Autti, S. | Search bioRxiv

bioRxiv2026

MEG-informed navigated TMS for individualized speech cortical mapping

Speech cortical mapping by means of navigated repetitive transcranial magnetic stimulation (SCM nrTMS) provides neurosurgeons with noninvasive prior information about individuals cortical speech network. Individualized mapping is required, since the exact locations and activation patterns of speech production show high variability between individuals. We hypothesized that magnetoencephalography (MEG) data of an individuals speech production could guide the SCM TMS process temporally and spatially, leading to higher error rates at MEG-defined locations with TMS pulse timings coinciding with MEG activity. 13 healthy subjects participated in MEG and TMS measurements, where the timing of the TMS pulse (PTI; picture-to-TMS interval) was adjusted based on the individuals MEG activation in a picture naming task. At the group level, significant correlations were observed between the latency of the peak MEG activation and the PTI that produced the highest speech error rate. The MEG peak preceded the best PTI by 132 ms (R=0.713, p=0.006) across the entire stimulation area in the lateral left hemisphere, and by 103 ms (R=0.673, p=0.012) in the left frontal regions. We found 17 combinations of PTI and stimulation area in which the subjects speech error rate increased significantly compared to their average error rate. Our findings suggest that optimal PTIs are highly individual, and that individualizing the PTI according to MEG activation provides a straightforward method for accounting individual variability in speech function and may increase the sensitivity and utility of SCM TMS.

neuroscience↗

bioRxiv2024

Automated speech artefact removal from MEG data utilizing facial gestures and mutual information

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 e.g., for development of rehabilitation protocols for language-related disorders. While our understanding of speech-related neural processes has 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 languagerelated 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 MEG data. We developed an ICA-based speech artefact removal routine by utilizing 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.

neuroscience↗