The number of studies examining functional connectivity of the human brain is increasing rapidly. In this magnetoencephalography (MEG) study, we examined the reliability of connectivity related to language production in a picture naming test-retest paradigm, using data collected from the same participants on 2 separate days. We determined the connections that were reliable (Intraclass Correlation Coefficient, ICC) across both days and also examined the behavioral, functional, and structural properties underlying this reliability. A particularly salient finding among a rich set of results was a reliable pattern of beta connectivity increase in the left motor and frontal regions (0–400 ms and 400–800 ms after picture onset) and gamma connectivity decrease in the bilateral motor regions (800–1200 ms) which we suggest to represent the motor preparation of speech production. Furthermore, the reliable connections tended to be more frequently associated with language performance than the non-reliable ones. Finally, the reliable connections were also linked to stronger functional connectivity, as well as to stronger structural connectivity and shorter structural path length, as determined through diffusion MRI (magnetic resonance imaging). Overall, this study defines reliable language production-related functional connectivity and introduces practices that may increase reliability.

The ability to speak is one of the most crucial human skills, motivating neuroscientific studies of speech production and speech-related neural dynamics. Increased knowledge in this area allows, for example, for development of rehabilitation protocols for language-related disorders. While our understanding of speech-related neural processes has been greatly enhanced owing to non-invasive neuroimaging techniques, the interpretations have been limited by speech artefacts caused by the activation of facial muscles that mask important language-related information. Despite earlier approaches applying independent component analysis (ICA), the artefact removal process continues to be time consuming, poorly replicable, and affected by inconsistencies between different observers, typically requiring manual selection of artefactual components. The artefact component selection criteria have been variable, leading to non-standardized speech artefact removal processes. To address these issues, we propose here a pipeline for automated speech artefact removal from magnetoencephalography (MEG) data. We developed an ICA-based speech artefact removal routine by utilizing electromyography (EMG) data measured from facial muscles during a facial gesture task for isolating the speech-induced artefacts. Additionally, we used mutual information (MI) as a similarity measure between the EMG signals and the ICA-decomposed MEG to provide a feasible way to identify the artefactual components. Our approach efficiently and in an automated manner removed speech artefacts from MEG data. The method can be feasibly applied to improve the understanding of speech-related cortical dynamics, while transparently evaluating the removed and preserved MEG activation.

Reliable individual-level measures of neural activity are essential for capturing interindividual variability in brain activity recorded by magnetoencephalography (MEG). While conventional group-level analyses highlight shared features in the data, individual-level specificity is often lost. Current methods for assessing reproducibility of brain responses focus on group-level statistics and neglect subject-specific temporal and spatial characteristics. This study proposes a combined ICA algorithm (comICA), aimed at extracting within-individual consistent MEG evoked responses. The proposed hypotheses behind comICA are based on the temporal profiles of the evoked responses, the corresponding spatial information, as well as independence and linearity. ComICA is presented and tested against simulated data and test–retest recordings of a high-level cognitive task (picture naming). The results show high reliability in extracting the shared activations in the simulations (success rate >93%) and the ability to successfully reproduce group-level results on reproducibility for the test–retest MEG recordings. Our model offers means for noise reduction, targeted extraction of specific activation components in experimental designs, and potential integration across different recordings.