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Functional identification of language-responsive channels in individual participants in MEG investigations
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Poster B117 in Poster Session B, Tuesday, October 24, 3:30 - 5:15 pm CEST, Espace Vieux-Port
Rose Bruffaerts1,2, Alvince Pongos2,3, Cory Shain2, Benjamin Lipkin2, Matthew Siegelman2,4, Vincent Wens5, Martin Sjøgård5, Dimitrios Pantazis2, Idan Blank2,6, Serge Goldman5, Xavier De Tiège5, Evelina Fedorenko2; 1University of Antwerp, Belgium, 2Massachusetts Institute of Technology, USA, 3UC Berkeley-UCSF, USA, 4Columbia University, USA, 5Université libre de Bruxelles, Belgium, 6UCLA, USA
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 neuromaging approaches often align and average brains together in a common space. However, lateral frontal and temporal cortex, where the language system resides, is characterized by high structural and functional inter-individual variability. This variability reduces the sensitivity and functional resolution of group-averaging analyses. A solution inspired by other fields of cognitive neuroscience (e.g., vision) 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 (Fedorenko et al., 2010)., yielding a number of discoveries about the language system, and has been successfully extended to intracranial recording investigations (Fedorenko et al., 2016). Here, we apply this approach to MEG using a whole-head 306 channel (102 magnetometers, 204 planar gradiometers) Triux system (Elekta Neuromag). Across two experiments (one in Dutch speakers, n=19; one in English speakers, n=23), we examined neural responses while participants read sentences and performed a control condition (reading nonword sequences). For every word or nonword (MEG signal epoched between 0-350ms after (non)word presentation), we grouped each pair of planar gradiometers into a single effective gradiometer derived as their Euclidean norm (Chetail et al., 2018) and averaged the norm values across the epoch. First, we examined the Spearman correlation in the size of the sentence effect (percent signal change for the sentence condition relative to the baseline) across all channels within each participant across odd- and even-numbered trials, compared to the correlations between different participants (Wilcoxon rank sum test). This analysis demonstrated that the neural response to language was spatially consistent at the individual level in both the English and Dutch datasets (English: mean rho: 0.51 within participant, 0.14 between participants, P<0.001; Dutch: mean rho: 0.59 within participant, 0.20 between participants, P<0.001). Second, we used the data from the odd-numbered trials to define sensors of interest (SOIs) in each participant. SOIs were defined as the 10% sensors with the highest increase in percentage signal change in the sentence condition relative to the baseline. In the even-numbered trials, we then examined the effect size for the sentence condition in these SOIs relative to the effect size for the nonwords condition using a signed rank test. The language-responsive SOIs were, as expected, significantly less responsive to the nonwords condition (English: P = 0.017; Dutch: P < 0.001). Finally, when we defined the SOIs based on the group-level map for the odd-numbered trials, the effect size for the sentence condition in the even-numbered trials was significantly smaller compared to the analyses that take inter-individual differences into account (English & Dutch: P<0.001). Thus, as in fMRI, functional localization yields benefits in MEG and thus opens the door to probing fine-grained distinctions in space and time in future MEG investigations of language processing.
Topic Areas: Methods, Reading