Functional identification of language-responsive sensors in individual participants in MEG investigations.

Making meaningful inferences about the functional architecture of the language system requires the ability to refer to the same neural units across individuals and studies. Traditional brain imaging approaches align and average brains together in a common space. However, lateral frontal and temporal cortices, where the language system resides, are characterized by high structural and functional interindividual variability, which reduces the sensitivity and functional resolution of group-averaging analyses. This issue is compounded by the fact that language areas lie in close proximity to regions of other large-scale networks with different functional profiles. A solution inspired by vision neuroscience is to identify language areas functionally in each individual brain using a "localizer" task (e.g., a language comprehension task). This approach has proven productive in fMRI, yielding a number of robust and replicable findings about the language system. Here, we extend this approach to MEG. Across two experiments (one in Dutch speakers, n = 19; one in English speakers, n = 23), we examined neural responses to the processing of sentences and a control condition (nonword sequences). We demonstrate that the sensor and source topography of neural responses to language is spatially stable within individuals but varies across individuals. Consequently, analyses that take this interindividual variability into account are characterized by greater sensitivity, compared with the group-level analyses. In summary, similar to fMRI, functional identification within individuals yields benefits in MEG, thus opening the door to future investigations of language processing, including questions where whole brain coverage and temporal resolution are both critical.NEW & NOTEWORTHY Language areas vary across individuals, challenging traditional group-averaged brain-imaging approaches. Using an fMRI-based strategy, this study validates that functionally localizing language responses within individuals improves sensitivity in magnetoencephalography (MEG). Across Dutch and English speakers, language-related sensor and source topographies were stable within individuals but varied between individuals. Individual-level functional identification thus enhances MEG analyses, enabling precise investigations of language processing with both high temporal resolution and whole brain coverage.