Vallarino, E. | Search bioRxiv

bioRxiv2023

Tuning Minimum-Norm regularization parameters for optimal MEG connectivity estimation

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

neuroscience↗

bioRxiv2021

Transfreq: a Python package for computing the theta-to-alpha transition frequency from resting state EEG data

A classic approach to estimate the individual theta-to-alpha transition frequency requires two electroencephalographic (EEG) recordings, one acquired in restingstate condition and one showing an alpha de-synchronisation due e.g. to task execution. This translates into longer recording sessions that my be cumbersome in studies involving patients. Moreover, incomplete de-synchronisation of the alpha rhythm may compromise the final estimation of the transition frequency. Here we present transfreq, a Python library that allows the computation of the transition frequency from resting-state data by clustering the spectral profiles at different EEG channels based on their content in the alpha and theta bands. We first provide an overview of the transfreq core algorithm and of the software architecture. Then we demonstrate its feasibility and robustness across different experimental setups on a publicly available EEG data set and on in-house recordings. A detailed documentation of transfreq and the codes for reproducing the analysis of the paper with the open-source data set are available online at https://elisabettavallarino.github.io/transfreq/

neuroscience↗