Time-domain Analysis of Neural Tracking of Hierarchical Linguistic Structures

When listening to continuous speech, cortical activity measured by MEG concurrently follows the rhythms of multiple linguistic structures, e.g., syllables, phrases, and sentences. This phenomenon was previously characterized in the frequency domain. Here, we investigate the waveform of neural activity tracking linguistic structures in the time domain and quantify the coherence of neural response phases over subjects listening to the same stimulus. These analyses are achieved by decomposing the multi-channel MEG recordings into components that maximize the correlation between neural response waveforms across listeners. Each MEG component can be viewed as the recording from a virtual sensor that is spatially tuned to a cortical network showing coherent neural activity over subjects. This analysis reveals information not available from previous frequency-domain analysis of MEG global field power: First, concurrent neural tracking of hierarchical linguistic structures emerges at the beginning of the stimulus, rather than slowly building up after repetitions of the same sentential structure. Second, neural tracking of the sentential structure is reflected by slow neural fluctuations, rather than, e.g., a series of short-lasting transient responses at sentential boundaries. Lastly and most importantly, it shows that the MEG responses tracking the syllabic rhythm are spatially separable from the MEG responses tracking the sentential and phrasal rhythms.