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.

EEG in extreme conditions: An advanced analysis pipeline for the human electroencephalographic signals recorded in space during the ALTEA experiment.

The Anomalous Long Term Effects in Astronauts (ALTEA) project originally aimed at disentangling the mechanisms behind astronauts' perception of light flashes. To this end, an experimental apparatus was set up in order to concurrently measure the tracks of cosmic radiation particles in the astronauts' head and the electroencephalographic (EEG) signals generated by their brain. So far, the ALTEA data set has never been analyzed with the broader intent to study possible interference between cosmic radiation and the brain, regardless of light flashes. The aim of this work is to define a pipeline to systematically pre-process the ALTEA EEG data. Compared to the analysis of standard EEG recording, this task is made more difficult by the presence of unconventional artifacts due to the extreme recording conditions that, in particular, require the EEG cap to be positioned next to another noisy electronic device, namely the particle detectors. Here we show how standard tools for the analysis of EEG data can be tuned to deal with these unconventional artifacts. After pre-processing the available data we were able to elucidate a shift of the center frequency of the α rhythm induced by visual stimulation, thus proving the effectiveness of the implemented pipeline. This work represents the first study presenting results of signal processing of ALTEA EEG time series. Further, it is the starting point of a future work aimed at analyzing the interaction between EEG and cosmic radiation.

Transfreq: A Python package for computing the theta-to-alpha transition frequency from resting state electroencephalographic data.

A classic approach to estimate individual theta-to-alpha transition frequency (TF) requires two electroencephalographic (EEG) recordings, one acquired in a resting state condition and one showing alpha desynchronisation due, for example, to task execution. This translates into long recording sessions that may be cumbersome in studies involving patients. Moreover, an incomplete desynchronisation of the alpha rhythm may compromise TF estimates. Here we present transfreq, a publicly available Python library that allows TF computation from resting state data by clustering the spectral profiles associated to the EEG channels based on their content in alpha and theta bands. A detailed overview of transfreq core algorithm and software architecture is provided. Its effectiveness and robustness across different experimental setups are demonstrated on a publicly available EEG data set and on in-house recordings, including scenarios where the classic approach fails to estimate TF. We conclude with a proof of concept of the predictive power of transfreq TF as a clinical marker. Specifically, we present a scenario where transfreq TF shows a stronger correlation with the mini mental state examination score than other widely used EEG features, including individual alpha peak and median/mean frequency. The documentation of transfreq and the codes for reproducing the analysis of the article with the open-source data set are available online at https://elisabettavallarino.github.io/transfreq/. Motivated by the results showed in this article, we believe our method will provide a robust tool for discovering markers of neurodegenerative diseases.