MVPA analysis of intertrial phase coherence of neuromagnetic responses to words reliably classifies multiple levels of language processing in the brain
Neural processing of language is still among the most poorly understood functions of the human brain, whereas a need to objectively assess the neurocongitve status of the language function in a participant-friendly and noninvasive fashion arises in various situations. Here, we propose a solution for this based on a short task-free recording of MEG responses to a set of spoken linguistic contrasts. We used spoken stimuli that diverged lexically (words/pseudowords), semantically (action-related/abstract) or morphosyntactically (grammatically correct/ungrammatical). Based on beamformer source reconstruction we investigated inter-trial phase coherence (ITPC) in five canonical bands (alpha, beta, and low, medium and high gamma) using multivariate pattern analysis (MVPA). Using this approach, we could successfully classify brain responses to meaningful words from meaningless pseudowords, correct from incorrect syntax, as well as semantic differences. The best classification results indicated distributed patterns of activity dominated by core temporofrontal language circuits and complemented by other areas. They varied between the different neurolinguistic properties across frequency bands, with lexical processes classified predominantly by broad gamma, semantic distinctions – by alpha and beta, and syntax – by low gamma feature patterns. Crucially, all types of processing commenced in a near-parallel fashion from ∼100 ms after the auditory information allowed for disambiguating the spoken input. This shows that individual neurolinguistic properties take place simultaneously and involve overlapping yet distinct neuronal networks that operate at different frequency bands. This brings further hope that brain imaging can be used to assess neurolinguistic processes objectively and noninvasively in a range of populations.
Significance Statement In an MEG study that was optimally designed to test several language features in a non-attentive paradigm, we found that, by analysing cortical source-level inter-trial phase coherence (ITPC) in five canonical bands (alpha, beta, and low, medium and high gamma) with machine-learning classification tools (multivariate pattern analysis, MVPA), we could successfully classify meaningful words from meaningless pseudowords, correct from incorrect syntax, and semantic differences between words, based on passive brain responses recorded in a task-free fashion. The results show different time courses for the different processes that involve different frequency bands. It is to our knowledge the first study to simultaneously map and objectively classify multiple neurolinguistics processes in a comparable manner across language features and frequency bands.
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