decisions at each step of the analysis pipeline, such as the choice of a source estimation algorithm, a connectivity metric and a cortical parcellation, to name but a few. Recent studies have emphasized the importance of selecting the regularization parameter in minimum norm estimates with caution, as variations in its value can result in significant differences in connectivity estimates. In particular, the amount of regularization that is optimal for MEG source estimation can actually be suboptimal for coherence-based MEG connectivity analysis. In this study, we expand upon previous work by examining a broader range of commonly used connectivity metrics, including the imaginary part of coherence, corrected imaginary part of Phase Locking Value, and weighted Phase Lag Index, within a larger and more realistic simulation scenario. Our results show that the best estimate of connectivity is achieved using a regularization parameter that is 1 or 2 orders of magnitude smaller than the one that yields the best source estimation. This remarkable difference may imply that previous work assessing source-space connectivity using minimum-norm may have benefited from using less regularization, as this may have helped reduce false positives. Importantly, we provide the code for MEG data simulation and analysis, offering the research community a valuable open source tool for informed selections of the regularization parameter when using minimum-norm for source space connectivity analyses. HighlightsO_LIThe regularization parameter of the Minimum Norm Estimate of neural activity impacts connectivity estimation C_LIO_LIWe study empirically the optimal parameter for connectivity estimation using realistic synthetic datasets C_LIO_LIWe find the optimal parameter for connectivity estimation is systematically smaller than the optimal parameter for source imaging; different connectivity metrics yield the same result C_LIO_LICode and data are available open source. C_LI

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Vallarino, E., Hincapie, A. S., Jerbi, K., Leahyf, R., Pascarella, A., Sorrentino, A., Sommariva, S.. 2023-04-16. Tuning Minimum-Norm regularization parameters for optimal MEG connectivity estimation. https://doi.org/10.1101/2023.04.15.537017

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